AI-Enabled IoT-Based Hydroponic Farming with Embedded Automation and Nutrient Prediction

1. Introduction and Background

Controlled-environment agriculture is moving quickly toward distributed sensing, edge computing, and cloud analytics to reduce manual supervision. However, in practice, many hydroponic platforms treat sensing, vision-based plant health screening, cloud monitoring, and actuator control as separate modules that happen to sit next to each other rather than work together. This separation leaves a gap between collecting real-time data and actually closing the loop on decisions. This work addresses this gap by integrating multi-sensor hydroponic monitoring, RGB image-based disease screening, nutrient-status prediction, actuator control, and Digital Twin visualization within a single operational platform.

Meeting the nutritional needs of a growing global population is hardly easy, and the challenges are not helped by climate change, dwindling freshwater resources, or the gradual loss of arable land [1,2]. In urban areas, where space is limited and supply chains are extended, traditional farming has difficulty meeting demand. The solution that has emerged as more and more viable is controlled-environment agriculture (CEA): growers can control temperature, humidity, light, and nutrients, and thus grow food reliably and efficiently, irrespective of the outside climate [3,4]. Hydroponics sits at the center of CEA. Growing plants without soil reduces water consumption significantly and often yields better per square meter than conventional planting. But the problem is that most hydroponic systems continue using manual monitoring and static control rules, causing the systems to be slow to change when problems start [5,6]. With advancements in recent developments in the fields of IoT, AI, and cloud computing, it is now possible to develop systems that monitor themselves, predict issues, and intervene without human supervision [7,8,9].

Sensors for pH, electrical conductivity (EC), total dissolved solids (TDS), ultrasonic sensors, temperature, and humidity are already commercially available. Their data can be used to feed into machine learning models, enabling early detection of nutrient imbalances, optimization of fertigation timings, and identification of environmental anomalies [10,11]. On the vision side, deep learning has enabled rapid and accurate automatic disease diagnosis of leaf images [12,13,14]. The Digital Twin adds to this by keeping a current virtual model of the physical system. The real environment is simulated, and operators can run trials on a virtual plant prior to implementation on their own crop [15,16,17]. All these developments at the individual level, however, do not make a system that integrates sensing, prediction, visualization, and actuation under a single roof commonplace [4,18]. This paper introduces Hydro Grow, a hydroponic farming system, which combines two different types of AI models, IoT sensors, and an Azure-supported Digital Twin in a functional cyber-physical system. The details of the specific contribution are given below.

• A dual-stream AI pipeline with computer vision and inference extracted from the sensors.

• MobileNetV2 for plant disease classification and a decision tree for nutrient deficiency prediction.

• Cloud-synchronized digital twin with Microsoft Azure Digital Twins for real-time monitoring.

• Closed-loop actuator control (pumps, dozers, ventilation) with all control based on sensor data.

• An inbuilt AI chatbot for handling client queries was integrated with a web-based monitoring dashboard.

Most early hydroponic systems were manually operated. Nutrient and pH levels had to be manually analyzed and adjusted, and early automated systems used mechanical timers and simple pumps. These instruments made the work easier but did not add any intelligence to the process, as they operated on a timetable regardless of the conditions in the reservoir. Microcontroller-based systems allow for simple feedback, irrigation, and nutrient timing. They are less reliable than timers but still fundamentally reactive; they might be able to obtain a signal when a threshold is crossed, but they would not be able to predict when a crossing is likely to occur or why it is occurring. This resulted in their being sufficient only for small, stable systems and unsuitable for more complex systems [19]. IoT has transformed the landscape by making it possible and affordable to continuously monitor multiple parameters [1,20]. Today, grain growers have far greater real-time access to information about what is happening with their distributed sensor networks. Thus, there is another big space—intelligent interpretation of that data, and that is where the technology lies. Most deployed systems are still rule-based (hard-coded) and have not yet learned models, which means that they cannot adapt to unknown environments or optimize their behavior over time [21,22].

The remainder of this paper is organized as follows: Section 2 reviews the relevant prior work. Section 3 covers system modeling and simulation. Section 4 discusses the implementation of hardware and software. Section 5 presents the evaluation of the model’s performance. Section 6 provides a comparison of Hydro Grow with other systems. Section 7 represents the results and discussion, the conclusion is provided in Section 8.

2. Related Work

IoT-based monitoring has become standard in commercial and research hydroponic systems, and monitoring using the Internet of Things (IoT) is becoming commonplace. Platforms based on ESP32 microcontroller electronics have been shown to provide well-defined advantages for nutrient management and decrease manual nutrient monitoring [9,23,24,25]. These systems have great functionality within their design specifications but come to a halt when the conditions change outside of the designer’s expectations [6,21]. Machine learning has been used in hydroponics primarily for predictive operations such as predicting water demand, nutrient uptake, and alerting if the operations fall out of the range [10,22,26]. Structured sensor data have been used to demonstrate a high level of accuracy using decision trees and other hybrid supervised methods [10,11]. The problem, however, is that most of these models are trained separately and then applied. They are not updated as the crop and environment change and do not provide feedback to actuation systems [27]. Plant health assessment using computer vision has come a long way. CNN architectures are well-suited to reliably classify diseases from leaf images, for example, MobileNet variants, ResNet, and other lightweight architectures, and transfer learning from ImageNet weights significantly reduces the amount of data required [14,28,29,30,31]. However, there are complications when using the vision system in the real world; lighting, leaf overlap, and other background clutter can all reduce accuracy in field conditions, which is why the use of sensor-based confirmation makes a good design choice. Table 1 summarizes the gaps identified in each of these research threads.

Table 1. Research gaps identified in the related literature.

The literature has considered digital twin applications in hydroponic and aquaponic systems, with some platforms moving towards complete robot integration, such as DigiHortiRobot [16,17,32,33]. It is still a challenge to achieve real-time synchronization between physical and virtual environments, and the communication overhead can be substantial. Nevertheless, the idea is intriguing: having a well-maintained twin system that allows the operator to test interventions in a virtual environment before deploying them in the actual system.

2.1. Smart and Automated Hydroponic Systems

Transitioning from manual scheduling to sensor-based control has been a gradual process. Most early systems used presets without feedback, and later started to implement feedback based on sensor data using ESP32 microcontrollers [19,24]. Despite this, many implementations rely on hard-coded thresholds and fall short in regions where the environmental conditions fall outside the threshold ranges for which they are calibrated [21,25]. Awal et al. [23] created a house-type hydroponic system for Bangladesh that measures various parameters in real-time without making any prediction, nor do they integrate it with a cloud.

2.2. AI and Machine Learning for Hydroponics

Nutrient concentration prediction and finding optimum growth conditions have been predicted with reasonable success using random forest, linear regression, and hybrid AI methods [11,26]. Rahman et al. [26] developed an IoT system that integrates crop recommendation and nutrient monitoring, demonstrating the potential of IoT systems when using real sensor data as input for ML. The models adopted in the vision part are MobileNet-family models and VGG variants, which have been proven to be well suited for edge deployment due to their small size and efficiency [14,28,30]. Labeled hydroponic datasets are small, and transfer learning from ImageNet can be used in these cases [29,31]. The common challenge in all this work is how to link these models to the actuation layer. Most systems produce a prediction and then call it a day and leave it up to the human to decide on the next action to take [22,27]. Recent agricultural vision research is moving beyond image-level classification toward fine-grained segmentation and multimodal reasoning; Yang et al. proposed PestScope, an exclusion-aware large multimodal model for fine-grained agricultural pest segmentation, showing that agricultural diagnosis benefits from models that can localize visually similar stress or pest patterns [34]. The present work instead adopts MobileNetV2 for lightweight image-level disease screening on the Raspberry Pi 4B, prioritizing edge deployability over pixel-level segmentation. Feature-fusion strategies have also shown value in other sensor-driven automation tasks: Wang et al. improved seam extraction for teachless welding robots using a multichannel feature-fusion network, demonstrating that combining complementary visual features can improve perception reliability under challenging imaging conditions [35]. This supports the design choice of combining visual plant-health screening with sensor-based nutrient inference rather than relying on a single sensing stream.

Taken together, the shortfall in the literature is not a lack of sensors or models. Recent hydroponic systems increasingly combine IoT monitoring with cloud dashboards; however, most still rely on threshold-based actuation or evaluate machine learning models separately from the control layer. What is missing is an experimental validation of a unified architecture in which sensing, edge inference, cloud synchronization, visualization, and actuator response are tested together as one closed-loop system, rather than as components verified in isolation. Hybrid CNN-Transformer architectures have also been applied to high-resolution agricultural remote-sensing tasks; for example, Xie et al. used a boundary-guided CNN-Transformer network for cropland parcel mapping, reflecting the wider movement toward hybrid visual architectures in agricultural image interpretation [36]. However, such models are computationally heavier than MobileNetV2 and less suitable for the Raspberry Pi-based edge deployment targeted in this work.

3. Materials and Methods

3.1. Conceptual Framework

In this system, the physical growing system employed is deep water culture (DWC), in which the root system hangs in the solution, and the solution is supplied with oxygen and nutrients [1,5]. Water moves and is oxygenated by an air pump, and stagnant water results in anaerobic conditions that adversely affect the roots and yield. DWC is a suitable application for an automated system because of the relatively low changes in the system chemistry, allowing the sensor to settle before the actuator reacts to the change in the water quality. It consists of five layers: real-time IoT monitoring, edge AI inference, cloud-based analytics, digital twin synchronization, and a human-supervised web interface [7,15,17]. These layers are loosely coupled, such that a drop to the cloud does not eliminate the use of local sensing or edge inference. Five sensors were used to measure the important environmental variables. The pH sensor determines the acidity of the nutrient solution, which directly influences mineral absorption [10,25]. The total dissolved mineral concentration was measured using an EC/TDS sensor. The DHT22 is a sensor that measures the temperature and air humidity around the plant canopy of a tree. The water level of the reservoir was monitored using an ultrasonic sensor. Images of the leaves were captured using a Raspberry Pi Camera Module V2 for visual disease screening [12,14].

3.2. Four-Layer System Architecture

The main architecture is a four-layer pyramid to separate sensing, edge control, cloud services, and user-facing applications, as presented in Figure 1. These concerns should be kept separate to allow the system to be debugged, updated, and scaled without redesign [7,8]. The sensing layer is a data-collection layer. Analog sensors are connected to the ADC circuits on the Raspberry Pi 4B, and digital sensors are connected through I2C or GPIO. No analysis is performed here.

Figure 1. Four-layered architecture of the proposed AI-enhanced intelligent hydroponic farming.

The control layer is installed on the Raspberry Pi 4B. It receives sensor data, checks against target ranges, and activates the relay-controlled actuators, which include the water pump, nutrient dispensers, and LED grow lights when deviations appear. The system includes the sensing layer (pH, EC, and ultrasonic imaging), control layer (logical controller, microcontroller, supervisory control, and AI controller), processing layer (cloud server, AI services, AI services control, and artificial intelligence unit), and user layer (mobile application). Microsoft Azure is used in the c