[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-413":3,"related-413":73},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":43,"search_phrases":66,"slug":69,"view_count":70,"doi":71,"paper":8,"created_at":72},413,"深度学习支持下地块尺度农田宜机化遥感评价方法","http:\u002F\u002Fwww.tcsae.org\u002Fcn\u002Farticle\u002Fdoi\u002F10.11975\u002Fj.issn.1002-6819.202510247","《农业工程学报》2026年第42卷第9期刊载华中师范大学等单位杨子恒、胡琼、吴尚岐、徐保东、蔡志文论文。DOI: 10.11975\u002Fj.issn.1002-6819.202510247。研究基于多源中、高分辨率遥感数据和深度学习语义分割算法,自动提取农田地块与道路等基础设施的空间分布信息,结合数字高程模型与水旱分布数据,从地块形状、规模、平整度以及道路通达度4个维度建立综合评价体系,以当阳市为研究区域。结果:语义分割模型农田地块与道路面积准确度分别达到0.92和0.79,边界匹配度0.85和0.86;研究区宜机化良好农田面积占比达67.3%,丘陵山区乡镇普遍面临道路通达度不足与地块平整度较差问题。",null,"**摘要:**\n\n农业机械化是推进农业现代化与保障国家粮食安全的关键支撑，而农田宜机化水平是决定农机作业效率的重要基础。然而，现有研究多集中于宜机化的必要性论证与成本效益分析，缺乏一套系统的精细化评价方法，难以科学性与有针对性的指导实施大区域农田宜机化改造工作。该研究基于多源中、高分辨率遥感数据和深度学习语义分割算法，自动提取农田地块与道路等基础设施的空间分布信息，为农田宜机化提供关键数据基础，同时结合数字高程模型与水旱分布数据，从地块形状、规模、平整度以及道路通达度4个维度建立综合评价体系，并以当阳市为研究区域，实现地块尺度农田宜机化水平精细化评估。结果表明：1）语义分割模型在地块提取与道路识别中表现良好，农田地块与道路的面积准确度分别达到0.92和0.79，边界匹配度分别达到0.85和0.86；共识别出农田地块46.8万个，道路总长度9 747.23 km；2）研究区农田宜机化水平空间分布差异显著，高宜机化区集中分布于东南部平原，在中部平原与低丘地带呈分散分布，西部丘陵区的宜机化水平较低；研究区宜机化分级结果显示，宜机化良好的农田面积占比达67.3%，乡镇尺度归因分析表明，除草埠湖镇外，地块形状与规模是各乡镇宜机化水平的共性制约因素，丘陵山区乡镇还普遍面临道路通达度不足与地块平整度较差的问题。该研究构建的评价方法能够有效、精细地表征地块尺度的农田宜机化水平，为大范围农田宜机化动态监测与差异化改造策略制定提供数据支持和决策依据。\n\n**Abstract:**\n\nThe adaptability level of farmland to agricultural machinery (i.e., the degree to which farmland facilitates agricultural machinery) can determine the efficiency and cost of mechanized operations in modern agriculture. However, existing research has focused mostly on the machinery adaptability or cost-benefit analysis after farmland consolidation. It is lacking in a systematic and refined evaluation of national food security. It is often required to implement the high adaptability of the large-scale farmland machinery. This study aims to automatically extract the spatial distribution of farmland parcels and roads using multi-source medium- and high-resolution remote sensing data and the deep learning semantic segmentation network HRNet (High-Resolution Network). A multi-task learning was employed to simultaneously predict the farmland, parcel boundaries, and roads. Parcel boundaries were also refined using conditional probability post-processing. Thereby, fine-scale data were provided to assess the adaptability level of farmland machinery. A 30 m digital elevation model (DEM) was combined with the paddy\u002Fdryland distribution data from a random forest classifier. An evaluation system was established from four dimensions: parcel shape, parcel size, surface evenness, and road accessibility. Seven indicators were obtained to determine the weight of each indicator. Criteria Importance Through Intercriteria Correlation (CRITIC) and the parcel-level Farmland Machinery Adaptability Index (FMAI) were subsequently calculated to validate in Dangyang City, Hubei Province. The results show that the HRNet semantic segmentation model performed well in both parcel and road extraction, with an area F1 score of 0.92 for farmland and 0.79 for roads, as well as an edge F1-score (F1-edge) of 0.85 for parcel boundaries and 0.86 for roads. A total of 468 000 farmland parcels and 9747.23 km of roads were identified in the study area. The spatial distribution of farmland machinery adaptability exhibited significant heterogeneity: High-adaptability areas (FMAI > 77.3) were concentrated in the southeastern Juzhang River alluvial plain in the central plains and low hilly areas, while the western hilly and mountainous regions generally shared the low adaptability levels. The grading results indicated that the area proportion of grades 1 and 2 (good adaptability) accounted for 67.3% of the total farmland area, but their parcel number proportion was only 38.6%, indicating the fragmented parcels generally had the lower adaptability grades. Township-scale attribution analysis revealed that the irregular parcel shape and small parcel size were common constraints to machinery adaptability over all townships, except for Caobuhu Town. The additional constraints were attributed to the insufficient road accessibility (road accessibility score 62-70) and low surface evenness (evenness score 78-85) in hilly and mountainous townships (e.g., Wangdian and Miaoqian). The evaluation can effectively and finely characterize the parcel-level farmland machinery adaptability and its limiting factors. The finding can provide data support and decision-making for dynamic monitoring of large-scale machinery adaptability, differentiated consolidation, and high-standard construction in farmland.\n\n[![Image 1](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fshy-202510247-1.jpg)](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fshy-202510247-1.jpg \"Figure1.Remote sensing imagery and samples of the study area\")\n\n**图 1** 研究区域遥感影像与样本\n\n**Figure 1.** Remote sensing imagery and samples of the study area\n\n[![Image 2](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fshy-202510247-2.jpg)](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fshy-202510247-2.jpg \"Figure2.Digital elevation model, slope and distribution of paddy and dryland\")\n\n**图 2** 数字高程模型、坡度与水田旱地分布数据\n\n**Figure 2.** Digital elevation model, slope and distribution of paddy and dryland\n\n[![Image 3](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fshy-202510247-3.jpg)](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fshy-202510247-3.jpg \"Figure3.Architecture of the HRNet\")\n\n**图 3** 高分辨率特征网络结构\n\n**Figure 3.** Architecture of the HRNet\n\n[![Image 4](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fshy-202510247-4.jpg)](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fshy-202510247-4.jpg \"Figure4.Extraction results of parcels and roads in the study area\")\n\n**图 4** 研究区地块与道路提取结果\n\n**Figure 4.** Extraction results of parcels and roads in the study area\n\n[![Image 5](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fshy-202510247-5.jpg)](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fshy-202510247-5.jpg \"Figure5.Spatial distribution of farmland machinery adaptability index in the study area\")\n\n**图 5** 研究区农田宜机化指数空间分布\n\n**Figure 5.** Spatial distribution of farmland machinery adaptability index in the study area\n\n[![Image 6](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fshy-202510247-6.jpg)](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fshy-202510247-6.jpg \"Figure6.Area distribution of different farmland machinery adaptability grades in various townships\")\n\n**图 6** 各乡镇不同农田宜机化等级的面积分布\n\n**Figure 6.** Area distribution of different farmland machinery adaptability grades in various townships\n\n[![Image 7](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fshy-202510247-7.jpg)](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fshy-202510247-7.jpg \"Figure7.Area-weighted mean values of farmland machinery adaptability indicators across townships\")\n\n**图 7** 各乡镇农田宜机化评价指标面积加权均值对比\n\n**Figure 7.** Area-weighted mean values of farmland machinery adaptability indicators across townships\n\n[![Image 8](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fshy-202510247-8.jpg)](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fshy-202510247-8.jpg \"Figure8.Changes in the mean values of farmland machinery adaptability indicators under different scale errors\")\n\n**图 8** 不同比例误差下的宜机化评价指标均值变化\n\n**Figure 8.** Changes in the mean values of farmland machinery adaptability indicators under different scale errors\n\n[![Image 9](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fshy-202510247-9.jpg)](http:\u002F\u002Fwww.tcsae.org\u002FfileNYGCXB\u002Fjournal\u002Farticle\u002Fnygcxb\u002F2026\u002F9\u002Fshy-202510247-9.jpg \"Figure9.Changes in the share of farmland parcels with different mechanization levels under different scale errors\")\n\n**图 9** 不同比例误差下不同宜机化等级的农田地块数量占比变化\n\n**Figure 9.** Changes in the share of farmland parcels with different mechanization levels under different scale errors\n\n**表 1** 精度评价指标\n\n**Table 1** Accuracy assessment metrics\n\n指标类别\n\nMetrics category 评价指标\n\nAssessment metrics 计算公式\n\nFormulation 适用对象\n\nObject 说明\n\nExplanation\n面积准确度\n\nArea accuracy 精确率Precision$ {\\mathrm{Precision}}=\\dfrac{{\\mathrm{TP}}}{{\\mathrm{TP+FP}}} $农田\n道路 TP、TN、FP和FN分别表示准确识别的目标像素数量、准确识别的非目标像素数量、目标错分的像素数量以及非目标错分的像元数量\n召回率Recall$ {\\mathrm{Recall}}=\\dfrac{{\\mathrm{TP}}}{{\\mathrm{TP+FN}}} $\n面积综合得分F1$ F1=\\dfrac{2\\text{×}{\\mathrm{Precision}}\\text{×}{\\mathrm{Recall}}}{{\\mathrm{Precision}}+{\\mathrm{Recall}}} $\n边界匹配度\n\nEdge accuracy 完整度Com$ \\mathrm{Com}=\\dfrac{L_{\\mathrm{pred}}\\cap L\\mathrm{_{truth}}}{L_{\\mathrm{truth}}} $地块边界\n道路 _L_ pred 与 _L_ truth 分别表示模型提取的线状地物和真实的线状地物，∩表示两者在彼此缓冲区内的重叠长度，本研究缓冲区大小设置为2.5 m\n精准度Corr$ \\mathrm{Corr}=\\dfrac{L_{\\mathrm{truth}}\\cap L_{\\mathrm{pred}}}{L_{\\mathrm{pred\\mathrm{ }}}} $\n边界综合得分F1-edge$ F1-\\text{edge}=\\dfrac{2\\text{×}\\mathrm{Com}\\text{×}\\mathrm{Corr}}{\\mathrm{Com}+\\mathrm{Corr}} $\n\n**表 2** 农田宜机化评价指标赋分标准\n\n**Table 2** Scoring standards of farmland machinery adaptability indicators\n\n一级指标\n\nPrimary indicators 二级指标\n\nSecondary indicators 赋分Score\n10 20 30 40 50 60 70 80 90 100\n地块形状\n\nParcel shape 地块长宽比-水田 PAR-P(1.0, 1.5](1.5, 2.0](2.0, 2.5](2.5, 3.0]>5.0(3.0, 5.0]\n地块长宽比-旱地 PAR-D(1.0, 1.5](1.5, 2.0](2.0, 2.5](2.5, 3.0](3.0, 10.0](10.0, 20.0]>20.0\n地块宽度 PW\u002Fm[0, 5](5, 10](10, 15](15, 20](20, 25]>25\n地块与最小包围盒面积比\n\nPMBAR\u003C0.1[0.1, 0.2)[0.2, 0.3)[0.3, 0.4)[0.4, 0.5)[0.5, 0.6)[0.6, 0.7)[0.7, 0.8)[0.8, 0.9)>0.9\n地块规模\n\nParcel size 面积-水田 Area-P\u002Fhm 2(0, 0.03](0.03, 0.06](0.06, 0.13](0.13, 0.20](0.20, 0.33]>0.33\n面积-旱地 Area-D\u002Fhm 2(0, 0.03](0.03, 0.06](0.06, 0.33](0.33,0.66](0.66, 1.33](1.33, 3.33]>3.33\n地块平整度\n\nSurface levelness 坡度 Slope\u002F(°)>25[25, 15)[15, 10)[10, 6)[6, 2)[2, 0]\n农田道路通达度\n\nFarmland road accessibility 地块与道路距离 RD\u002Fm>100[50, 100)[20, 50)[10, 20)[5, 10)\u003C5\n道路宽度 RW\u002Fm 0(0, 1.5](1.5, 2.0](2.0, 3.0](3.0, 4.0](4.0, 5.0](5.0, 6.0]>6.0\n\n**表 3** 模型精度评价\n\n**Table 3** Model accuracy evaluation\n\n指标\n\nMetrics 面积准确度Area accuracy 边界匹配度Edge accuracy\nPrecision Recall F1 Com Corr F1-edge\n农田 0.908 0.946 0.927---\n地块边界---0.897 0.828 0.859\n道路 0.811 0.775 0.792 0.848 0.878 0.862\n\n**表 4** 农田宜机化指标权重\n\n**Table 4** Weights for farmland machinery adaptability indicators\n\n指标\n\nIndicator 地块形状Parcel shape 地块规模\n\nParcel size 地块平整度\n\nSurface levelness 农田道路通达度\n\nFarmland road accessibility\n地块长宽比\n\nPAR 地块宽度\n\nPBW 地块最小包围盒面积比\n\nPMBAR 最近道路距离\n\nRD 道路宽度\n\nRW\n主观权重Objective weight 0.083 0.083 0.083 0.250 0.250 0.125 0.125\n客观权重Subjective weight 0.177 0.134 0.097 0.141 0.097 0.186 0.168\n综合权重Comprehensive weight 0.107 0.081 0.058 0.256 0.176 0.169 0.153\n\n**表 5** 研究区农田宜机化分级统计结果\n\n**Table 5** Grading statistics result of farmland machinery adaptability in the study area\n\n宜机化等级\n\nGrading 宜机化指数\n\nFMAI 数量统计\n\nNumber statistics 面积统计\n\nArea statistics 宜机化指标统计\n\nMachinery adaptability indicators statistics\n农田地块数\n\nNumber of \n\nparcels 数量占比\n\nPercentage of \n\ntotal number\u002F%农田面积\n\nFarmland area\u002Fhm2 面积占比\n\nPercentage of \n\ntotal number\u002F%地块形状均值\n\nMean of\n\nP-shape 地块规模均值\n\nMean of\n\nP-size 地块平整度均值\n\nMean of SL 农田道路通\n\n达度均值\n\nMean of FRA\n一级 77.3～100 73297 15.65 24869.00 42.40 76.37 74.98 94.06 85.82\n二级 67.3～77.3 107234 22.90 14581.13 24.86 68.43 56.32 85.65 81.03\n三级 57.3～67.3 108130 23.09 9448.59 16.11 64.92 44.87 84.66 61.54\n四级 47.1～57.3 103400 22.08 7495.62 12.78 64.58 43.70 81.61 35.75\n五级 20.7～47.1 76243 16.28 2257.48 3.85 57.30 25.67 74.53 24.70\n合计 468304 100 58651.82 100 66.20 48.82 84.04 58.11\n\n[1]陈印军, 易小燕, 方琳娜, 等. 中国耕地资源与粮食增产潜力分析[J]. 中国农业科学, 2016, 49(6): 1117-1131. doi: [10.3864\u002Fj.issn.0578-1752.2016.06.008](https:\u002F\u002Fdx.doi.org\u002F10.3864\u002Fj.issn.0578-1752.2016.06.008)\nCHEN Yinjun, YI Xiaoyan, FANG Linna, et al. Analysis of cultivated land and grain production potential in China[J]. Scientia Agricultura Sinica, 2016, 49(6): 1117-1131. (in Chinese with English abstract) doi: [10.3864\u002Fj.issn.0578-1752.2016.06.008](https:\u002F\u002Fdx.doi.org\u002F10.3864\u002Fj.issn.0578-1752.2016.06.008)\n[2]卢成会, 祝坤. 农村劳动力老龄化趋势问题研究[J\u002FOL]. 中国农业资源与区划, 1-11[2025-09-10]. https:\u002F\u002Flink.cnki.net\u002Furlid\u002F11.3513.S.20250924.1520.006. \nLU Chenghui, ZHU Kun. Research on the problem of the aging trend of rural labor force[J\u002FOL]. Chinese Journal of Agricultural Resources and Regional Planning,1-11[2025-09-10]. https:\u002F\u002Flink.cnki.net\u002Furlid\u002F11.3513.S.20250924.1520.006. (in Chinese with English abstract)\n[3]胡志全, 朱殿霄, 辛岭, 等. 基于产业要素年代差距分析的农业现代化水平国际比较研究[J]. 中国农业科学, 2018, 51(7): 1412-1420. \nHU Zhiquan, ZHU Dianxiao, XIN Ling, et al. Comparison study on the level of international agricultural modernization based on the method of generation gap of industry elements[J]. Scientia Agricultura Sinica, 2018, 51(7): 1412-1420. (in Chinese with English abstract)\n[4]张洪程, 胡雅杰, 戴其根, 等. 中国大田作物栽培学前沿与创新方向探讨[J]. 中国农业科学, 2022, 55(22): 4373-4382. doi: [10.3864\u002Fj.issn.0578-1752.2022.22.004](https:\u002F\u002Fdx.doi.org\u002F10.3864\u002Fj.issn.0578-1752.2022.22.004)\nZHANG Hongcheng, HU Yajie, DAI Qigen, et al. Discussions on frontiers and directions of scientific and technological innovation in China’s field crop cultivation[J]. Scientia Agricultura Sinica, 2022, 55(22): 4373-4382. (in Chinese with English abstract) doi: [10.3864\u002Fj.issn.0578-1752.2022.22.004](https:\u002F\u002Fdx.doi.org\u002F10.3864\u002Fj.issn.0578-1752.2022.22.004)\n[5]DENG O, RAN J, HUANG S, et al. Managing fragmented croplands for environmental and economic benefits in China[J]. Nature Food, 2024, 5(3): 230-240. doi: [10.1038\u002Fs43016-024-00938-7](https:\u002F\u002Fdx.doi.org\u002F10.1038\u002Fs43016-024-00938-7)\n[6]魏鹏, 余敦, 胡宜之, 等. 规划管控约束下耕地细碎化评价与整治分区[J]. 农业工程学报, 2024, 40(3): 283-293. \nWEI Peng, YU Dun, HU Yizhi, et al. Evaluating cultivated land fragmentation and remediation zoning under planning management[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2024, 40(3): 283-293. (in Chinese with English abstract)\n[7]邹利林, 梁一凡, 林程, 等. 基于“田块-农户”尺度的耕地细碎化评价及影响因素探测[J]. 农业工程学报, 2025, 41(22): 306-315. \nZOU Lilin, LIANG Yifan, LIN Cheng, et al. Multidimensional evaluation and driving factors detection of cultivated land fragmentation based on plot-household scale[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2025, 41(22): 306-315. (in Chinese with English abstract)\n[8]廖宜涛, 施彬彬, 王传奇, 等. 南方丘陵山地油菜和小麦播种机械化发展现状与趋势[J]. 农业工程学报, 2025, 41(1): 12-26. doi: [10.11975\u002Fj.issn.1002-6819.202406151](https:\u002F\u002Fdx.doi.org\u002F10.11975\u002Fj.issn.1002-6819.202406151)\nLIAO Yitao, SHI Binbin, WANG Chuanqi, et al. Development status and trend of mechanization of rapeseed and wheat sowing in hilly and mountainous region of southern China[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2025, 41(1): 12-26. (in Chinese with English abstract) doi: [10.11975\u002Fj.issn.1002-6819.202406151](https:\u002F\u002Fdx.doi.org\u002F10.11975\u002Fj.issn.1002-6819.202406151)\n[9]周应恒, 胡凌啸. 对我国农田基础设施建设的思考[J]. 华中农业大学学报(社会科学版), 2016(2): 23-29, 135. doi: [10.13300\u002Fj.cnki.hnwkxb.2016.02.004](https:\u002F\u002Fdx.doi.org\u002F10.13300\u002Fj.cnki.hnwkxb.2016.02.004)\nZHOU Yingheng, HU Lingxiao. Some considerations on farmland infrastructure construction[J]. Journal of Huazhong Agricultural University (Social Sciences Edition), 2016(2): 23-29, 135. (in Chinese with English abstract) doi: [10.13300\u002Fj.cnki.hnwkxb.2016.02.004](https:\u002F\u002Fdx.doi.org\u002F10.13300\u002Fj.cnki.hnwkxb.2016.02.004)\n[10]张宗毅, 李玉珠, 张萌, 等. 基于农田宜机化视角的农用柴油技术效率提升路径[J]. 农业工程学报, 2024, 40(19): 62-71. doi: [10.11975\u002Fj.issn.1002-6819.202404019](https:\u002F\u002Fdx.doi.org\u002F10.11975\u002Fj.issn.1002-6819.202404019)\nZHANG Zongyi, LI Yuzhu, ZHANG Meng, et al. Pathways for enhancing the technological efficiency of agricultural diesel from the perspective of the level of farmland suitability for agricultural machinery operations[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2024, 40(19): 62-71. (in Chinese with English abstract) doi: [10.11975\u002Fj.issn.1002-6819.202404019](https:\u002F\u002Fdx.doi.org\u002F10.11975\u002Fj.issn.1002-6819.202404019)\n[11]毕红杰. 基于AHP对吉林省粮食综合生产能力评价与分析[J]. 中国农机化学报, 2015, 36(6): 323-327. doi: [10.13733\u002Fj.jcam.issn.2095-5553.2015.06.076](https:\u002F\u002Fdx.doi.org\u002F10.13733\u002Fj.jcam.issn.2095-5553.2015.06.076)\nBI Hongjie. Evaluation and analysis of grain comprehensive production capacity in Jilin province based on AHP[J]. Journal of Chinese Agricultural Mechanization, 2015, 36(6): 323-327. (in Chinese with English abstract) doi: [10.13733\u002Fj.jcam.issn.2095-5553.2015.06.076](https:\u002F\u002Fdx.doi.org\u002F10.13733\u002Fj.jcam.issn.2095-5553.2015.06.076)\n[12]袁鹏, 张宗毅, 李洪波. 农田宜机化水平与农户农机购置行为——基于全国丘陵山区10省4905户农户的数据[J]. 农业技术经济, 2024(10): 91-105. \nYUAN Peng, ZHANG Zongyi, LI Hongbo. Mechanization suitability of farmland and farmers agricultural machinery acquisition behavior: Based on data from 4905 farming households in 10 provinces in the hilly mountainous regions of China[J]. Journal of Agrotechnical Economics, 2024(10): 91-105. (in Chinese with English abstract)\n[13]陈乐为, 张宗毅, 李洪波, 等. 改地降本: 农田宜机化视角下的农机化成本效率分析——基于空间计量模型的实证研究[J]. 自然资源学报, 2024, 39(9): 2188-2205. doi: [10.31497\u002Fzrzyxb.20240911](https:\u002F\u002Fdx.doi.org\u002F10.31497\u002Fzrzyxb.20240911)\nCHEN Lewei, ZHANG Zongyi, LI Hongbo, et al. Farmland transformation to reduce costs: An empirical analysis of the cost efficiency of agricultural mechanization from the perspective of the level of farmland suitability for agricultural machinery operations based on spatial econometric models[J]. Journal of Natural Resources, 2024, 39(9): 2188-2205. (in Chinese with English abstract) doi: [10.31497\u002Fzrzyxb.20240911](https:\u002F\u002Fdx.doi.org\u002F10.31497\u002Fzrzyxb.20240911)\n[14]牛坡, 夏飞, 尤显平, 等. 重庆市宜机化土地整治效益研究[J]. 农业工程, 2021, 11(8): 86-89. \nNIU Po, XIA Fei, YOU Xianping, et al. Benefits of land consolidation for suitable large-medium farm mechanization of Chongqing[J]. Agricultural Engineering, 2021, 11(8): 86-89. (in Chinese with English abstract)\n[15]陈梅, 曹中华. 丘陵山区宜机化土地整治项目综合效益评价研究——基于改进AHP—模糊综合评价法[J]. 中国农机化学报, 2019, 40(1): 145-151. doi: [10.13733\u002Fj.jcam.issn.2095-5553.2019.01.27](https:\u002F\u002Fdx.doi.org\u002F10.13733\u002Fj.jcam.issn.2095-5553.2019.01.27)\nCHEN Mei, CAO Zhonghua. Research on comprehensive benefits evaluation of land consolidation in hilly and mountainous areas based on method of improved AHP-Fuzzy comprehensive evaluation[J]. Journal of Chinese Agricultural Mechanization, 2019, 40(1): 145-151. (in Chinese with English abstract) doi: [10.13733\u002Fj.jcam.issn.2095-5553.2019.01.27](https:\u002F\u002Fdx.doi.org\u002F10.13733\u002Fj.jcam.issn.2095-5553.2019.01.27)\n[16]林建平, 黄坤, 邓爱珍, 等. 耦合耕地综合质量和空间连通性的基本农田划定研究[J]. 农业机械学报, 2024, 55(7): 221-231, 251. \nLIN Jianping, HUANG Kun, DENG Aizhen, et al. Basic farmland demarcation coupling comprehensive quality and spatial connectivity of cultivated land[J]. Transactions of the Chinese Society for Agricultural Machinery, 2024, 55(7): 221-231, 251. (in Chinese with English abstract)\n[17]吴尚岐, 胡琼, 蔡志文, 等. 基于多源遥感和多分支时空特征融合网络的高标准农田建设地块形态变化[J]. 农业工程学报, 2025, 41(21): 277-286. \nWU Shangqi, HU Qiong, CAI Zhiwen, et al. Morphological variation in well-facilitated farmland parcels after construction using multi-source remote sensing and dual branch spatiotemporal fusion network[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2025, 41(21): 277-286. (in Chinese with English abstract)\n[18]刘光盛, 赵乐松, 程迎轩, 等. 基于限制因子的粤北丘陵山区耕地宜机化整治分区[J]. 农业工程学报, 2021, 37(12): 262-270. doi: [10.11975\u002Fj.issn.1002-6819.2021.12.030](https:\u002F\u002Fdx.doi.org\u002F10.11975\u002Fj.issn.1002-6819.2021.12.030)\nLIU Guangsheng, ZHAO Lesong, CHENG Yingxuan, et al. Land consolidation zoning of Northern Guangdong for suitable mechanization transformation in hilly and mountainous areas based on limiting factors[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2021, 37(12): 262-270. (in Chinese with English abstract) doi: [10.11975\u002Fj.issn.1002-6819.2021.12.030](https:\u002F\u002Fdx.doi.org\u002F10.11975\u002Fj.issn.1002-6819.2021.12.030)\n[19]董光龙, 赵轩, 刘金花, 等. 基于耕地质量评价与空间集聚特征的基本农田划定研究[J]. 农业机械学报, 2020, 51(2): 133-142. doi: [10.6041\u002Fj.issn.1000-1298.2020.02.015](https:\u002F\u002Fdx.doi.org\u002F10.6041\u002Fj.issn.1000-1298.2020.02.015)\nDONG Guanglong, ZHAO Xuan, LIU Jinhua, et al. Prime cropland assignment based on cultivated land quality evaluation and spatial cluster pattern[J]. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(2): 133-142. (in Chinese with English abstract) doi: [10.6041\u002Fj.issn.1000-1298.2020.02.015](https:\u002F\u002Fdx.doi.org\u002F10.6041\u002Fj.issn.1000-1298.2020.02.015)\n[20]刘彦文, 刘成武, 何宗宜, 等. 基于像元尺度耕地质量局部空间自相关的基本农田划定[J]. 农业机械学报, 2019, 50(5): 260-268, 319. \nLIU Yanwen, LIU Chengwu, HE Zongyi, et al. Delineation of basic farmland based on local spatial autocorrelation analysis of cultivated land quality in pixel scale[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(5): 260-268, 319. (in Chin","农业工程学报 2026 42(9) 2026-08-09","2026-08-09T02:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,7,1,"核心期刊论文，提出基于深度学习的农田宜机化遥感评价方法，数据详实，对农业现代化有实际指导意义。",[24],{"name":10,"url":6},[26,27,28,29,30],"数字农业","深度学习","语义分割","农田宜机化","遥感评价",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":44,"locked":14,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":51},[49,55,61],{"code":32,"score":50,"supported":51,"title":52,"abstract":53,"tags":54},13,true,[27],[27],[27,28],{"code":39,"score":56,"supported":51,"title":57,"abstract":59,"tags":60},11,[58],"遥感",[58],[],{"code":62,"score":21,"supported":14,"title":63,"abstract":64,"tags":65},"I1",[],[],[26],[67,68],"农田宜机化 数字农业 深度学习 语义分割","农田宜机化 数字农业","农田宜机化数字农业深度学习语义分割-413",6,"10.11975\u002Fj.issn.1002-6819.202510247","2026-08-11T23:57:03.136894Z",{"total":70,"page":21,"page_size":70,"items":74},[75,144,218,300,384,453],{"id":76,"title":77,"url":78,"summary":79,"summary_zh":80,"content":8,"source_name":81,"source_url":78,"published_at":82,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":83,"score_detail":84,"sources":89,"tags":91,"primary_topic":94,"secondary_topics":95,"classification":97,"search_phrases":123,"slug":126,"view_count":87,"doi":127,"paper":128,"created_at":143},3618,"Multi-Target Remote Sensing Segmentation with Cross-Scale Attention for Urban-Ecological Intelligence","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijgem.vol.12.no1.2026.pg131.154","Remote sensing image segmentation is essential to extract valuable information from satellite and aerial images to achieve significant applications such as urban planning and ecological monitoring. Yet, it is hard to accurately segment diverse and complicated features because of the constraints of conventional approaches and the requirement to process variable object sizes and complicated boundaries. This research aims to address the above difficulties through the use and assessment of advanced deep learning models specifically UNet, SegNet, and TransU-Net in multi target semantic segmentation. These networks have been applied in single-object segmentation tasks with a focus on structures and roads, while the UNet architecture was additionally utilized for multi-object segmentation comprising buildings, woods, grasslands, water bodies, and cultivated land. Using appropriate remote sensing datasets, the accuracy of the models was thoroughly evaluated using common evaluation metrics, including pixel accuracy, Intersection over Union (IoU), and F1-score. The experimental outcomes illustrate the capabilities of these deep learning methods to provide accurate identification of essential features. Notably, the maximum attainable accuracy for single-object building segmentation was 96.85%, whereas the overall accuracy for multi-object segmentation with the UNet was 84.2%. The experimental results show these deep learning algorithms can effectively separate different targets from remote sensing imagery, thus making them fundamental tools for geospatial analysis and related fields.","遥感图像分割对于从卫星和航空图像中提取有价值信息，以实现城市规划和生态监测等重要应用至关重要。然而，由于传统方法的局限以及处理可变目标尺寸和复杂边界的需要，准确分割多样且复杂的地物十分困难。本研究旨在通过使用和评估先进的深度学习模型，特别是UNet、SegNet和TransU-Net在多目标语义分割中的应用，来解决上述难题。这些网络已被应用于单目标分割任务，重点关注建筑物和道路，而UNet架构还被额外用于多目标分割，包括建筑物、林地、草地、水体和耕地。利用适当的遥感数据集，通过常用评价指标对模型的精度进行了全面评估，包括像素精度、交并比（IoU）和F1分数。实验结果表明了这些深度学习方法在准确识别关键地物方面的能力。值得注意的是，单目标建筑物分割的最高可达精度为96.85%，而使用UNet进行多目标分割的总体精度为84.2%。实验结果表明，这些深度学习算法能够有效地从遥感图像中分离出不同目标，从而使其成为地理空间分析及相关领域的基础工具。","IIARD INTERNATIONAL JOURNAL OF GEOGRAPHY AND ENVIRONMENTAL MANAGEMENT","2026-09-25T00:00:00Z",63,{"impact":85,"substance":17,"depth":86,"authority":13,"freshness":87,"relevant":21,"comment":88},12,15,8,"将UNet、SegNet、TransU-Net用于多目标遥感语义分割，建筑单目标精度96.85%、多目标84.2%，方法常规但结论对农业与生态遥感监测有参考价值。",[90],{"name":81,"url":78},[92,27,28,58,93],"农业人工智能","生态监测",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[96],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":98},{"candidates":99,"agricultural_evidence":51},[100,105,111,118],{"code":39,"score":86,"supported":51,"title":101,"abstract":103,"tags":104},[102],"remote sensing",[58,102],[58],{"code":32,"score":106,"supported":51,"title":107,"abstract":108,"tags":110},9,[],[27,109],"deep learning",[92,27,28],{"code":112,"score":113,"supported":51,"title":114,"abstract":115,"tags":117},"I4",3,[],[116],"空间分析",[],{"code":119,"score":21,"supported":14,"title":120,"abstract":121,"tags":122},"H6",[],[],[93],[124,125],"UNet 遥感 多目标分割","TransU-Net 城市生态 遥感","UNet遥感多目标分割-3618","10.56201\u002Fijgem.vol.12.no1.2026.pg131.154",{"doi":127,"openalex_id":129,"authors":130,"venue":81,"cited_by_count":133,"oa_url":134,"card":135,"direction":141,"ingested_from":142},"W7214298981",[131],{"name":132,"orcid":8},"Chibueze Favour Aririguzo",0,"https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJGEM\u002FVOL. 12 NO. 1 2026\u002FMulti-Target Remote Sensing Segmentation 131-154.pdf",{"tldr":136,"method":137,"finding":138,"direction":139,"opportunity":140},"评估UNet、SegNet和TransU-Net在多目标遥感语义分割中的性能，用于城市与生态监测。","使用UNet、SegNet、TransU-Net模型，在遥感数据集上以像素精度、","单目标建筑分割最高精度96.85%，UNet多目标分割总体精度84.2%。","农业遥感与作物表型","可探索跨尺度注意力机制提升多目标分割精度，并迁移至耕地与作物精细分类。","智慧农业 \u002F 农业物联网","openalex","2026-09-27T23:30:28.366758Z",{"id":145,"title":146,"url":147,"summary":148,"summary_zh":149,"content":8,"source_name":150,"source_url":147,"published_at":151,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":152,"score_detail":153,"sources":157,"tags":159,"primary_topic":164,"secondary_topics":165,"classification":167,"search_phrases":190,"slug":193,"view_count":21,"doi":194,"paper":195,"created_at":217},5165,"Proximal Remote Sensing of Oil Palm Basal Stem Rot Using Deep Learning Vision Algorithms and Ground-based Images","https:\u002F\u002Fdoi.org\u002F10.22266\u002Fijies2026.1130.31","Basal Stem Rot (BSR), caused by Ganoderma boninense, threatens oil palm productivity in Southeast Asia.This study develops and benchmarks a low-cost remote sensing framework for BSR severity diagnosis from smartphone-captured RGB trunk images, evaluating two pathways for future field deployment.The first pairs YOLO detection with a Random Forest classifier: YOLO26n gave the best accuracy-to-cost trade-off among three nanovariants for localizing BSR symptoms (mushroom, rot, white-button), and the end-to-end YOLO26n+RF pipeline achieved 0.83 accuracy and macro-F1 of 0.79 across Healthy, Mild, Moderate, and Severe classes.Rot was consistently the hardest symptom to localize, owing to its diffuse, boundary-ambiguous appearance.The second pathway trained four image classifiers (EfficientNet-B0, MobileNetV2, Swin-Tiny, and ConvNeXt-V2-Tiny) directly on severity labels, with ConvNeXt-V2-Tiny performing best (0.83 accuracy, 0.83 macro-F1).Both pathways performed well at severity extremes but struggled with Moderate, reflecting visual overlap between stages.SHAP analysis identified rot-area ratio and fruiting-body count as top predictors.These offline results show accurate BSR diagnosis is potentially achievable from smartphone imagery, laying groundwork for future on-device deployment.","由灵芝（Ganoderma boninense）引起的基茎腐病（Basal Stem Rot, BSR）威胁着东南亚油棕的生产力。本研究开发并基准测试了一个低成本遥感框架，用于从智能手机拍摄的RGB树干图像中诊断基茎腐病的严重程度，并评估了未来田间部署的两条技术路径。第一条路径将YOLO检测与随机森林分类器相结合：在三种纳米变体中，YOLO26n在定位基茎腐病症状（蘑菇、腐烂、白钮）方面实现了最佳的精度-成本权衡，端到端的YOLO26n+RF流程在健康、轻度、中度和重度四个类别上达到了0.83的准确率和0.79的宏F1值。腐烂始终是最难定位的症状，原因在于其弥散且边界模糊的外观。第二条路径直接在严重程度标签上训练了四个图像分类器（EfficientNet-B0、MobileNetV2、Swin-Tiny和ConvNeXt-V2-Tiny），其中ConvNeXt-V2-Tiny表现最佳（准确率0.83，宏F1值0.83）。两条路径在严重程度极端类别上表现良好，但在中度类别上表现欠佳，反映出各阶段之间存在视觉重叠。SHAP分析确定腐烂面积比和子实体数量为最重要的预测因子。这些离线结果表明，利用智能手机图像有望实现准确的基茎腐病诊断，为未来设备端部署奠定了基础。","International journal of intelligent engineering and systems","2026-10-16T00:00:00Z",69,{"impact":85,"substance":154,"depth":155,"authority":85,"freshness":106,"relevant":21,"comment":156},20,16,"该研究利用智能手机图像与深度学习实现油棕基腐病严重度诊断，方法新颖、结论可靠，但属细分作物研究，产业影响有限，适合作为技术类精选。",[158],{"name":150,"url":147},[160,161,27,162,163],"智慧农业","农业遥感","油棕","病害诊断",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[166],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":168},{"candidates":169,"agricultural_evidence":51},[170,174,179,186],{"code":39,"score":86,"supported":51,"title":171,"abstract":172,"tags":173},[102],[58,102],[161],{"code":32,"score":85,"supported":51,"title":175,"abstract":176,"tags":178},[109],[177],"YOLO",[27],{"code":180,"score":70,"supported":51,"title":181,"abstract":182,"tags":185},"B5",[],[183,184],"蘑菇","mushroom",[],{"code":62,"score":21,"supported":14,"title":187,"abstract":188,"tags":189},[],[],[160],[191,192],"油棕 基腐病 遥感","Ganoderma boninense 智能手机 检测","油棕基腐病遥感-5165","10.22266\u002Fijies2026.1130.31",{"doi":194,"openalex_id":196,"authors":197,"venue":150,"cited_by_count":133,"oa_url":147,"card":212,"direction":139,"ingested_from":142},"W7222626135",[198,201,204,206,209],{"name":199,"orcid":200},"Wan Siu Hong","https:\u002F\u002Forcid.org\u002F0000-0002-8254-4962",{"name":202,"orcid":203},"Jonathan Yong Chung Ee","https:\u002F\u002Forcid.org\u002F0000-0003-4079-8005",{"name":205,"orcid":8},"Jin Yuan Chan",{"name":207,"orcid":208},"Mohamad Izzuddin Anuar","https:\u002F\u002Forcid.org\u002F0000-0002-4929-5175",{"name":210,"orcid":211},"Mahmud Iwan Solihin","https:\u002F\u002Forcid.org\u002F0000-0002-5293-7466",{"tldr":213,"method":214,"finding":215,"direction":139,"opportunity":216},"用手机拍摄油棕树干图像，结合深度学习检测与分类，实现基腐病严重度诊断。","YOLO26n+随机森林与四种图像分类器，基于手机RGB图像，SHAP解释。","两条路径准确率均约0.83，中度病级最难区分，腐区比例与子实体数为关键预测因子。","可探索轻量化模型在手机端离线部署，并扩充多地区、多品种数据以提升中度病级辨识。","2026-10-17T23:30:28.307953Z",{"id":219,"title":220,"url":221,"summary":222,"summary_zh":223,"content":8,"source_name":224,"source_url":221,"published_at":225,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":226,"score_detail":227,"sources":231,"tags":233,"primary_topic":236,"secondary_topics":237,"classification":245,"search_phrases":271,"slug":274,"view_count":133,"doi":275,"paper":276,"created_at":299},4898,"An Interpretable Deep Learning Framework for Spatial Prediction of Soil Salinity Using Multi-Source Remote Sensing and Environmental Data: A Case Study of Cangzhou, Hebei Province, China","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26206465","Soil salinity poses a significant challenge to agricultural productivity and sustainable land management in coastal and semi-arid regions. Soil salinity in coastal agricultural regions is controlled by complex interactions among surface conditions, groundwater processes, soil properties, and terrain characteristics. Accurate spatial prediction requires integrating remote sensing observations with environmental information that represents these processes. This study presents a multi-source deep learning framework that integrates Sentinel-2-derived spectral indices with environmental covariates for regional soil salinity spatial prediction under the environmental conditions of the 2024 sampling period. SHapley Additive exPlanations (SHAP) is employed to quantify the contribution of environmental variables within the prediction framework and improve model interpretability. A convolutional neural network (CNN) incorporating a squeeze-and-excitation (SE) attention mechanism is developed as a nonlinear feature representation framework for integrating heterogeneous predictors, while the Crested Porcupine Optimizer (CPO) is used to optimize model hyperparameters. The proposed framework achieved the highest predictive performance among the benchmark models, with an R2 of 0.785, an RMSE of 0.928, and an MAE of 0.720. Integrating environmental covariates with spectral features further improves the prediction performance, increasing the R2 from 0.701 to 0.785. The results indicate the potential of integrating multi-source environmental information with interpretable deep learning for spatial prediction of near-surface soil salinity under similar coastal agricultural conditions.","土壤盐渍化对沿海和半干旱地区的农业生产力和可持续土地管理构成了重大挑战。沿海农业区域的土壤盐渍化受地表条件、地下水过程、土壤属性和地形特征之间复杂相互作用的控制。准确的空间预测需要将遥感观测与表征这些过程的环境信息相结合。本研究提出了一种多源深度学习框架，该框架将Sentinel-2衍生的光谱指数与环境协变量相结合，用于2024年采样期环境条件下的区域土壤盐渍化空间预测。采用SHapley加法解释（SHAP）方法量化预测框架中环境变量的贡献并提高模型可解释性。开发了一种融合压缩-激励（SE）注意力机制的卷积神经网络（CNN）作为非线性特征表示框架，用于整合异质性预测因子，同时使用冠豪猪优化器（CPO）优化模型超参数。所提出的框架在基准模型中取得了最高的预测性能，R²为0.785，RMSE为0.928，MAE为0.720。将环境协变量与光谱特征相结合进一步提高了预测性能，R²从0.701提升至0.785。结果表明，在类似的沿海农业条件下，将多源环境信息与可解释深度学习相结合用于近地表土壤盐渍化空间预测具有潜力。","Sensors","2026-10-13T00:00:00Z",75,{"impact":155,"substance":228,"depth":229,"authority":50,"freshness":87,"relevant":21,"comment":230},21,17,"方法新颖、指标可靠，将多源遥感与可解释深度学习结合用于盐渍化空间预测，对沿海农业区土壤治理有参考价值。",[232],{"name":224,"url":221},[160,161,234,27,235],"可解释AI","土壤盐渍化",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[238,239],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"code":240,"slug":241,"name":242,"path":243,"parent_code":244},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":44,"locked":14,"version":45,"method":46,"evidence":246},{"candidates":247,"agricultural_evidence":51},[248,252,256,261,267],{"code":32,"score":155,"supported":51,"title":249,"abstract":250,"tags":251},[109],[27,109],[234,27],{"code":39,"score":86,"supported":51,"title":253,"abstract":254,"tags":255},[102],[58,102],[161],{"code":240,"score":19,"supported":51,"title":257,"abstract":258,"tags":260},[241],[259,241],"土壤",[],{"code":262,"score":113,"supported":51,"title":263,"abstract":264,"tags":266},"H7",[],[265],"干旱",[],{"code":62,"score":21,"supported":14,"title":268,"abstract":269,"tags":270},[],[],[160],[272,273],"沧州 土壤盐渍化 遥感","Sentinel-2 土壤盐分 预测","沧州土壤盐渍化遥感-4898","10.3390\u002Fs26206465",{"doi":275,"openalex_id":277,"authors":278,"venue":224,"cited_by_count":133,"oa_url":221,"card":294,"direction":139,"ingested_from":142},"W7221715654",[279,282,285,287,290,292],{"name":280,"orcid":281},"Mingyuan Ye","https:\u002F\u002Forcid.org\u002F0009-0001-6434-5505",{"name":283,"orcid":284},"Peng Chen","https:\u002F\u002Forcid.org\u002F0000-0002-2549-6765",{"name":286,"orcid":8},"Junchi Ma",{"name":288,"orcid":289},"Bingxia Liu","https:\u002F\u002Forcid.org\u002F0000-0001-7958-7112",{"name":291,"orcid":8},"Jiahao Qi",{"name":293,"orcid":8},"Yuan Jing",{"tldr":295,"method":296,"finding":297,"direction":139,"opportunity":298},"构建融合多源遥感与环境变量的可解释深度学习框架，预测沧州土壤盐分空间分布。","Sentinel-2光谱指数+环境协变量，CNN-SE注意力，CPO优化超参数，","环境协变量与光谱特征结合将R²从0.701提升至0.785，模型性能最优。","可迁移至其他滨海盐渍区，并探索时序动态预测与多深度土壤盐分协同反演。","2026-10-14T23:30:27.313838Z",{"id":301,"title":302,"url":303,"summary":304,"summary_zh":305,"content":8,"source_name":306,"source_url":303,"published_at":307,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":308,"score_detail":309,"sources":311,"tags":313,"primary_topic":316,"secondary_topics":317,"classification":319,"search_phrases":355,"slug":358,"view_count":359,"doi":360,"paper":361,"created_at":383},4079,"Pixel-level high-throughput estimation of crop photosynthetic phenotyping parameters using a multi-task deep learning framework","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.aiia.2026.08.014","Hyperspectral remote sensing holds promise for estimating crop physiological and biochemical traits, yet conventional approaches are often constrained by small sample sizes, limited accuracy in multi-trait retrieval, and poor robustness. To alleviate these challenges, we present an integrated framework that combines a pixel-wise dataset construction strategy with a multi-task deep learning architecture for estimating photosynthetic phenotyping parameters (PPPs)—a suite of traits encompassing gas exchange, pigment composition, and structural properties. Specifically, under controlled experimental conditions, we developed MTI-Net, a lightweight multi-task inversion network that simultaneously estimates multiple PPPs in rice and tomato with high precision ( R 2 > 0.90). Since physiological ground-truth measurements were obtained at the leaf level, the pixel-wise predictions should be interpreted as weakly supervised, model-derived spatial estimates or relative distributions rather than directly validated pixel-level physiological measurements. This methodological framework demonstrated stable performance across data partitions and random initializations, and maintained stable performance when applied to pixel-averaged spectra. A complementary differentiable band selection module (DRS-Net) further reduced spectral dimensionality to 40 informative bands while maintaining retrieval accuracy above 0.75, suggesting potential pathways for simplified sensor design. Importantly, the estimated PPPs revealed biologically interpretable patterns in tomato, including stage-dependent dynamics, irradiance-driven shifts, and canopy-layer differentiation—where middle leaves dominated early photosynthesis and upper leaves prevailed at later stages. Collectively, this study provides a methodological foundation for proximal hyperspectral phenotyping under specific environments. While further validation in diverse field conditions is required, the framework demonstrates potential for high-throughput multi-parameter estimation and provides a methodological basis for future cost-effective hyperspectral systems.","高光谱遥感在估算作物生理生化性状方面具有潜力，但传统方法常受限于样本量小、多性状反演精度有限以及鲁棒性差等问题。为缓解这些挑战，我们提出了一种集成框架，将像元级数据集构建策略与多任务深度学习架构相结合，用于估算光合表型参数（photosynthetic phenotyping parameters, PPPs）——一组涵盖气体交换、色素组成和结构特性的性状。具体而言，在受控实验条件下，我们开发了MTI-Net，一种轻量级多任务反演网络，可同时高精度估算水稻和番茄的多个PPPs（R² > 0.90）。由于生理真值测量是在叶片尺度获得的，像元级预测应被解释为弱监督的、模型导出的空间估计或相对分布，而非直接验证的像元级生理测量。该方法框架在数据划分和随机初始化下表现出稳定性能，并在应用于像元平均光谱时保持稳定性能。一个互补的可微分波段选择模块（DRS-Net）进一步将光谱维度降至40个信息波段，同时保持反演精度高于0.75，提示了简化传感器设计的潜在路径。重要的是，估算的PPPs在番茄中揭示了具有生物学可解释性的模式，包括阶段依赖性动态、辐照度驱动的变化以及冠层分层分化——其中中层叶片在早期光合作用中占主导，而上层叶片在后期阶段占优势。总体而言，本研究为特定环境下的近地高光谱表型分析提供了方法学基础。尽管需要在多样化田间条件下进一步验证，但该框架展示了高通量多参数估算的潜力，并为未来经济高效的高光谱系统提供了方法学依据。","Artificial Intelligence in Agriculture","2026-10-01T00:00:00Z",80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":87,"relevant":21,"comment":310},"方法新颖、结论可靠的高光谱作物表型多任务反演研究，对智慧农业遥感监测有参考价值。",[312],{"name":306,"url":303},[160,161,27,314,315],"高光谱","作物表型",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[318],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":320},{"candidates":321,"agricultural_evidence":51},[322,326,331,338,345,351],{"code":32,"score":86,"supported":51,"title":323,"abstract":324,"tags":325},[109],[27,109],[27],{"code":39,"score":85,"supported":51,"title":327,"abstract":328,"tags":330},[],[58,102,329],"hyperspectral",[315,161,314],{"code":332,"score":70,"supported":51,"title":333,"abstract":334,"tags":337},"A1",[],[335,336],"水稻","rice",[],{"code":339,"score":70,"supported":51,"title":340,"abstract":341,"tags":344},"B1",[],[342,343],"番茄","tomato",[],{"code":346,"score":113,"supported":51,"title":347,"abstract":348,"tags":350},"I7",[],[349],"methodological framework",[],{"code":62,"score":21,"supported":14,"title":352,"abstract":353,"tags":354},[],[],[160],[356,357],"MTI-Net 高光谱 作物表型","水稻 番茄 光合表型 高光谱","MTI-Net高光谱作物表型-4079",5,"10.1016\u002Fj.aiia.2026.08.014",{"doi":360,"openalex_id":362,"authors":363,"venue":306,"cited_by_count":133,"oa_url":303,"card":378,"direction":139,"ingested_from":142},"W7215067733",[364,366,368,371,374,376],{"name":365,"orcid":8},"Mengqi Zhang",{"name":367,"orcid":8},"Jiaren Zhou",{"name":369,"orcid":370},"Qingfeng Song","https:\u002F\u002Forcid.org\u002F0000-0003-4314-6897",{"name":372,"orcid":373},"Man Zhang","https:\u002F\u002Forcid.org\u002F0009-0007-4481-0514",{"name":375,"orcid":8},"Xinguang Zhu",{"name":377,"orcid":8},"Minjuan Wang",{"tldr":379,"method":380,"finding":381,"direction":139,"opportunity":382},"提出像素级多任务深度学习框架，从高光谱数据高通量估算作物光合表型参数。","构建像素级数据集，设计轻量多任务网络MTI-Net及可微波段选择模块DRS-Ne","MTI-Net对水稻和番茄多个光合参数估算R²>0.90，DRS-Net降至40波段仍保持精度>0.","可探索弱监督像素级预测的田间验证，以及低成本高光谱传感器与多任务模型的联合优化。","2026-10-03T23:30:24.577846Z",{"id":385,"title":386,"url":387,"summary":388,"summary_zh":389,"content":8,"source_name":390,"source_url":387,"published_at":391,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":392,"score_detail":393,"sources":396,"tags":398,"primary_topic":401,"secondary_topics":402,"classification":404,"search_phrases":427,"slug":430,"view_count":21,"doi":431,"paper":432,"created_at":452},2942,"Deep learning-driven multisource remote sensing image fusion: Advances, challenges, and future directions","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.engappai.2026.116319","Multisource remote sensing image fusion has become an important solution to a long-standing limitation in Earth observation: individual sensors rarely provide high spatial detail, rich spectral information, reliable structural sensitivity, and frequent temporal coverage at the same time. This review examines how deep learning and artificial intelligence are being used to integrate multispectral, hyperspectral, panchromatic, optical, and synthetic aperture radar imagery for more reliable interpretation of complex ground scenes. It provides a technical synthesis of convolutional neural networks, autoencoders, generative adversarial networks, transformer architectures, diffusion models, and hybrid model driven approaches, with attention to their fusion mechanisms, reconstruction behavior, computational demand, and suitability for operational use. Applications include land cover mapping, precision agriculture, environmental monitoring, urban analysis, disaster assessment, and defense related interpretation. Rather than treating each fusion task separately, this review connects sensor heterogeneity, spatial and spectral resolution trade offs, radiometric correction, geometric correction, registration, noise reduction, and fusion level design within a single framework. The analysis indicates that convolutional models remain effective for stable local detail recovery, adversarial learning can improve visual sharpness but may introduce spectral distortion, transformer models better capture long range spatial and spectral relationships, and diffusion models offer refined reconstruction at greater computational cost. The review further identifies open challenges involving misregistration, spectral bias, limited labeled data, weak generalization across sensors, high memory requirements, and limited interpretability. Future progress should prioritize sensor aware learning, self supervised training, uncertainty aware evaluation, lightweight deployment, and application oriented benchmarks to improve reliability in operational Earth observation.","多源遥感图像融合已成为解决地球观测领域一个长期局限的重要方案：单一传感器很少能够同时提供高空间细节、丰富光谱信息、可靠的结构敏感性以及频繁的时间覆盖。本文综述了如何利用深度学习和人工智能整合多光谱、高光谱、全色、光学和合成孔径雷达（synthetic aperture radar, SAR）影像，以更可靠地解译复杂地表场景。文章对卷积神经网络、自编码器、生成对抗网络、Transformer架构、扩散模型以及混合模型驱动方法进行了技术综合，重点关注其融合机制、重建行为、计算需求以及业务化适用性。应用领域包括土地覆盖制图、精准农业、环境监测、城市分析、灾害评估和国防相关解译。本文并非将每种融合任务分开处理，而是在一个统一框架内将传感器异质性、空间与光谱分辨率权衡、辐射校正、几何校正、配准、降噪和融合层级设计联系起来。分析表明，卷积模型在稳定的局部细节恢复方面仍然有效，对抗学习可以提升视觉锐度但可能引入光谱失真，Transformer模型能更好地捕捉长程空间与光谱关系，而扩散模型以更高的计算成本提供精细重建。本文进一步指出了涉及配准误差、光谱偏差、标注数据有限、跨传感器泛化能力弱、高内存需求以及可解释性有限等开放挑战。未来的进展应优先关注传感器感知学习、自监督训练、不确定性感知评估、轻量化部署以及面向应用的基准测试，以提高业务化地球观测的可靠性。","Engineering Applications of Artificial Intelligence","2026-09-18T00:00:00Z",82,{"impact":17,"substance":18,"depth":394,"authority":19,"freshness":106,"relevant":21,"comment":395},19,"发表于核心期刊的综述，系统梳理深度学习多源遥感融合的方法、应用与挑战，对农业遥感与精准农业有直接参考价值，时效性强，值得进入每日精选。",[397],{"name":390,"url":387},[92,27,399,58,400],"精准农业","多源数据融合",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[403],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":405},{"candidates":406,"agricultural_evidence":51},[407,412,417,422],{"code":32,"score":394,"supported":51,"title":408,"abstract":409,"tags":411},[109],[27,109,410],"artificial intelligence",[92,27],{"code":39,"score":17,"supported":51,"title":413,"abstract":414,"tags":416},[102],[58,102,415,329],"multispectral",[58],{"code":62,"score":20,"supported":51,"title":418,"abstract":419,"tags":421},[],[399,420],"precision agriculture",[399],{"code":423,"score":21,"supported":14,"title":424,"abstract":425,"tags":426},"I5",[],[],[400],[428,429],"多源遥感 图像融合 深度学习","农业人工智能 多源数据融合 深度学习 精准农业","多源遥感图像融合深度学习-2942","10.1016\u002Fj.engappai.2026.116319",{"doi":431,"openalex_id":433,"authors":434,"venue":390,"cited_by_count":133,"oa_url":387,"card":447,"direction":139,"ingested_from":142},"W7213544281",[435,438,441,444],{"name":436,"orcid":437},"Shahid Karim","https:\u002F\u002Forcid.org\u002F0000-0001-9986-5052",{"name":439,"orcid":440},"Akeel Qadir","https:\u002F\u002Forcid.org\u002F0000-0003-0358-6505",{"name":442,"orcid":443},"Asif Ali Laghari","https:\u002F\u002Forcid.org\u002F0000-0001-5831-5943",{"name":445,"orcid":446},"Irfana Bibi","https:\u002F\u002Forcid.org\u002F0000-0003-2794-504X",{"tldr":448,"method":449,"finding":450,"direction":139,"opportunity":451},"综述深度学习多源遥感图像融合方法、挑战与未来方向。","综述CNN、GAN、Transformer、扩散模型等融合机制与重建行为。","CNN擅局部细节，GAN易谱失真，Transformer长程关系强，扩散模型精度高但算力大。","面向农业的轻量、自监督、不确定性感知融合与基准数据集构建。","2026-09-19T23:30:32.767384Z",{"id":454,"title":455,"url":456,"summary":457,"summary_zh":458,"content":8,"source_name":459,"source_url":456,"published_at":460,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":461,"score_detail":462,"sources":465,"tags":467,"primary_topic":469,"secondary_topics":470,"classification":472,"search_phrases":488,"slug":491,"view_count":463,"doi":492,"paper":493,"created_at":505},1769,"Land Cover Classification using Hyperspectral Imagery: A Deep Learning Approach","https:\u002F\u002Fdoi.org\u002F10.17485\u002Fijst\u002Fv19i31.487","Objectives: This research aimed to perform proper land cover classification of the Krishnagiri and Dharmapuri districts using hyperspectral imagery. This research also attempted to determine the usefulness of deep learning models in detecting large land cover classes, such as quarry, barren, forest, built-up, and agricultural land, using hyperspectral data. Method: The analysis of the land cover patterns in the study area used hyperspectral images (HSI) taken between 2015 and 2022, which had 230 spectral bands. A total of 3000 hyperspectral images were processed, 2000 of which were utilized for training and 1000 for testing. The images were then preprocessed, segmented into superpixels using superpixel segmentation, and classified. Three deep learning models, a Convolutional Neural Network (CNN), Residual Neural Network 18 (ResNet-18), and Visual Geometry Group-16 (VGG-16), were used as classification models. Standard measures of accuracy, precision, recall, and F-score were used to evaluate the performance and effectiveness of the models. Findings: The results indicate that ResNet-18 outperformed the other models in determining land cover classes using hyperspectral images. The model has shown high classification levels among various types of land, with built-up land having an accuracy level of approximately 95.09, indicating a better ability to extract and classify features than CNN and VGG-16. Novelty: The novelty of this study lies in the combination of hyperspectral image processing with deep learning frameworks to classify regions based on land cover. The relative analysis of CNN, ResNet-18, and VGG-16 demonstrated the usefulness of residual learning networks in enhancing the classification accuracy in hyperspectral remote sensing scenarios. Keywords: Hyperspectral Image (HSI), Land Cover Classification, Deep Learning, Convolutional Neural Network (CNN), ResNet-18, VGG-16, Remote Sensing, Spectral–Spatial Analysis","目标：本研究旨在利用高光谱影像对克里希纳吉里区和达尔马普里区进行准确的土地覆盖分类，并尝试确定深度学习模型在利用高光谱数据检测大型土地覆盖类别（如采石场、荒地、森林、建成区和农业用地）方面的有效性。方法：研究区域的土地覆盖模式分析使用了2015年至2022年间获取的高光谱图像（HSI），共包含230个光谱波段。共处理了3000幅高光谱图像，其中2000幅用于训练，1000幅用于测试。图像经过预处理后，采用超像素分割方法将其分割为超像素，并进行分类。使用三种深度学习模型作为分类模型，即卷积神经网络（CNN）、残差神经网络18（ResNet-18）和视觉几何组-16（VGG-16）。采用准确率、精确率、召回率和F值等标准指标来评估模型的性能和有效性。结果：结果表明，ResNet-18在利用高光谱图像确定土地覆盖类别方面优于其他模型。该模型在各种土地类型中表现出较高的分类水平，其中建成区的准确率约为95.09，表明其在特征提取和分类能力上优于CNN和VGG-16。新颖性：本研究的创新之处在于将高光谱图像处理与深度学习框架相结合，基于土地覆盖对区域进行分类。CNN、ResNet-18和VGG-16的相对分析证明了残差学习网络在提高高光谱遥感场景分类准确性方面的有效性。关键词：高光谱图像（HSI）、土地覆盖分类、深度学习、卷积神经网络（CNN）、ResNet-18、VGG-16、遥感、光谱-空间分析","Indian Journal of Science and Technology","2026-09-05T00:00:00Z",58,{"impact":85,"substance":17,"depth":155,"authority":13,"freshness":463,"relevant":21,"comment":464},2,"研究比较了三种深度学习模型在高光谱影像土地分类中的表现，方法有参考价值，但时效性差，影响范围有限。",[466],{"name":459,"url":456},[92,27,58,468],"土地分类",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[471],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":473},{"candidates":474,"agricultural_evidence":51},[475,479,483],{"code":39,"score":17,"supported":51,"title":476,"abstract":477,"tags":478},[329],[58,102,329],[58],{"code":32,"score":155,"supported":51,"title":480,"abstract":481,"tags":482},[109],[27,109],[92,27],{"code":112,"score":70,"supported":51,"title":484,"abstract":485,"tags":487},[],[116,486],"spatial analysis",[],[489,490],"农业人工智能 土地分类 深度学习 遥感","农业人工智能 土地分类","农业人工智能土地分类深度学习遥感-1769","10.17485\u002Fijst\u002Fv19i31.487",{"doi":492,"openalex_id":494,"authors":495,"venue":459,"cited_by_count":133,"oa_url":456,"card":500,"direction":139,"ingested_from":142},"W7208825957",[496,498],{"name":497,"orcid":8},"P Nithya",{"name":499,"orcid":8},"P Sudhakar",{"tldr":501,"method":502,"finding":503,"direction":139,"opportunity":504},"利用高光谱影像和深度学习模型对印度两地区进行土地覆盖分类，比较CNN、ResNet-18和VGG-1","使用2015-2022年230波段高光谱影像，超像素分割后，用CNN、ResNe","ResNet-18表现最佳，建成区分类准确率约95.09%，优于CNN和VGG-16。","可探索将ResNet-18等深度学习模型应用于高光谱影像的作物精细分类，或结合多时相数据提升农业监测精度。","2026-09-06T23:30:27.341932Z"]