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Integrating intermediate wheatgrass (IWG; Thinopyrum intermedium ), a perennial grain and forage crop, into agroecosystems may help achieve these goals. Additionally, diversification of IWG plantings by intercropping with a perennial legume could further increase other ecosystem services along with increases in nutrient availability. In this large-scale field experiment, IWG performance was assessed over two growing seasons in monoculture, monoculture with nitrogen fertilizer, and in an intercrop with alfalfa ( Medicago sativa ) at seven sites in four states that vary in environmental conditions. At the field scale, IWG grain production was similar in the intercrop and monoculture in both growing seasons at a majority of the sites. Intercropping did not reduce IWG biomass in the second growing season, but IWG biomass was reduced in the intercrop in the first growing season. Nitrogen fertilizer addition did not substantially alter IWG productivity at a majority of the sites. The results reinforce findings that intercropping with alfalfa has minimal impacts on IWG productivity after two growing seasons while providing potential for additional ecosystem services.","Frontiers in Sustainable Food Systems","2026-08-27T00:00:00Z","论文",false,74,{"impact":361,"substance":321,"depth":361,"authority":583,"freshness":584,"relevant":455,"comment":585},13,3,"多站点田间试验表明紫花苜蓿间作对中间偃麦草产量影响有限，为多年生谷物间作提供实证支持。",[587],{"name":577,"url":575},[589,590,591],"可持续农业","多年生谷物","间作",{"code":256,"slug":257,"name":258,"path":259,"parent_code":200},[],{"status":595,"locked":580,"version":4,"method":596,"evidence":597},"classified","rules",{"candidates":598,"agricultural_evidence":601},[599,607,612,617,621],{"code":256,"score":600,"supported":601,"title":602,"abstract":604,"tags":606},15,true,[603],"alfalfa",[605,603],"intercropping",[591],{"code":364,"score":584,"supported":601,"title":608,"abstract":609,"tags":611},[],[610],"forage",[],{"code":96,"score":584,"supported":601,"title":613,"abstract":614,"tags":616},[],[615],"fertilizer",[],{"code":144,"score":584,"supported":601,"title":618,"abstract":619,"tags":620},[],[145],[],{"code":192,"score":455,"supported":580,"title":622,"abstract":623,"tags":624},[],[],[589],[626,627],"可持续农业 多年生谷物 间作","可持续农业 多年生谷物","可持续农业多年生谷物间作-1235","10.3389\u002Ffsufs.2026.1816743",{"doi":629,"openalex_id":631,"authors":632,"venue":577,"cited_by_count":424,"oa_url":661,"card":662,"direction":666,"ingested_from":668},"W7204489875",[633,636,639,641,644,646,649,651,653,655,658],{"name":634,"orcid":635},"Thomas P. McKenna","https:\u002F\u002Forcid.org\u002F0000-0001-6531-3452",{"name":637,"orcid":638},"Laura K. van der Pol","https:\u002F\u002Forcid.org\u002F0000-0002-9430-5985",{"name":640,"orcid":11},"Madeline Dubois",{"name":642,"orcid":643},"Ranjith P. Udawatta","https:\u002F\u002Forcid.org\u002F0000-0002-2097-7922",{"name":645,"orcid":11},"Kenneth C. Okonkwo",{"name":647,"orcid":648},"Nyree J. C. Zerega","https:\u002F\u002Forcid.org\u002F0000-0003-1132-4943",{"name":650,"orcid":11},"Liz Vogel",{"name":652,"orcid":11},"Eric J. von Wettberg",{"name":654,"orcid":11},"Kevin Haggerty",{"name":656,"orcid":657},"Allison J. Miller","https:\u002F\u002Forcid.org\u002F0000-0002-2722-9361",{"name":659,"orcid":660},"Timothy E. Crews","https:\u002F\u002Forcid.org\u002F0000-0003-4764-341X","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1816743\u002Fpdf",{"tldr":663,"method":664,"finding":665,"direction":666,"opportunity":667},"研究苜蓿间作对中间偃麦草生产力的影响，发现间作很少限制其产量。","多站点田间试验，比较单作、施氮单作和苜蓿间作下中间偃麦草的生长表现。","间作在多数站点不降低谷物产量，第二季生物量不受影响，施氮效果不显著。","智慧农业 \u002F 农业物联网","可探索间作系统在不同气候土壤下的长期生态服务功能及优化管理策略。","openalex","2026-09-01T04:03:09.020063Z",{"id":671,"title":672,"url":673,"summary":674,"summary_zh":11,"content":675,"source_name":676,"source_url":11,"published_at":677,"category":678,"cover_url":11,"hotness":385,"is_selected":580,"score":679,"score_detail":680,"sources":682,"tags":684,"primary_topic":689,"secondary_topics":690,"classification":691,"search_phrases":708,"slug":711,"view_count":408,"doi":11,"paper":11,"created_at":712},1175,"湖南省农科院柏连阳院士调研江华鹅塘村江永七工岭村乡村振兴帮扶工作 鹅塘七工岭村集体经济增至27万元","https:\u002F\u002Fso.html5.qq.com\u002Fpage\u002Freal\u002Fsearch_news?docid=70000021_5216a904ea156852","湖南日报8月27日报道,25日至26日中国工程院院士、湖南省农科院党委书记柏连阳带队赴江华县鹅塘村、江永县七工岭村实地调研指导驻村帮扶工作。省农科院驻村帮扶工作队自2025年5月入村以来,两村村集体经济由2024年的5万余元增至2025年的27万余元。在鹅塘村,工作队引进市场主体发展订单农业,累计带动近3000人次就近务工。在江永七工岭村,工作队大力推进香芋种质改良、脱毒种苗繁育、水旱轮作示范,有效破解香芋品种退化、病害高发等痛点。在2025年度全省驻村帮扶考核中,鹅塘村、七工岭村两个帮扶队考核获评优秀,6名驻村帮扶队员个人考核全部获优秀等次。","发挥科研主力军优势 省农科院全力帮扶驻点村\n\n![Image 1: 湖南日报](https:\u002F\u002Fqb-profile.image.myqcloud.com\u002Favatar_d8f78cf42dcc6148be98118825b62d87.png)\n\n湖南日报\n\n4天前\n\n湖南日报8月27日讯（全媒体记者 胡盼盼 通讯员 陈俊宇 刘维帅）25日至26日，中国工程院院士、湖南省农科院党委书记柏连阳带队赴江华县鹅塘村、江永县七工岭村实地调研指导驻村帮扶工作。\n\n![Image 2](http:\u002F\u002Fqqpublic.qpic.cn\u002Fqq_public\u002F0\u002F28-2453784835-098AA502A4E38FF786511F1040FAB9A6\u002F0?fmt=jpg&size=110&h=358&w=640&ppv=1)\n\n调研期间，省农科院院士专家详细了解水稻、香芋等特色产业发展规模、品种改良、市场销路及品牌建设等情况，并走访慰问困难群众，详细了解政策落实成效，摸清群众急难愁盼问题。在鹅塘村、七工岭村，还举行了省农科院定点帮扶村儿童读物捐赠与“好种子”书屋揭牌仪式，并开展农科知识微课堂，旨在为帮扶村儿童播撒知识的“好种子”，以文化力量激发乡村振兴内生动力。\n\n![Image 3](http:\u002F\u002Fqqpublic.qpic.cn\u002Fqq_public\u002F0\u002F28-118380255-742E7CAEE48D037C6B3A68FE70688D8B\u002F0?fmt=jpg&size=100&h=330&w=640&ppv=1)\n\n据介绍，省农科院驻村帮扶工作队自2025年5月入村以来，聚焦产业发展、基础设施提质、基层治理、民生服务、人才培育等多点发力，两村村集体经济实现明显增长，由2024年的5万余元增至2025年的27万余元。\n\n在鹅塘村，工作队引进市场主体发展订单农业，累计带动近3000人次就近务工，带动脱贫户稳定就业增收。在江永七工岭村，工作队大力推进香芋种质改良、脱毒种苗繁育、水旱轮作示范，有效破解香芋品种退化、病害高发等痛点。在2025年度全省驻村帮扶考核中，鹅塘村、七工岭村两个帮扶队考核获评优秀，6名驻村帮扶队员个人考核全部获优秀等次。\n\n![Image 4](http:\u002F\u002Fqqpublic.qpic.cn\u002Fqq_public\u002F0\u002F28-2667786742-09C7BEC43EA4DFDC70C927D0AA75411D\u002F0?fmt=jpg&size=130&h=408&w=640&ppv=1)\n\n柏连阳对驻村工作队取得的扎实成效给予充分肯定。他表示，省农科院将持续发挥科技、品种、人才核心优势，扛牢定点帮扶政治责任，做强特色主导产业，用心用情解决群众急难愁盼问题，高质量完成本轮驻村帮扶既定任务，探索建立院地常态化长效合作机制，把试验示范、品种选育、技术服务等留在乡村。\n\n作者：胡盼盼\n\n责编：王亮\n\n一审：王亮\n\n二审：黄利飞\n\n三审：李伟锋","湖南日报 2026-08-27","2026-08-27T14:50:00Z","报道",60,{"impact":600,"substance":361,"depth":416,"authority":524,"freshness":400,"relevant":455,"comment":681},"省级科研机构帮扶成效报道，数据具体，但时效性一般。",[683],{"name":676,"url":673},[685,686,687,688],"乡村振兴","驻村帮扶","科技助农","集体经济",{"code":256,"slug":257,"name":258,"path":259,"parent_code":200},[],{"status":595,"locked":580,"version":4,"method":596,"evidence":692},{"candidates":693,"agricultural_evidence":601},[694,699,704],{"code":480,"score":695,"supported":601,"title":696,"abstract":697,"tags":698},9,[685],[],[685],{"code":256,"score":584,"supported":601,"title":700,"abstract":701,"tags":703},[],[702],"轮作",[],{"code":488,"score":455,"supported":580,"title":705,"abstract":706,"tags":707},[],[],[687],[709,710],"乡村振兴 科技助农 集体经济 驻村帮扶","乡村振兴 科技助农","乡村振兴科技助农集体经济驻村帮扶-1175","2026-09-01T00:03:38.261124Z",{"id":714,"title":715,"url":716,"summary":717,"summary_zh":11,"content":718,"source_name":719,"source_url":11,"published_at":720,"category":678,"cover_url":11,"hotness":385,"is_selected":580,"score":377,"score_detail":721,"sources":724,"tags":726,"primary_topic":734,"secondary_topics":735,"classification":737,"search_phrases":770,"slug":773,"view_count":400,"doi":11,"paper":11,"created_at":774},1018,"紫金举办蝉茶产业专项培训 广东省农科院茶叶所副研究员陈义勇博士分享'冬菌夏蝉'生态套种模式","http:\u002F\u002Fwww.heyuanxw.com\u002F2026\u002Fzjnews_0825\u002F86277.html","近日,一场聚焦紫金蝉茶产业核心竞争力的专项技术培训在紫金县紫城镇黄花庄园举行。培训由紫金县农业农村局指导,中国科学院华南植物园、河源市农业产业化龙头企业协会与广东省农业科学院河源分院联合主办,吸引全县50余名茶企技术骨干与制茶农户参与。本次培训重点推广'冬菌夏蝉:红松茸生态套种促进紫金蝉茶提质增效'项目,由84岁的紫金蝉茶资深种植者陈炯昌与中国科学院华南植物园研究员曾兰亭联合发起。该模式利用茶树行间空隙和冠层天然遮阴,冬闲期铺设稻壳、玉米芯等农业废弃物基质套种红松茸,每亩茶园可产红松茸2000至3000斤,45天出菇,采摘期持续至次年4月,恰好衔接春茶开采。改良后土壤为小绿叶蝉营造更适宜栖息环境,茶叶中游离氨基酸、可溶性糖等关键风味物质含量明显提升。","̬԰跽棬㶫ʡũҵѧԺҶооԱ²ʿϸ˹㶫ʡ̬԰չسɹص̬԰׶ʵ“һ˫”ģʽʵüӹɹ㶫Ʋ輼ʦҶ÷Բή͡Ĵѵ㣬ҶԭԳϵͳѹʱ䡢ͳ̶ȵĸйپ׼жϡԼ¶ȵķֶοƲԣչְʵٽѧũѧɿصļӹϵ\n\n˴ѵһ㣬صƹںԴеڶ촴ʹٻ㽱“IJִ̬ٽϽЧ”ĿĿ84Ͻֲ߳¾йѧԺֲ԰оԱͤϷϽ“й֮”Զ“”гÿ114²Ҷͣɣ԰˹ݳɱߡ“IJ”ģʽƽһģʽڲı԰ṹǰ£òм϶͹ڲȻ赾ǡоũҵֺף“һض”̬ϵÿĶ԰ɲ2000300045ժڳ4£ǡνӴ迪ɡ\n\nһģʽ“Ծ衢蹲”̬ջڲΪӪʪ󻷾׾˿ֽʱøأֱ֣ӻΪлʣлʺӲݡΪСҶӪ˵ϢȺȶΪ“”ṩȻϡƷϣҶ백ᡢǵȹؼζʺϣȷΪ׳ɱϣÿʡ˹Ͽ֧൱ɹۡʡЧӦӣһƬ԰ֳʵʵڵ档\n\nĿǰйѧԺֲ԰㶫ʡũԺҶоϵͳչлʷģʽСҶϢӰרоؼƻֲģԵʮƽ50ĶʽԽ׶Ρ“IJ”ģʽ½Ͻϳ򡢾ȲIJء","河源新闻网 2026-08-25","2026-08-25T00:00:00Z",{"impact":416,"substance":345,"depth":385,"authority":722,"freshness":722,"relevant":455,"comment":723},7,"地方农业技术培训报道，涉及生态套种模式，有一定信息量但影响范围有限。",[725],{"name":719,"url":716},[727,728,729,730,731,732,733],"广东省农科院","茶叶所","紫金蝉茶","冬菌夏蝉","红松茸","生态套种","陈义勇",{"code":294,"slug":295,"name":296,"path":297,"parent_code":271},[736],{"code":256,"slug":257,"name":258,"path":259,"parent_code":200},{"status":595,"locked":580,"version":4,"method":596,"evidence":738},{"candidates":739,"agricultural_evidence":601},[740,746,751,755,760,765],{"code":294,"score":345,"supported":601,"title":741,"abstract":743,"tags":745},[742],"茶叶",[742,744],"茶树",[],{"code":256,"score":524,"supported":601,"title":747,"abstract":749,"tags":750},[748],"套种",[748],[],{"code":224,"score":584,"supported":601,"title":752,"abstract":753,"tags":754},[],[226],[],{"code":488,"score":584,"supported":601,"title":756,"abstract":757,"tags":759},[],[758],"技术培训",[],{"code":144,"score":584,"supported":601,"title":761,"abstract":762,"tags":764},[],[763],"土壤",[],{"code":168,"score":584,"supported":601,"title":766,"abstract":767,"tags":769},[],[768],"农业废弃物",[],[771,772],"广东省农科院 冬菌夏蝉 生态套种 紫金蝉茶","广东省农科院 冬菌夏蝉","广东省农科院冬菌夏蝉生态套种紫金蝉茶-1018","2026-08-28T00:07:00.251296Z",{"id":776,"title":777,"url":778,"summary":779,"summary_zh":11,"content":780,"source_name":781,"source_url":11,"published_at":782,"category":579,"cover_url":11,"hotness":385,"is_selected":580,"score":783,"score_detail":784,"sources":786,"tags":788,"primary_topic":794,"secondary_topics":795,"classification":796,"search_phrases":807,"slug":810,"view_count":584,"doi":11,"paper":11,"created_at":811},843,"基于EfficientNet与FPN多尺度特征融合的田间害虫智能检测(原标题:Enhancing Pest Detection in Agriculture: A Multi-Scale Feature Fusion Approach with YOLOv3)","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591","华北大学Zhang等基于EfficientNet与FPN多尺度特征融合构建田间害虫智能检测框架:EfficientNet作为轻量级注意力嵌入骨干网络提取层次特征,集成多尺度检测与分层FPN融合以提升微小、不显眼的害虫识别性能。在37种田间作物常见害虫上的实验表明,所提模型在IoU阈值0.5下的平均精度(mAP@0.5)达到98.89%、平均识别错误率1.57%、平均推理时间仅0.048秒\u002F图像,在检测精度和实时性能间取得平衡,为田间害虫监测提供轻量、可靠、自动化的监测解决方案,促进数据驱动、精准的害虫管理,推动可持续绿色精准农业。","## 1. Introduction\n\nSustainable production of staple cereal (e.g., wheat and maize) and oilseed crops (e.g., soybean and canola) underpins global agricultural stability and catalyzes the advancement of precision and ecologically sustainable farming systems [[1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B1-agronomy-16-01591),[2](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B2-agronomy-16-01591)]. However, pest infestation remains the predominant biotic constraint on crop productivity, accounting for global yield losses ranging from 10% to 28%, with even more severe losses reported in developing countries [[3](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B3-agronomy-16-01591)]. Additionally, pests are indirectly responsible for major losses in few plants, such as cankers and shoot blights in nuts, since many insects serve as a vector for pathogens [[4](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B4-agronomy-16-01591)]. Beyond significant yield reduction, pest infestation often compels farmers to increase pesticide application, hinders the development of green and sustainable agriculture [[5](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B5-agronomy-16-01591),[6](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B6-agronomy-16-01591)]. These adverse effects further disrupt routine field management, reduce crop productivity, and hinder the implementation of precision agriculture, forming a core bottleneck limiting the stability and sustainability of agricultural production systems. With the global transition toward digital and precision agriculture, mitigating pest-induced hazards is essential for advancing agronomic development and optimizing crop cultivation and management [[7](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B7-agronomy-16-01591)]. Traditional pest monitoring relies heavily on manual field surveys, which are characterized by low operational efficiency, delayed feedback loops, and limited spatial coverage [[8](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B8-agronomy-16-01591)]. In the absence of robust, reliable monitoring systems, large-scale pest outbreaks can emerge readily—compromising agroecosystem integrity and significantly reducing crop productivity [[9](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B9-agronomy-16-01591)].\n\nIn recent years, advances in deep learning and computer vision have built a new intelligent agricultural risk management paradigm [[10](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B10-agronomy-16-01591)]. Vision-based pest detection has become a mainstream research direction, outperforming manual surveys in field practicality [[11](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B11-agronomy-16-01591)]. Convolutional neural network (CNN)-based pest identification methods automatically extract hierarchical visual features to enable fast, high-accuracy pest classification [[12](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B12-agronomy-16-01591),[13](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B13-agronomy-16-01591)]. The hierarchical features extracted by the VGG network are shown in [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#fig_body_display_agronomy-16-01591-f001)a. Compared with traditional methods, deep learning detection techniques have superior performance in real-time capability, scalability and accuracy, relying on standardized unified frameworks. As illustrated in [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#fig_body_display_agronomy-16-01591-f001)b, mainstream object detectors adopt a four-stage structure: input, backbone, neck and head ([Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#fig_body_display_agronomy-16-01591-f001)b). These detectors are broadly categorized into single-stage (dense prediction) and two-stage (sparse prediction) paradigms. Each paradigm further subdivides into anchor-based and anchor-free approaches. Representative dense-prediction models include the anchor-based YOLO and RetinaNet, as well as the anchor-free CenterNet and FCOS. In contrast, sparse-prediction models encompass the anchor-based Faster R-CNN and the anchor-free RepPoints. Among these algorithms, the YOLO series models have attracted considerable attention due to their excellent balance between detection speed and accuracy [[14](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B14-agronomy-16-01591),[15](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B15-agronomy-16-01591)].\n\n**Figure 1.** Schematic diagram of convolutional neural network feature extraction and object detection structure. (**a**) Schematic diagram of feature extraction in convolutional neural networks; (**b**) Schematic diagram of the target detection structure.\n\nAlthough YOLO-based architectures have demonstrated robust performance in agricultural pest monitoring, several critical methodological limitations persist in practical field-scale crop pest detection tasks. First, the original Darknet-53 backbone of YOLOv3 lacks attention modules, failing to capture subtle features of tiny pests in complex field backgrounds [[16](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B16-agronomy-16-01591),[17](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B17-agronomy-16-01591)], leading to misclassification of similar pest species and frequent missed detections [[18](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B18-agronomy-16-01591)]. Second, the primitive simple upsampling feature fusion module of original YOLOv3 fails to construct complete top-down multi-scale feature transmission, weakening the model’s ability to distinguish small-size pests against cluttered leaf [[19](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B19-agronomy-16-01591),[20](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B20-agronomy-16-01591)]. Existing feature fusion frameworks rarely construct bidirectional feature circulation, restricting miniature pest recognition [[21](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B21-agronomy-16-01591),[22](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B22-agronomy-16-01591)]. Third, most detection models adopt generic anchor boxes pre-trained on public natural image datasets. The size distribution of these anchor boxes may not match miniature field pests, leading to frequent missed localization of small pest individuals. Dataset-adaptive anchor clustering effectively alleviates this issue [[23](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B23-agronomy-16-01591),[24](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B24-agronomy-16-01591)]. Fourth, existing YOLO variants struggle to balance detection precision and computational overhead, forming an unaddressed trade-off dilemma [[25](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B25-agronomy-16-01591)]. Deep heavy backbones deliver favorable recognition accuracy but incur excessive inference latency, which cannot be deployed on low-power portable edge devices for field real-time monitoring [[26](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B26-agronomy-16-01591)]. While lightweight YOLO-tiny models cut computational cost by simplifying network layers, yet the compressed feature extraction capacity drastically reduces detection accuracy for small and overlapping pests [[27](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B27-agronomy-16-01591)]. Furthermore, few datasets have been specially annotated for wheat, corn, soybean and canola pests, restricting the targeted optimization and benchmark evaluation of detection algorithms for dominant field crops [[28](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B28-agronomy-16-01591),[29](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B29-agronomy-16-01591)]. To address the above shortcomings, this study proposes an optimized YOLOv3-based intelligent pest detection framework with three targeted optimizations: replacing Darknet-53 with lightweight attention-equipped EfficientNet as the feature extraction backbone, introducing a complete Feature Pyramid Network (FPN) for comprehensive multi-scale feature fusion, and comparing the COCO anchor boxes with the anchor boxes that are adaptively generated from the dataset through K-means clustering, thereby selecting the scale features that are more suitable for field pests. Additionally, a manually annotated multi-class pest dataset covering four major field crops is constructed to validate the performance of the optimized detection model. The proposed model demonstrates enhanced pest detection performance and delivers a computationally efficient, lightweight detection solution, thereby providing technical support for intelligent and precision monitoring of crop pests in agricultural fields.\n\n## 2. Material and Methods\n\n### 2.1. Dataset\n\nThe experimental image data in this paper is sourced from the Anhui Academy of Agricultural Sciences [[30](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B30-agronomy-16-01591),[31](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B31-agronomy-16-01591)], covering 4285 images of 37 types of pests found in several common field crops, including corn, soybean, wheat, and canola. All images were captured by the use of digital cameras (e.g., Canon, Nikon, Tokyo, Japan, and mobile devices). Among them, 3162 images (73.8% of the entire dataset) contained only a single pest individual; the remaining 1123 images (26.2% of the total) contained two or more overlapping pest instances. Furthermore, Following the standard pixel-area-based threshold definition used in YOLO-based detection tasks, we classified annotated pest objects into three size categories and computed their respective proportions: small targets (pixel area \u003C 32 2) constitute 47.2%, medium targets (32 2 ≤ pixel area \u003C 96 2) account for 39.6%, and large targets (pixel area ≥ 96 2) represent only 13.2%. The predominance of small-scale pests further underscores the necessity of our multi-scale feature fusion architecture. To eliminate the potential negative effects of illumination variability, all sample images were preprocessed with uniform illumination settings in crop field situations, as was done by Wen and Guyer (2012) [[32](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B32-agronomy-16-01591)]. The various types of pest examples included in the dataset are shown in [Figure 2](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#fig_body_display_agronomy-16-01591-f002), and the names and quantities of each type of pest are listed in [Table 1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#table_body_display_agronomy-16-01591-t001).\n\n**Figure 2.** Sample images from the pest dataset, along with their corresponding identifiers, which match those listed in [Table 1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#table_body_display_agronomy-16-01591-t001).\n\n**Table 1.** Pest infestation dataset: image names and quantities.\n\n### 2.2. Data Processing\n\nTo train the model, it is necessary to label the pests in the dataset first. LabelImg is a tool for marking the position and name of the target in the image in deep learning. It is written in Python 3.12 and uses PyQt as its graphical interface. During the annotation process, the annotations are saved as XML files in the PASCAL VOC format. The images of the labeled pests and the generated XML files after labeling are shown in [Figure 3](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#fig_body_display_agronomy-16-01591-f003).\n\n**Figure 3.** Sample images and annotated pest files.\n\nSecondly, the dataset constitutes the foundational input for training deep neural network models, and its quality critically determines the performance and generalizability of the resulting models. Prior to model construction, it is essential to design an input data processing strategy aligned with the chosen network architecture commonly referred to as dataset preprocessing. This preprocessing stage typically comprises two key components: image rescaling and image augmentation.\n\nAll pest images in this dataset have an original resolution of 200 × 200 pixels. However, the YOLOv3 network model expects input images of size 416 × 416 pixels by default. Thus, all images are first resized to this dimension. This upsampling operation inevitably introduces interpolation artifacts, which smooth fine high-frequency spatial gradients and attenuate subtle edge-texture features, potentially degrading the model’s capability in feature extraction and precise localization of tiny pest targets. To mitigate the adverse effects of such gradient distortion, we implement a suite of coordinated optimization strategies: (i) the multi-scale feature fusion architecture inherent to YOLO-series networks preserves delicate local gradient information through shallow feature branches, specifically enhancing sensitivity to small objects; (ii) dataset-adaptive for anchor box generation ensures anchor dimensions closely match the actual pixel scales of miniature pests, thereby reducing localization bias arising from blurred target boundaries; and (iii) L2 regularization and gradient accumulation configured via the hyperparameters jointly stabilize gradient updates that would otherwise fluctuate due to resizing-induced artifacts.\n\nA unified baseline augmentation pipeline is implemented identically across all four models to guarantee fair cross-architecture comparison. All augmentation operations are applied on-the-fly during training rather than generating a static offline expanded dataset; the raw dataset retains a fixed total of 4285 original images, with unique random distortions generated dynamically for every training iteration. The shared augmentation suite includes standardized geometric and photometric transformations, with full parameter constraints defined as follows: (1) Isotropic random scaling: Each image and its corresponding bounding boxes are scaled synchronously with a randomly sampled scaling factor ranging from 0.5 to 1.5, which avoids extreme shrinkage or magnification that would obscure pest morphological features; (2) Random translation: Horizontal and vertical translation offsets are restricted within ±15% of the total image width and height to prevent partial cropping of pest bodies. (3) Horizontal flipping and color jittering: Random horizontal mirroring is performed with a 50% probability, while mild brightness, contrast and saturation jitter are applied to enrich photometric diversity without distorting pest texture identities. Meanwhile, to prevent morphological distortion that could lead to inter-species pest misidentification, strict amplitude constraints are imposed on all geometric transformations. Concurrently, shallow feature extraction branches retain fine-grained texture gradients, while COCO anchor boxes are more suitable for the natural size distribution of the target pests in this research dataset. These measures collectively enhancing the model’s ability to discriminate visually similar pest individuals, even under augmentation-induced distortions. Since all YOLO variants are bounding-box–based object detectors, ground-truth bounding box coordinates are transformed in strict synchrony with each augmentation operation, thereby preserving consistent spatial alignment between input images and their corresponding annotation labels.\n\nBeyond the shared baseline preprocessing, YOLOv4 and YOLOv4-tiny incorporate an exclusive additional data augmentation technique named Mosaic augmentation, a refined variant derived from CutMix [[33](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B33-agronomy-16-01591)], while Mosaic augmentation is not activated during the training of YOLOv3 and YOLOv3-tiny. The detailed operating procedure of Mosaic augmentation is as follows. Firstly, the Mosaic data augmentation technique combines four different input images (each with its corresponding bounding box) into a single composite image. Following this integration, a new image and its corresponding bounding boxes are generated and subsequently fed into the neural network for training and optimization. Specifically, each of the four constituent images is first independently subjected to a series of geometric and photometric transformations, including horizontal flipping, isotropic scaling, and color-space jittering. The transformed images are then spatially arranged in the top-left, bottom-left, bottom-right, and top-right quadrants of the output canvas, respectively. Finally, fixed subregions from each quadrant are extracted via matrix indexing and seamlessly stitched together to form the final mosaic image, whose merged bounding boxes are fed into the network for training and optimization.\n\n### 2.3. The Framework Structure of the YOLO Model\n\nYOLOv3 is developed based on YOLOv1 and YOLOv2. As shown in [Figure 4](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#fig_body_display_agronomy-16-01591-f004), it is a fully convolutional network with a total of 252 layers, using Darknet53 as the backbone feature extraction network [[34](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B34-agronomy-16-01591)]. To enhance the detection ability for small objects, the network adopts the methods of feature map upsampling and multi-scale feature fusion, conducting detection on feature maps of multiple scales. The input is the image of pests. The backbone feature extraction network adopts the 52 convolutional layers of Darknet53 after removing the last fully connected layer, which consists of 5 groups of ResNet residual blocks. Each group of residual blocks is repeated 1, 2, 8, 8, and 4 times, respectively. During each repetition process, a 1 × 1 convolution is executed first, followed by a 3 × 3 convolution, and finally the filter is halved and then restored. YOLO takes the entire insect damage image as the input of the network. After the backbone network extracts the feature map of a certain size (S × S), the entire insect damage image is divided into S × S grids. Each grid predicts three bounding boxes. If the center of a certain insect damage target falls within a certain grid, then this grid is responsible for predicting this insect damage target. The output layer outputs the position of the bounding box and the category to which the insect damage target belongs. Each bounding box predicts four positions (x, y, w, h) and one confidence, totaling five values. Each grid also predicts one category information, with a total of C categories (there are 37 types of insect damage in the dataset of this article). For one anchor box, the total number of predicted channels equals 5 + 37 = 42; since each grid predicts three boxes, the total channel count per grid is 3 × 42 = 126, and ultimately the output tensor of each detection branch takes the shape of S × S × 126, where S × S stands for the spatial grid partition of the feature map, channels 1–4 correspond to the bounding box regression parameters (x, y, w, h) of the first anchor box, channel 5 represents the objectness confidence score of the first anchor box, channels 6–42 denote the class probability distribution of the first anchor box, and this identical channel layout is repeated sequentially for the second and third anchor boxes within every grid cell.\n\n**Figure 4.** Schematic diagram of the YOLOv3 structure.\n\n**Figure 4.** Schematic diagram of the YOLOv3 structure.\n\n[![Image 1: Agronomy 16 01591 g004](https:\u002F\u002Fwww.mdpi.com\u002Fagronomy\u002Fagronomy-16-01591\u002Farticle_deploy\u002Fhtml\u002Fimages\u002Fagronomy-16-01591-g004.png)](https:\u002F\u002Fwww.mdpi.com\u002Fagronomy\u002Fagronomy-16-01591\u002Farticle_deploy\u002Fhtml\u002Fimages\u002Fagronomy-16-01591-g004.png)\n\nYOLOv3-tiny is a compressed version of the YOLOv3 network, which has removed the residual layers and only uses two YOLO output layers of different scales. The network has a total of 23 layers, including 13 convolutional layers, 6 pooling layers, 1 routing layer, 1 upsampling layer, and 2 output layers. Except for the convolutional layer before the YOLO output layer, which uses a linear activation function, each convolutional layer is followed by a BN layer, and each convolutional layer is followed by a convolutional layer with a leaky rectified linear unit (Leaky ReLU) activation function [[35](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1591#B35-agronomy-16-01591)]. The output dimensions of the two output layers are","MDPI Agronomy 16(16):1591 2026年8月18日","2026-08-18T00:00:00Z",76,{"impact":361,"substance":508,"depth":361,"authority":416,"freshness":173,"relevant":455,"comment":785},"论文提出多尺度特征融合的害虫检测方法，方法新颖，数据详实，对智慧农业有实质贡献。",[787],{"name":781,"url":778},[789,790,791,792,793],"精准农业","害虫检测","EfficientNet","FPN多尺度融合","MDPI Agronomy",{"code":256,"slug":257,"name":258,"path":259,"parent_code":200},[],{"status":595,"locked":580,"version":4,"method":596,"evidence":797},{"candidates":798,"agricultural_evidence":601},[799,803],{"code":16,"score":400,"supported":601,"title":800,"abstract":801,"tags":802},[],[789],[789],{"code":256,"score":584,"supported":601,"title":804,"abstract":805,"tags":806},[],[591],[],[808,809],"FPN多尺度融合 害虫检测 精准农业 MDPI Agronomy","FPN多尺度融合 害虫检测","FPN多尺度融合害虫检测精准农业MDPIAgronomy-843","2026-08-22T00:06:00.849334Z",{"id":813,"title":814,"url":815,"summary":816,"summary_zh":11,"content":817,"source_name":818,"source_url":11,"published_at":819,"category":579,"cover_url":11,"hotness":385,"is_selected":580,"score":820,"score_detail":821,"sources":823,"tags":825,"primary_topic":831,"secondary_topics":832,"classification":835,"search_phrases":853,"slug":856,"view_count":400,"doi":11,"paper":11,"created_at":857},790,"基于处方图的变量喷药系统设计与试验(大豆-玉米带状复合种植):实时处方图解析、时空喷嘴匹配与延时补偿(原标题:Design and Experiment of a Prescription-Map-Based Variable-Rate Spraying System for Soybean-Maize Strip Intercropping)","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784","为满足大豆-玉米带状复合种植中差异化施药需求,本研究开发了基于实时处方图解析、时空喷嘴匹配与延时补偿的带状变量喷药系统。使用ArcMap生成具有预定施药量级别的模拟处方图,基于高精度RTK-BDS定位建立田间坐标转换模型并结合SRAM预加载实现快速处方矩阵寻址;开发喷嘴空间位置预测模型与前向延时补偿控制算法,减少动态作业中由定位偏移、执行器响应滞后和液压延迟引起的边界错位。带状变量喷药试验中延时补偿后平均边界偏移由0.69米降至0.29米;大豆与玉米带不同施药量级下平均流量控制精度均超95%,流量变异系数均低于6%。","## 1. Introduction\n\nMaize and soybean are globally important food, feed, and oilseed crops, playing vital roles in ensuring food security, supporting livestock production, and facilitating agricultural processing and utilization. Over time, maize–soybean cropping systems have been continuously optimized [[1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B1-agriculture-16-01784),[2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B2-agriculture-16-01784),[3](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B3-agriculture-16-01784),[4](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B4-agriculture-16-01784)]. Soybean–maize strip intercropping optimizes the spatial arrangement of the two crops, enabling stable maize production while expanding soybean cultivation, thereby improving land-use efficiency and promoting coordinated grain and oilseed production [[5](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B5-agriculture-16-01784),[6](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B6-agriculture-16-01784),[7](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B7-agriculture-16-01784)]. However, soybean and maize differ markedly in inter-row canopy structure, crop-protection requirements, and herbicide tolerance, making conventional large-scale uniform spraying prone to crop injury and missed applications [[8](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B8-agriculture-16-01784),[9](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B9-agriculture-16-01784)]. Prescription-map-based variable-rate spraying enables real-time adjustment of application rates according to field weed distribution, crop growth status, and machinery operating parameters, providing an important approach to advancing precision crop protection [[10](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B10-agriculture-16-01784),[11](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B11-agriculture-16-01784),[12](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B12-agriculture-16-01784)].\n\nWith the development of precision agriculture, variable-rate pesticide application has become an important research direction in agricultural engineering. In recent years, it has attracted extensive attention from researchers worldwide and has gradually emerged as a research hotspot [[13](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B13-agriculture-16-01784),[14](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B14-agriculture-16-01784),[15](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B15-agriculture-16-01784),[16](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B16-agriculture-16-01784)].\n\nExisting studies show that variable-rate pesticide application has achieved clear progress in prescription map generation, remote-sensing image recognition, spray actuator control, and improvement of application effectiveness [[17](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B17-agriculture-16-01784)]. Hamouz et al. constructed field-zoned application maps, and their experiments showed that site-specific weed control reduced herbicide use by 15.6–100% compared with conventional whole-field uniform herbicide application [[18](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B18-agriculture-16-01784)]. With advances in remote sensing and aerial operation technologies, prescription map generation has gradually expanded from traditional manual sampling and grid surveys to airborne platforms, unmanned aerial vehicles, and multispectral image recognition. Yang et al. integrated an airborne multispectral imaging system to generate prescription maps and used the imaging system to evaluate post-application effects [[19](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B19-agriculture-16-01784)]. Campos et al. used an unmanned aerial vehicle equipped with a multispectral camera to acquire crop canopy information for prescription-map generation and conducted field evaluations of prescription-map-based variable-rate spraying [[20](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B20-agriculture-16-01784)]. Guan et al. reviewed advances in variable-rate spraying from two perspectives: information acquisition based on prescription maps or real-time sensors, and three variable-rate control strategies involving pressure regulation, direct injection, and pulse-width modulation [[21](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B21-agriculture-16-01784)]. Yin et al. developed an indoor prescription-map-based variable-rate herbicide spraying system, in which weed images were acquired to generate prescription maps, and precise targeted spraying was achieved by selectively controlling individual nozzles [[22](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B22-agriculture-16-01784)]. Abioye et al. developed a deep-learning-guided variable-rate spraying robot that integrates visual perception and deep learning for target plant identification, enabling targeted and variable-rate spraying and providing a new approach to integrating prescription maps, machine vision, and robotic actuation [[23](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B23-agriculture-16-01784)].\n\nDespite substantial advances in variable-rate spraying, offline prescription-map-based application still involves time-consuming preparatory steps, including data acquisition, processing, and prescription map generation [[10](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B10-agriculture-16-01784)]. During dynamic field operations, the microcontroller must continuously parse positioning data, perform spatial matching, and retrieve prescription values, placing stringent real-time demands on online prescription map interpretation under limited computational and memory resources [[24](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B24-agriculture-16-01784)]. Moreover, inherent response delays from control-command output to solenoid-valve actuation and spray formation can translate into spatial application offsets as the sprayer moves, thereby compromising flow-rate control accuracy [[25](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B25-agriculture-16-01784),[26](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B26-agriculture-16-01784)].\n\nAccordingly, this study developed a variable-rate spraying system for soybean–maize strip intercropping that innovatively integrates fast embedded prescription-matrix addressing, nozzle-level spatial position prediction, and dynamic delay compensation to achieve precise spatiotemporal matching between prescription information and actual spray locations. The main contributions are as follows: A prescription-map-based strip-specific variable-rate spraying system was proposed for soybean–maize strip intercropping, providing technical support for differentiated and potentially more efficient pesticide application; a rapid prescription-map interpretation method was implemented on the embedded controller to enable real-time matching between machine position and target application rate, thereby improving prescription retrieval efficiency; and a prescription map–nozzle matching delay-compensation method was developed and validated through outdoor droplet-deposition, delay-compensation, and strip-specific variable-rate spraying tests, demonstrating the feasibility of the proposed system.\n\n## 2. Materials and Methods\n\n### 2.1. System Architecture\n\n#### 2.1.1. Overall System Design\n\nThe overall design of the strip-specific variable-rate spraying system for soybean–maize strip intercropping is shown in [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#fig_body_display_agriculture-16-01784-f001). Because maize and soybean differ markedly in pesticide tolerance and application requirements, the system adopts two independent pesticide delivery pipelines to enable separate strip-specific application for maize and soybean. To prevent spray liquid from drifting between different crop zones during application, anti-drift nozzles were selected, and cover structures were installed outside the soybean nozzles to reduce the risk of droplet drift.\n\n**Figure 1.** Overall structure of the variable-rate spraying system. 1. water tank; 2. return valve; 3. check valve; 4. maize pesticide tank; 5. peristaltic pump; 6. Venturi injector; 7. pesticide mixer; 8. pressure sensor; 9. flow sensor; 10. high-speed solenoid valve; 11. anti-drift nozzle; 12. shroud; 13. microcontroller; 14. GNSS receiver; 15. water pump; 16. RTK-BDS; 17. soybean pesticide tank.\n\nBefore field operation, strip-specific variable-rate prescription maps are generated on a GIS platform using crop growth information and converted into a format suitable for embedded processing. During operation, the main controller receives real-time RTK-BDS positioning data, calculates the spatial position of each nozzle based on the boom geometry, and retrieves the corresponding target application rate through prescription-map addressing. Flow and pressure sensors in the two independent spray circuits provide real-time feedback, and the controller adjusts the PWM duty cycles of the high-speed solenoid valves according to the deviation between the target and measured flow rates, thereby enabling independent variable-rate application to the maize and soybean strips. In the hydraulic system, the spray solution sequentially passes through the pressure sensor, flow sensor, and high-speed solenoid valve before being delivered to the drift-reducing nozzles, with every three nozzles forming an independent control unit.\n\n#### 2.1.2. Control System Design\n\nSTM32 series microcontrollers are characterized by high performance, low power consumption, and low cost, and are therefore commonly used as core controllers in control systems [[27](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B27-agriculture-16-01784),[28](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B28-agriculture-16-01784),[29](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B29-agriculture-16-01784)]. Based on the prescription map construction and interpretation method described above, together with the variable-rate pesticide application control algorithm, a soybean–maize variable-rate pesticide application control system was designed with an STM32F103ZET6 microcontroller (STMicroelectronics, Geneva, Switzerland) as the core. The system mainly consists of a power supply module, signal acquisition module, main controller module, actuator module, and communication module.\n\nThe overall architecture of the variable-rate spraying control system is shown in [Figure 2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#fig_body_display_agriculture-16-01784-f002). During operation, the RTK-BDS positioning terminal continuously outputs the sprayer’s longitude, latitude, heading, and travel speed, which are transmitted to the main controller via a serial interface. Using the preloaded prescription-map data and real-time positioning information, the controller performs field-coordinate transformation, grid identification, and prescription-map addressing to determine the target application rate for the current or predicted spray position. Meanwhile, flow-rate and pressure measurements from the two independent spray circuits are fed back to the main controller. An Improved Beetle Antennae Search (IBAS)-optimized Fuzzy-PID controller calculates the control output from the deviation between the target and measured flow rates. The resulting PWM duty cycles are transmitted to the actuator module via the communication bus, enabling independent variable-rate application to the soybean and maize strips.\n\n**Figure 2.** Overall structure of the variable-rate pesticide application control system.\n\nThe system software employs a hybrid task-scheduling architecture that combines a main-loop routine with interrupt-driven processing. Time-critical signals, such as flow-meter pulses, are processed using timer input-capture interrupts. The main loop sequentially performs RTK-BDS positioning updates, nozzle-position calculation and prescription-map addressing, sensor-data processing for the two spray circuits, flow-control computation, and command output. To reduce the computational burden of multichannel actuation on the MCU, the main controller only calculates and updates the PWM duty cycle for each channel, while the actual PWM signals are generated independently by an external multichannel PWM module to drive the high-speed solenoid valves. This architecture reduces the real-time processing load associated with continuous PWM generation on the main controller.\n\nIn terms of hardware, an NZ510G high-precision intelligent positioning terminal (Yanshi Zhikong, Beijing, China) was selected as the GNSS receiver positioning device for the variable-rate spraying system. Spray output was regulated by pulse-width modulation (PWM), whereby the duty cycle of the solenoid valve at each nozzle was adjusted by controlling its opening and closing durations. Consequently, the response speed, corrosion resistance, and operational stability of the solenoid valves directly affect spray-control performance. Considering the field operating environment and the properties of pesticide solutions, normally closed 2WA030-08B solenoid valves (AirTAC (China) Co., Ltd., Ningbo, China) were selected.\n\n### 2.2. Prescription Map Construction\n\n#### 2.2.1. Overview of ArcMap Software\n\nArcMap is a geographic information system software developed by Esri and is one of the major desktop applications of the ArcGIS 10.8.1 platform. ArcMap supports various spatial data formats, including vector data, raster data, and spatial database data, and has been widely applied in geographic information-related fields such as environmental management, urban planning, agricultural monitoring, disaster risk assessment, and emergency management [[30](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B30-agriculture-16-01784),[31](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B31-agriculture-16-01784),[32](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#B32-agriculture-16-01784)].\n\nThe proposed system requires a GIS platform with high spatial accuracy and compatibility with the data structure used by the embedded controller. ArcMap provides coordinate registration, projection transformation, regular grid generation, and attribute-data processing, enabling spatial alignment between prescription maps and RTK positioning data. Regular grids generated using the Fishnet tool facilitate spatial matching and prescription-value retrieval, while attribute data can be exported and converted into binary files for embedded processing. Thus, ArcMap satisfies the system requirements for spatial accuracy, grid-based processing, and data export.\n\n#### 2.2.2. Acquisition of Field Position Information\n\nField position information is a prerequisite for prescription map construction and variable-rate pesticide application control. In this study, a set of simulated prescription map data conforming to the spatial distribution pattern of soybean–maize strips was generated for testing. The dataset included the spatial position information of the operating area and the corresponding prescription values, as shown in [Table 1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#table_body_display_agriculture-16-01784-t001). These coordinates were used for prescription-map construction.\n\n**Table 1.** Longitude, latitude, and prescription values used for constructing the simulated prescription map.\n\nTo facilitate subsequent prescription map interpretation and invocation by the pesticide application control system, the above data were imported into ArcMap for organization and preprocessing, including spatial extent inspection, attribute field verification, and coordinate reference system unification. Through data preprocessing, the prescription map data and BeiDou positioning information were aligned to a unified spatial reference, and the correspondence between the spatial positions of the operating area and the prescription values was established.\n\nAfter processing, the basic spatial dataset required for subsequent prescription unit division, prescription map construction, and addressing-based matching was obtained, providing data support for real-time prescription retrieval by the variable-rate spraying system.\n\n#### 2.2.3. Prescription Unit Division and Application-Rate Assignment\n\nTo enable the spatial representation of prescription map data and subsequent invocation by the variable-rate spraying system, the original longitude, latitude, and prescription value data of the operating area must be converted into prescription units with spatial position attributes, and the correspondence between prescription units and application rates must be established. The original data used in this study were stored in tabular form and mainly included fields such as longitude, latitude, and the corresponding prescription value.\n\nTo spatially represent the prescription data for subsequent variable-rate spraying, the raw dataset containing longitude, latitude, and prescription values was imported into ArcMap 10.8.1. As shown in [Figure 3](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#fig_body_display_agriculture-16-01784-f003), a prescription-point layer was generated using the Add XY Data function, and the coordinate reference system was standardized to ensure spatial consistency with RTK-BDS positioning data. The resulting event layer was then exported as a vector layer, and the correspondence between spatial locations and prescription values was verified, providing the basis for subsequent prescription-cell delineation, prescription-map generation, and spatial addressing.\n\n**Figure 3.** Generation of the operating-area layer.\n\nAs shown in [Figure 4](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#fig_body_display_agriculture-16-01784-f004), a regular rectangular grid was generated over the operating area using the Fishnet tool in ArcMap. For the 4:2 soybean–maize strip intercropping configuration, the transverse grid resolution, Δx, was set equal to the actual crop-strip width to accurately represent strip-specific application boundaries. Prescription values were then assigned to the corresponding grid cells, establishing a one-to-one correspondence between spatial location and target application rate. Each grid cell therefore contained a unique spatial location and application attribute, representing the target application requirement for the corresponding area.\n\n**Figure 4.** Generation of the regular rectangular grid.\n\n#### 2.2.4. Construction of the Simulated Prescription Map\n\nAfter prescription-cell delineation and assignment of application rates, a prescription map of the operating area was generated to visualize the spatial distribution of application requirements and provide input data for subsequent prescription-map interpretation and variable-rate spraying control. In ArcMap, the operating area was discretized into regular grid cells based on the assigned prescription-cell layer, and the cells were classified according to their prescription values and displayed using different colors to distinguish application-rate levels, as shown in [Figure 5](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1784#fig_body_display_agriculture-16-01784-f005). The completed prescription map was then visually inspected to verify the prescription-cell delineation and spatial distribution of prescription values, thereby confirming the completeness and consistency of the prescription-map data.\n\n**Figure 5.** Prescription map for soybean–maize strip intercropping.\n\nOn the basis of the visualized prescription map, the structured attribute data of the prescription map must be further exported to meet the requirements of the subsequent prescription map interpretation system for grid identification and rapid addressing. In this study, row number r and column number c fields were established for each grid cell in ArcMap and saved together with the prescription value field, forming an attribute table containing both grid index information and prescription value information. The relevant attribute data were then exported as a .csv file and further converted into a .bin file readable by the microcontroller. Through this process, the visualized prescription map generated in ArcMap was converted in","MDPI Agriculture 16(16):1784 2026-08-20","2026-08-20T00:00:00Z",80,{"impact":361,"substance":321,"depth":361,"authority":345,"freshness":173,"relevant":455,"comment":822},"针对大豆-玉米带状复合种植的处方图变量喷药系统研究，方法新颖，数据详实，对精准农业有实质贡献。",[824],{"name":818,"url":815},[826,827,828,829,830],"处方图","MDPI Agriculture","大豆玉米带状复合种植","变量喷药","延时补偿",{"code":224,"slug":225,"name":226,"path":227,"parent_code":200},[833,834],{"code":232,"slug":233,"name":234,"path":235,"parent_code":200},{"code":256,"slug":257,"name":258,"path":259,"parent_code":200},{"status":595,"locked":580,"version":4,"method":596,"evidence":836},{"candidates":837,"agricultural_evidence":601},[838,842,849],{"code":224,"score":508,"supported":601,"title":839,"abstract":840,"tags":841},[226,225],[226],[828],{"code":232,"score":843,"supported":601,"title":844,"abstract":847,"tags":848},19,[845,846],"大豆","soybean",[845],[],{"code":256,"score":173,"supported":601,"title":850,"abstract":851,"tags":852},[605],[],[],[854,855],"大豆玉米带状复合种植 变量喷药 延时补偿 处方图","大豆玉米带状复合种植 变量喷药","大豆玉米带状复合种植变量喷药延时补偿处方图-790","2026-08-21T00:05:57.531874Z",{"id":859,"title":860,"url":861,"summary":862,"summary_zh":11,"content":863,"source_name":864,"source_url":11,"published_at":865,"category":678,"cover_url":11,"hotness":385,"is_selected":580,"score":820,"score_detail":866,"sources":869,"tags":871,"primary_topic":875,"secondary_topics":876,"classification":877,"search_phrases":889,"slug":892,"view_count":455,"doi":11,"paper":11,"created_at":893},452,"福建农科院联合中国热科院南亚所创新番薯—绿肥轮作技术——50亩田间示范落地广东湛江","https:\u002F\u002Fwww.faas.cn\u002Fcms\u002Fhtml\u002Ffjsnykxy\u002F2026-08-12\u002F1674801897.html","福建省农科院农科沃土生态草业产业研究院联合中国热带农业科学院南亚热带作物研究所,在广东省湛江市麻章区湖光镇举行'旱地—绿肥轮作栽培技术观摩会'。夏播60天,印度豇豆、崖州硬皮豆鲜草亩产分别1840.92公斤、1260.63公斤,豆科绿肥依托根瘤实现生物固氮,翻压还田后每亩可分别替代尿素17.96公斤、12.31公斤,有效削减了化肥施用量。科研团队创新实现绿肥播种与番薯采收同步作业,大幅压缩生产用工与时间成本,绿肥生长期无需额外追肥、灌溉,盛花期直接翻压还田,经10-15天充分腐解后即可栽种下一季番薯,形成闭环生态循环种植模式。","近日，在广东省农业环境与耕地质量保护中心支持下，中国热带农业科学院南亚热带作物研究所联合福建省农业科学院农科沃土生态草业产业研究院，在广东省湛江市麻章区湖光镇举行“旱地—绿肥轮作栽培技术观摩会”。\n\n夏播60天，印度豇豆、崖州硬皮豆鲜草亩产分别1840.92公斤、1260.63公斤，豆科绿肥依托根瘤实现生物固氮，翻压还田后每亩可分别替代尿素17.96公斤、12.31公斤，有效削减了化肥施用量。50亩田间示范现场集中展示闽粤联合创新的番薯—绿肥同步轮作核心技术体系。科研团队创新实现绿肥播种与番薯采收同步作业，大幅压缩生产用工与时间成本：番薯割除薯藤后，通过无人机飞播或人工撒播绿肥种子，后续机械化挖薯作业可自然完成覆土播种，无需单独整地工序。绿肥生长期无需额外追肥、灌溉，盛花期直接翻压还田，经10至15天充分腐解后即可栽种下一季番薯，形成闭环生态循环种植模式。\n\n两类绿肥田间控草率达70%至85%，显著降低田间除草人工成本，绿肥还田能够持续改良酸化板结红壤，活化土壤残留养分、优化土壤微生物群落，减轻线虫等土传病害，实现番薯化肥农药双减，同步提升作物产量与商品品质，达成改土、降本、增产、提质综合效益。\n\n“南方红黄壤旱地土壤条件相近、种植痛点高度重合，闽粤两省番薯种植规模大，旱地、果园土壤改良需求迫切，拥有扎实的科研协作基础与广阔的产业互补空间。”农科沃土生态草业产业研究院理事长陈得盛表示，“本次印度豇豆、崖州硬皮豆分别是福建省农科院资环所和中国热科院品种资源研究所选育的南方优质绿肥草种，由研究院提供，联合中国热科院南亚所完成本土化配套栽培技术集成，跨省份合作打破地域科研壁垒，对构建华南区域协同农业科技创新体系有重要示范价值。”（2026年8月11日农民日报报道，全媒体记者：蔡茂楷，资环所通讯员：詹杰）","农民日报\u002F福建省农科院 2026-08-12","2026-08-12T00:00:00Z",{"impact":508,"substance":321,"depth":867,"authority":416,"freshness":385,"relevant":455,"comment":868},16,"闽粤跨省合作创新番薯绿肥轮作技术，有具体数据和示范效果，对南方旱地农业有推广价值。",[870],{"name":864,"url":861},[872,873,702,874],"福建省农科院","番薯绿肥","减肥",{"code":256,"slug":257,"name":258,"path":259,"parent_code":200},[],{"status":595,"locked":580,"version":4,"method":596,"evidence":878},{"candidates":879,"agricultural_evidence":601},[880,884],{"code":256,"score":416,"supported":601,"title":881,"abstract":882,"tags":883},[702],[702],[702],{"code":152,"score":584,"supported":601,"title":885,"abstract":886,"tags":888},[],[887],"灌溉",[],[890,891],"福建省农科院 番薯绿肥 减肥 轮作","福建省农科院 番薯绿肥","福建省农科院番薯绿肥减肥轮作-452","2026-08-12T23:57:22.843489Z",{"id":895,"title":896,"url":897,"summary":898,"summary_zh":11,"content":899,"source_name":900,"source_url":11,"published_at":901,"category":678,"cover_url":11,"hotness":385,"is_selected":580,"score":93,"score_detail":902,"sources":904,"tags":906,"primary_topic":910,"secondary_topics":911,"classification":912,"search_phrases":960,"slug":963,"view_count":408,"doi":11,"paper":11,"created_at":964},357,"激活冬闲田!汕尾田间观摩会解锁'双季稻+麦\u002F肥\u002F菜'轮作增收新路径","https:\u002F\u002Fwww.gdaas.cn\u002Fgdiare\u002Fmtjj\u002Fcontent\u002Fpost_1334302.html","汕尾市农技推广服务中心联合广东省农业科学院农业资源与环境研究所、华南农业大学资源环境学院,在海丰县城东镇汕尾市丰美生态农业科技有限公司的180亩综合示范基地举办广东节水增粮推进县项目现场观摩会。示范基地分区打造了'双季稻—冬小麦''双季稻—甜玉米''双季稻—蔬菜''双季稻—冬种绿肥'四种周年种植模式,并配套水肥一体化滴灌、全程机械化、无人机植保、作物配方施肥等集成技术。'双季稻—冬小麦'模式实现周年化肥减量15%以上,全年粮食总产较传统单季稻提升400公斤\u002F亩;水肥一体化滴灌系统田间灌溉节水率达30%以上。","近日，汕尾市农技推广服务中心联合广东省农业科学院农业资源与环境研究所、华南农业大学资源环境学院，在海丰县城东镇汕尾市丰美生态农业科技有限公司的180亩综合示范基地，举办广东节水增粮推进县项目现场观摩会。全市农技骨干、规模种植户及农业经营主体代表共120余人走进田间，实地观摩“双季稻+麦\u002F肥\u002F菜”轮作样板，系统学习成套绿色高效栽培技术。\n\n汕尾地处南亚热带季风气候区，光热充足、雨量充沛、无霜期长，发展三熟轮作具有得天独厚的自然优势。然而长期以来，当地双季稻收获后大量稻田闲置，土地复种指数偏低，大水漫灌、盲目施肥等粗放管理方式制约了耕地产出效益。为此，示范基地分区打造了“双季稻—冬小麦”“双季稻—甜玉米”“双季稻—蔬菜”“双季稻—冬种绿肥”四种周年种植模式，并配套水肥一体化滴灌、全程机械化、无人机植保、作物配方施肥等集成技术，为汕尾地区稻田高效利用打造了可复制、可推广的示范样板。\n\n观摩现场田块分区清晰，各类作物长势对比鲜明，效果直观。技术专家向参会人员逐一拆解不同轮作模式的品种选配、茬口衔接、科学施肥及病虫害绿色防控等实操要点。其中，“双季稻—冬小麦”模式选用适配本地气候的品种，采用机械条播与种肥同播一体化作业，实现周年化肥减量15%以上，全年粮食总产较传统单季稻提升400公斤\u002F亩，稳粮保产、节本增效优势突出。“双季稻—甜玉米”和“双季稻—蔬菜”两大经济轮作模式则全覆盖水肥一体化滴灌系统，田间灌溉节水率达30%以上；甘蓝、包菜、西蓝花等蔬菜周期短、收益高，切合农户增收需求。冬种绿肥示范区重点介绍了耐旱光叶苕子的种植技术，在改良土壤的同时，全年氮肥用量可减少10%—20%，真正实现“用地与养地并举、生态与收益双赢”。\n\n观摩会上，广东省农科院资环所专家详细讲解了各类轮作模式的种植优势、技术要点和注意事项。华南农业大学资源环境学院专家则围绕肥料种类、施肥方式、用量及时期等，系统讲授了科学施肥的关键技术。\n\n“以前晚稻收完田地就空着，既浪费地力又少一笔收入，今天看了示范田才知道冬闲田大有可为，技术简单好上手，明年我打算流转几十亩试种冬小麦、油菜和光叶苕子。”汕尾市丰美生态农业科技有限公司负责人陈先生观摩后连连称赞。活动现场还为参会种植户发放了作物专用复合肥等物资，并指导科学施用。\n\n针对农户普遍关心的茬口衔接、施肥成本、病虫害防治及农产品销路等现实难题，现场农技专家逐一答疑解惑，干货满满的田间教学获得一致好评。四种轮作模式紧扣“节水增粮、提质增收、生态培肥”核心目标，集成合理密植、绿肥还田、绿色防控、精准滴灌、一喷多促等现代农业技术，在保障粮食产能的同时大幅减少化肥农药投入，探索出一条绿色低碳的耕地高效利用新路径。\n\n下一步，市、县两级农技推广部门将以本次观摩会为契机，持续优化稻后多元轮作配套技术体系，构建“示范基地+专题培训+技术指导”一体化农技服务机制，扩大示范推广覆盖面。通过将节水减肥、增产增收的成熟模式逐步复制到更多产区，充分挖掘汕尾地区冬闲田生产潜力，推进节水减肥与用地养地协同发展，持续夯实区域粮食安全根基。","南方+\u002F广东省农科院 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水肥一体化 三熟轮作 节水增粮","广东省农科院 水肥一体化","广东省农科院水肥一体化三熟轮作节水增粮-357","2026-08-10T23:54:42.579884Z",{"id":966,"title":967,"url":968,"summary":969,"summary_zh":11,"content":970,"source_name":971,"source_url":11,"published_at":972,"category":678,"cover_url":11,"hotness":385,"is_selected":580,"score":45,"score_detail":973,"sources":975,"tags":977,"primary_topic":981,"secondary_topics":982,"classification":984,"search_phrases":1015,"slug":1018,"view_count":1019,"doi":11,"paper":11,"created_at":1020},218,"河南省农科院食用菌所牵头制定黄淮海地区粮菇轮作行业标准——通过专家技术审查","https:\u002F\u002Fhnagri.org.cn\u002Farticle-113344.html","由河南省农业科学院食用菌研究所牵头制定的农业行业标准黄淮海地区粮菇轮作技术规程顺利通过专家组技术审查。该标准针对黄淮海地区小麦-玉米长期单一耕作方式、秸秆机械化还田、化肥过量使用导致的土壤质量下降等问题,将大球盖菇嵌入粮食作物种植体系,创建了粮菇轮作秸秆高值利用改土技术。该标准2025年9月立项,2026年6月通过专家审查。","2026年08月05日 星期三  繁  简 \n\n![Image 1](https:\u002F\u002Fhnagri.org.cn\u002Ftemplates\u002Fdefault\u002Fimages\u002Fnews\u002Flogo.png)\n\n![Image 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\"党建文化\")\n\n首页>新闻中心>媒体聚焦\n\n媒体聚焦\n\n《大象新闻》：河南省农科院牵头制定的食用菌领域行业标准通过专家审查\n\n近日，全国农业技术推广服务中心在北京召开了2026年第2批农业标准技术审查会（农技科函〔2026〕164号）。由河南省农业科学院食用菌研究所牵头制定的农业行业标准《黄淮海地区粮菇轮作技术规程》顺利通过专家组技术审查。这标志我省在食用菌领域行业标准制定取得了重要突破。\n\n该标准2025年9月份立项，2026年6月份通过专家审查，制标单位包括农业农村部农产品质量安全中心、山东省农业技术推广站等10家单位。该标准针对黄淮海地区小麦-玉米长期单一耕作方式，秸秆机械化还田、化肥过量使用导致的土壤质量下降等问题，将大球盖菇嵌入粮食作物种植体系，创建了粮菇轮作秸秆高值利用改土技术。以大球盖菇种植所需秸秆量为\n\n审查会上，国家食用菌产业技术体系首席专家黄晨阳表示，该标准具有较强的科学性、先进性和可操作性。该标准的制定，将进一步规范和指导黄淮海地区粮菇轮作的推广应用，为农业绿色高质量发展和乡村振兴战略提供有力的技术支撑。\n\n河南省农业科学院食用菌研究所副所长孔维丽表示，食用菌研究所2023年成立，现有硕、博士28人，是河南省唯一的省级食用菌专业研究机构，承担这河南食用菌全产业链发展中育种、栽培、加工重要任务。这是河南首次承担行业标准制定，下一步，我所将以解决产业发展关键需求为己任，持续科技创新，加大粮菇轮作技术的示范推广力度，让科研成果更好地服务于农业生产一线，为保障国家粮食安全和促进农民增收贡献力量。\n\n推广电话：0371-88916579\n\n 信息来源：食用菌所 (2026-07-07) \n\n[上一篇：《中国新闻》：河南郑州：中美农业合作 科技解锁节粮减碳新路径](https:\u002F\u002Fhnagri.org.cn\u002Farticle-113208.html)\n\n[下一篇：《顶端新闻》：科技下沉一线助油茶丰产 豫赣专家赴新县开展现场技术服务](https:\u002F\u002Fhnagri.org.cn\u002Farticle-113353.html)\n\n新闻中心\n\n[![Image 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2026-07-07","2026-07-30T00:00:00Z",{"impact":508,"substance":508,"depth":600,"authority":416,"freshness":69,"relevant":455,"comment":974},"省级科研机构牵头制定行业标准，对粮菇轮作技术推广有实质推动，但发布已近一月，时效性较低。",[976],{"name":971,"url":968},[978,311,979,980],"行业标准","粮菇轮作","河南省农科院",{"code":309,"slug":310,"name":311,"path":312,"parent_code":271},[983],{"code":256,"slug":257,"name":258,"path":259,"parent_code":200},{"status":595,"locked":580,"version":4,"method":596,"evidence":985},{"candidates":986,"agricultural_evidence":601},[987,991,995,999,1003,1007,1011],{"code":309,"score":416,"supported":601,"title":988,"abstract":989,"tags":990},[311],[311],[311],{"code":256,"score":524,"supported":601,"title":992,"abstract":993,"tags":994},[702],[702],[],{"code":216,"score":584,"supported":601,"title":996,"abstract":997,"tags":998},[],[218],[],{"code":224,"score":584,"supported":601,"title":1000,"abstract":1001,"tags":1002},[],[226],[],{"code":112,"score":584,"supported":601,"title":1004,"abstract":1005,"tags":1006},[],[944],[],{"code":144,"score":584,"supported":601,"title":1008,"abstract":1009,"tags":1010},[],[763],[],{"code":542,"score":455,"supported":580,"title":1012,"abstract":1013,"tags":1014},[],[],[978],[1016,1017],"河南省农科院 粮菇轮作 行业标准 食用菌","河南省农科院 粮菇轮作","河南省农科院粮菇轮作行业标准食用菌-218",6,"2026-08-04T23:54:46.678315Z",{"id":1022,"title":1023,"url":1024,"summary":1025,"summary_zh":11,"content":1026,"source_name":1027,"source_url":11,"published_at":1028,"category":579,"cover_url":11,"hotness":385,"is_selected":580,"score":1029,"score_detail":1030,"sources":1032,"tags":1034,"primary_topic":1039,"secondary_topics":1040,"classification":1041,"search_phrases":1067,"slug":1070,"view_count":695,"doi":11,"paper":11,"created_at":1071},202,"Pixel-Precise Explainable Stress Indexing: A Semantic Segmentation Framework for Disease Severity Quantification in Field Crops(像素级可解释压力指数:田间作物病害严重程度量化的语义分割框架)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.06585","植物病害每年造成全球农业产量损失20%-40%、经济损失超2200亿美元。文章提出统一深度学习流水线,集成语义分割、基于回归的严重程度估计和病害分类,根据感染叶片面积比例将胁迫严重程度分为四档。在包含1641样本、六个类别的苹果树叶病害分割数据集上,采用MobileNetV2的U-Net表现最佳,像素准确率98.20%、mIoU 0.70、检测准确率99.41%、单张图像处理时间仅14.7毫秒,适合实时田间应用。计算出的严重程度指数与专家标注高度相关(r=0.968, R²=0.937),为自动化作物监测和决策支持提供可靠方案。[预印本]","[Skip to main content](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.06585#content)\n\n![Image 1](https:\u002F\u002Farxiv.org\u002Fstatic\u002Fbase\u002F1.0.1\u002Fimages\u002Ficons\u002Fsmileybones-small.svg)arXiv is now an independent nonprofit![Learn more](https:\u002F\u002Finfo.arxiv.org\u002Fabout)×\n\n[](https:\u002F\u002Farxiv.org\u002FIgnoreMe)[![Image 2: archive](https:\u002F\u002Farxiv.org\u002Fstatic\u002Fbase\u002F1.0.1\u002Fimages\u002Farxiv-logo-primary-light.svg)](https:\u002F\u002Farxiv.org\u002F)[Search](https:\u002F\u002Farxiv.org\u002Fsearch)[Submit](https:\u002F\u002Farxiv.org\u002Fuser\u002Fcreate)[Donate](https:\u002F\u002Finfo.arxiv.org\u002Fabout\u002Fdonate.html)[Log in](https:\u002F\u002Farxiv.org\u002Flogin)\n\nSearch arXiv \n\n Press Enter to search · [Advanced search](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fadvanced)\n\n# Computer Science > Computer Vision and Pattern Recognition\n\n**arXiv:2607.06585** (cs) \n\n [Submitted on 5 Jul 2026]\n\n# Title:Pixel-Precise Explainable Stress Indexing: A Semantic Segmentation Framework for Disease Severity Quantification in Field Crops\n\nAuthors:[Raunak Kumar](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Kumar,+R), [Soumyashree Kar](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Kar,+S)\n\nView a PDF of the paper titled Pixel-Precise Explainable Stress Indexing: A Semantic Segmentation Framework for Disease Severity Quantification in Field Crops, by Raunak Kumar and Soumyashree Kar\n\n[View PDF](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2607.06585)[HTML (experimental)](https:\u002F\u002Farxiv.org\u002Fhtml\u002F2607.06585v1)\n> Abstract:Plant diseases, resulting from both biotic and abiotic stresses, cause an estimated 20-40% loss in global agricultural yield annually, resulting in economic damages exceeding USD 220 billion. Accurate and scalable stress quantification is essential for precision agriculture, yet traditional manual assessments are labour-intensive and subjective. This paper proposes a unified deep learning pipeline integrating semantic segmentation, regression-based severity estimation, and disease classification. Stress severity is categorised into four levels (Low to Very High) based on the proportion of infected leaf area. Experiments on the Apple Tree Leaf Disease Segmentation dataset (1,641 samples, six classes) evaluate four models: U-Net (MobileNetV2), SegFormer, FCN, and PSPNet. U-Net with MobileNetV2 achieves the best performance with 98.20% pixel accuracy, 0.70 mIoU, and 99.41% detection accuracy at 14.7 ms per image, making it suitable for real-time use. SegFormer performs competitively (mIoU 0.66), while FCN and PSPNet show lower spatial accuracy (approximately 0.49 mIoU). The computed severity index strongly correlates with expert annotations (r = 0.968, R^2 = 0.937), demonstrating the system's reliability for automated crop monitoring and decision support.\n\nComments:26 pages, 15 figures, 5 tables\nSubjects:Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)\nCite as:[arXiv:2607.06585](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.06585) [cs.CV]\n(or [arXiv:2607.06585v1](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.06585v1) [cs.CV] for this version)\n[https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2607.06585](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2607.06585)\n\nFocus to learn more\n\n arXiv-issued DOI via DataCite\n\n## Submission history\n\n From: Raunak Kumar [[view email](https:\u002F\u002Farxiv.org\u002Fshow-email\u002F62311445\u002F2607.06585)] \n\n**[v1]** Sun, 5 Jul 2026 06:04:20 UTC (2,899 KB)\n\n[](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.06585)Full-text links:\n## Access Paper:\n\n View a PDF 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