Research on Ecological Monitoring of Digital Agriculture Industry Driven by Remote Sensing-Smart Devices-AI Collaboration

Summary

This protocol visualizes a remote sensing-AI-IoT integrated workflow for real-time ecological monitoring and decision support in digital agriculture.

Abstract

Continuous ecological monitoring is required for digital agriculture, but traditional approaches usually rely on isolated data sources such as satellites, UAVs, or field sensors, which limit spatial coverage, temporal frequency, and real-time decision-making. A single protocol that combines multi-scale data and describes how to create an effective and scalable monitoring workflow is desperately needed. To provide a dependable and automated ecological monitoring system for digital agriculture, this research aims to provide a clear, sequential process for combining remote sensing, smart field-based equipment, and Artificial Intelligence (AI) techniques. Developing an integrated monitoring method that provides reliable, high-resolution ecological data remains possible by adhering to the protocol. Harmonized datasets, robust data streams, and automated analytical outputs appropriate for operational agricultural monitoring are produced by the integrated Long Short-Term Memory with Transformer and Graph Neural Network (LSTM/Transformer/GCN) technique. Experiments were carried out in the Huang-Huai-Hai Plain (China) over a full crop rotation cycle (June 2023-May 2024). Results showed that fused data improved overall integrity to 92.3 ± 2.1% (23.5% higher than single RS data), reducing RMSE of soil volumetric water content (to 1.78 ± 0.25%) and crop NDVI (to 0.04 ± 0.01) by over 50%. Investigators and practitioners are able to utilize the structured methodology to implement real-time ecological monitoring in a useful and flexible way. It facilitates effective, automated, and scalable digital farming applications by integrating remote sensing, advanced technology, and AI applications.

Introduction

Digitization of agriculture has become a necessity in the world to solve the twofold crises of food security and conservation of agricultural systems. As the global population is expected to grow to 9.7 billion by the year 2050, agricultural output will have to increase by a substantial margin, yet conventional intensive agriculture has already caused devastating ecological effects, including soil salinization, loss of biodiversity, and water pollution, which has created a dilemma between productivity and ecological wellbeing¹. Here, the combination of remote sensing (RS), smart devices, and artificial intelligence (AI) may be regarded as a disruptive technology to digital agricultural ecological monitoring, as it gathers the macro-scale of observation, micro-scale sensing, and intelligent analysis to close the gap between agricultural production and ecological protection. Together, RS provides macro-scale environmental observation through high-resolution satellite² and UAV multispectral imagery (0.1-0.5 m), IoT sensors³ deliver continuous field-level measurements such as soil moisture, EC, and pH at 10-50 cm depth along with 10 min weather updates, and AI models integrate these multisource inputs into coherent ecological insights for monitoring dynamic agricultural environments⁴. Despite these technological advancements, the synergistic potential of the three technologies has not been fully exploited, limiting their ability to support comprehensive industrial ecological monitoring in digital agriculture.

While remote sensing, smart devices, and AI have been applied in agricultural monitoring individually, critical gaps persist regarding their collaborative use for industrial ecological assessment -- directly aligning with the core focus of this study:

Gap 1: Production-living-ecological integration indicators are missing; current RS-IoT studies only monitor yield or soil moisture without linking rural livelihoods and habitat services⁵.

Gap 2: Circular-bioeconomy metrics are absent; the potential of recycling agricultural ash or polymer-based water retainers is not quantified, so ecological footprints are incomplete⁶.

Gap 3: Sub-field water-security early warning is lacking -- high-resolution imagery is rarely fused with ground aquifer or riparian-identifier data, hampering timely irrigation-deficit alerts in peri-urban areas⁷.

This study is motivated by the urgent need to address the above gaps and realize the full potential of RS-UAV-IoT-AI integrated system for digital agricultural industrial ecological monitoring. Drawing on prior advancements -- such as data-driven sustainable agriculture framework8 and multimodal RS research⁸ -- this study aims to:

This study designs a distributed collaborative architecture spanning perception, network, data, intelligence, and application layers to unify remote sensing for macro-scale observation, smart devices for micro-scale sensing, and AI for intelligent analysis. It further develops industrial-level ecological indicators that connect crop-level parameters with broader ecosystem health, overcoming the narrow scope of existing metrics. In addition, edge computing is integrated into smart device-AI collaboratiods to reduce system latency to below 500 ms, thereby enabling real-time ecological early warnings and satisfying the requirements of industrial-scale monitoring

By achieving these goals, this study seeks to provide a scalable, collaboration-driven solution for digital agricultural industrial ecological monitoring, reconciling food security with ecological sustainability and advancing digital agriculture from isolated technology application to integrated industrial ecosystem management.

RS technology provides an efficient means for large-scale agricultural ecological monitoring. With its macro perspective, satellite RS can acquire regional-scale information on vegetation, soil, and meteorology⁹. Sentinel-2 and Landsat satellites are widely used for vegetation monitoring. Owing to its high spatial resolution and short revisit cycle, Sentinel-2 is particularly suitable for capturing vegetation dynamics and is commonly used to derive NDVI for assessing vegetation condition¹⁰. Landsat-9 Landsat- with a medium resolution (30 m in visible/near-infrared bands and 100 m in thermal infrared bands) and a 16-day revisit period is used in research, including land surface temperature measurements. Fine-scale crop health monitoring, high-resolution commercial satellites such as PlanetScope, with a spatial resolution of approximately 3 m and daily revisit capability, can be employed to monitor small agricultural plots ranging from 0.1 to 1 ha. This compensates for the limitations of traditional medium-resolution satellites, such as Sentinel-2 and Landsat, which may be less effective in capturing crop heterogeneity at such fine spatial scales.

IoT devices enable real-time and sophisticated monitoring of farmland environments. Soil sensors continuously measure key parameters such as moisture, electrical conductivity, and pH, providing a foundation for precision irrigation and fertilization¹¹. Weather stations provide essential meteorological data for climate-adaptive crop management, while ecological sensors such as acoustic sensors and RGB cameras broaden agricultural ecological monitoring¹². UAVs equipped with multispectral cameras and thermal infrared sensors can conduct high-frequency inspections of farmlands, compensating for the blind spots of satellite RS in monitoring small-scale ecological anomalies. It was particularly suitable for early warning of pests and diseases and crop health assessment¹³. Edge computing plays a key role in localizing data processing. By performing real-time data processing on edge devices close to data sources, it reduces data transmission latency, improves system response speed, and meets the real-time monitoring needs of agriculture¹⁴.

AI is increasingly being applied in the agricultural field, especially in processing multi-source and complex agricultural data. Machine learning algorithms, such as Long Short-Term Memory (LSTM) networks, can effectively analyze time-series data and are used for predicting crop growth trends and the probability of pest and disease outbreaks¹⁵. Convolutional Neural Networks (CNNs), the networks that are best at extracting the spatial information of the images, are good at activities like crop pest and disease detection and crop classification¹⁶. Transformer models have demonstrated benefits in the ability to combine multi-modal data to accomplish tasks such as soil moisture inversion¹⁷. These AI solutions have good data-driven analysis functions used in Agricultural Decision Support Systems (DSS) that assist farmers to make more scientific decisions related to irrigation, fertilization, and control of pests and diseases.

Multi-source data monitoring has acute problems associated with data non-homogeneity and structural fragmentation. Integration of RS and GIS data on groundwater vulnerability mapping in arid areas involves complicated calibration processes in order to align the various data representation and semantic differences¹⁸. But technological inventions are offering new ways to overcome these obstacles. Agricultural intelligence models powered by AI have proven to be able to automate the multi-source data fusion processes greatly increasing efficiency in decisions in precision farming¹⁹. The example of international cooperative projects, including the cooperation between CIAT and Japan, shows that the combination of historical field data, RS, and IoT sensors can be used to manage agroecosystems in a sustainable way. The further improvements in computing technologies are also connecting multi-source monitoring to sustainable development goals directly, which allows a more accurate allocation of resources and minimization of adverse impacts on the environment in agricultural systems²⁰. These innovations imply that breaking the bonds of integration may open transformational potential of holistic management of agricultural ecosystems.

Despite the major advancements in the use of RS, smart devices, and AI in ecological monitoring in agriculture, various research gaps remain. At the moment, a coherent and consistent framework to combine the RS, IoT, and AI technologies to ensure complete collaboration between all processes of data collection, transmission, and processing to the decision support is lacking²¹. The available literature tends to concentrate on the optimization of a particular technology or an element of the process, and does not pay full attention to the integration and coordination of the whole monitoring system. In addition, regarding ecological-level monitoring indicators, current research mostly focuses on local indicators such as crop growth and soil conditions, and lacks a comprehensive indicator system that fully reflects the overall health status, stability, and sustainability of agricultural ecosystems²². This limits the ability to evaluate and predict the long-term dynamic changes of agricultural ecosystems, which is not conducive to formulating scientific agricultural ecological management strategies. This study aims to fill these gaps by constructing a system-level "RS-smart devices-AI" collaborative monitoring framework and developing a comprehensive monitoring indicator system suitable for agricultural ecosystems.

Existing systems like UAV-based multi-sensor fusion models still suffer from scale-mismatch and spatial-resolution bias because fine crop structures cannot be fully captured when moderate-resolution imagery is fused with high-resolution UAV data²³. Although the RS-GIS-AI soil-nutrient mapping system achieves good macronutrient classification, real-world deployment is limited by its high data requirements and spectral unmixing difficulties, particularly over heterogeneous terrain²⁴. While the IoT-UAV irrigation control system shows real-time sensor feedback for water management, its limited coverage and reliance on connectivity make it challenging to scale in remote or agriculturally underdeveloped areas²⁵.

Existing research lacks integrated "production-living-ecological" indicators, as current IoT-ML and UAV-IoT systems focus on isolated variables and irrigation tasks²⁶, and standardized protocols for satellite-UAV-IoT data fusion are still missing²⁷. To address these gaps, an RS-UAV-IoT-AI integrated system is proposed for multiscale and continuous ecological monitoring.

The research is best suited to open-field agricultural systems with moderate-to-high sensor accessibility and consistent connectivity. Its performance may be limited in densely forested areas, regions with consistent cloud coverage, or areas without 5G/LoRaWAN infrastructure. The workflow requires multi-season RS data (Sentinel-2/Landsat/PlanetScope), plot-level IoT measurements at 10-50 cm soil depth, and a minimum of 1-2 hours of UAV flight time per sampling cycle.

Protocol

All field activities related to sensor installation, UAV flights, and environmental data collecting were carried out in accordance with local regulations governing UAV operations and environmental monitoring, and with the policies of the responsible institutional authority. Figure 1 illustrates the workflow, including satellite data acquisition, UAV operation, IoT sensor deployment, data preprocessing, data fusion, AI model implementation, verification and quality control, safety notes, and data handling and storage.

System architecture design
To achieve "space-ground-intelligence" collaborative ecological monitoring of digital agriculture, this study designs a five-layer distributed system architecture. Through multi-source data collection at the perception layer, heterogeneous transmission at the network layer, fusion processing at the data layer, AI analysis at the intelligent layer, and decision output at the application layer, a full-chain, high-real-time ecological monitoring system is constructed. Its core functions and technical paths are as follows:

Perception layer: Multi-dimensional data acquisition network
UAV-based multispectral or RGB imagery was obtained using calibrated aerial platforms in accordance with manufacturer requirements. Before each flight, the batteries were fully charged, the camera payload was stabilized and calibrated with a reference panel, and the GNSS lock was verified. Flight missions were planned for altitudes ranging from 50 to 120 m, with 70% forward and 60% side overlap to assure adequate coverage and correct orthomosaic reconstruction. All flights were carried out in safe weather circumstances, avoiding rain and severe winds. Following recovery, all images were moved to a secure storage system and catalogued by date and location. As the "data source entrance" of the architecture, the perception layer adopts a "space-air-ground" three-level observation network to realize the three-dimensional collection of agricultural ecological elements:

Space-based observation: Sentinel-2 and Landsat-9 offer regional data on vegetation cover and land surface temperature, while PlanetScope enhances fine monitoring capabilities at plot-level resolution. This combination addresses ecological assessment needs across various spatial and temporal scales.

Air-based observation: UAVs with multispectral cameras and thermal infrared sensors perform high-frequency inspections at altitudes of 50-100 m, addressing observation gaps in satellite data for small-scale ecological anomalies.

Ground-based observation: Three types of IoT terminals are deployed: i) Soil sensors (measurement depth 10-50 cm, monitoring soil moisture content, electrical conductivity, pH, sampling frequency 1 time/h); ii) Weather stations (collecting temperature, humidity, precipitation, photosynthetically active radiation, 1 time/10 min); iii) Ecological sensors (acoustic sensors recording insect population sound signals, RGB cameras capturing crop morphology, 1 frame/30 min), enabling continuous monitoring of micro-scale ecological elements.

Network layer: Low-power and high-reliability transmission link
The network layer employs a hybrid transmission scheme of "5G+LoRa+edge gateway" to improve wide-area coverage and reduce power consumption in agriculture. It uses 5G slicing for low-latency data and LoRaWAN for low-power sensor data, achieving ranges of 1-5 km with a battery life exceeding 6 months. Edge gateways facilitate protocol conversion and data preprocessing, enhancing transmission efficiency by approximately 40% by reducing invalid data sent to the cloud. Equation (1) is the gateway data compression rate model:

    (1)

Where, D_in is the original data volume, D_out is the compressed data volume, and α is the redundant data coefficient (0.3-0.5). is the data integrity requirement coefficient (r ≥ 0.95 in ecological monitoring to ensure data reliability).

Data layer: Multi-source heterogeneous data fusion engine
Aiming at the heterogeneity of "RS-IoT-meteorology-socio-economic" multi-source data (Table 1), the data layer adopts a two-step "spatiotemporal alignment-feature fusion" process:

Spatiotemporal alignment: Using UTC timestamps as the benchmark, linear interpolation is used to supplement missing sensor data (when missing rate<5%, RMSE<0.05). RS images and ground-based data are spatially matched based on geographic coordinates (WGS84 coordinate system) with registration error < 1 m.

Feature fusion: A multi-modal data fusion model is built to map RS spectral features, IoT physical features, meteorological data, and socio-economic data to a unified feature space. The fusion formula is shown in equation (2),

   (2)

Where, w₁, w₂, w₃, w₄ are feature weights (calculated via random forest feature importance, satisfying Σw_i = 1).

Table 1: Characteristics of multi-source data in the data layer. It generalizes the key properties of all the sources of perception-layer, such as RS imagery, UAV observations, and IoT sensors. It enumerates the spatial resolution, sampling frequency and sensor types. Please click here to download this Table.

Intelligent layer and application layer
The intelligent layer utilizes a hybrid AI model stack (CNN, LSTM, Transformer, GNN) to analyze fused data, where CNN extracts spatial features, LSTM models temporal dynamics, Transformer integrates RS and IoT data, and GNN assesses biodiversity through ecological networks. The application layer provides three primary services: ecological index monitoring, real-time early warning, and a decision support system for optimizing irrigation and fertilization.

Data collection and preprocessing
To ensure reliable multi-source data for AI-driven ecological monitoring, this section standardizes data collection protocols, preprocessing workflows, and quality control measures, providing detailed specifications for data sources and technical operations.

Multi-source data collection scheme
Data collection adopts a "macro-micro integration" strategy, combining RS data (regional scale) and ground-based data (plot/micro scale) to cover agricultural ecological elements comprehensively:

Remote sensing data acquisition
Three satellite datasets are selected to balance spatial-temporal resolution needs (Table 2). Sentinel-2 (ESA) provides atmospherically corrected Level-2A products with a spatial resolution between 10-60 m across 13 spectral bands (490-2190 nm) and a 5-day revisit cycle, with key bands for NDVI and soil moisture estimation. Landsat-9 (USGS) offers OLI/TIRS sensors with 30 m resolution for visible/NIR/SWIR and 100 m for thermal infrared, featuring a 16-day revisit cycle for land surface temperature retrieval. Planet Scope (Planet Labs) delivers 3 m resolution RGB and 4-band multispectral data with daily revisit, ideal for monitoring small farmland plots and validating crop stress signals from Sentinel-2 and Landsat-9.

Ground-based data acquisition
Soil and environmental IoT sensors were deployed in realistic field areas and placed at depths ranging from 10 to 30 cm, depending on the target variable. Each sensor node was assigned a unique device identity and linked to the communication gateway via LoRaWAN or 5G modules. The sampling intervals were chosen at 15 and 60 min. Sensors were allowed to equilibrate for 10-20 min after installation before data collection started, ensuring consistent baseline readings.

In the Huang-Huai-Hai Plain experimental area (33°N-38°N, 114°E-120°E, winter wheat-summer maize rotation), three types of IoT terminals are deployed. i) Soil sensors: 50 × 50 m grid layout, measuring soil volumetric water content (θ, 0-50%), electrical conductivity (EC, 0-10 dS/m), and pH (4-10) at 10/30/50 cm depths; data logged hourly and transmitted via LoRaWAN. ii) Meteorological stations: 15 automatic stations recording air temperature (-40-60 °C), relative humidity (0-100%), precipitation (0-999 mm/24 h), and photosynthetically active radiation (PAR, 0-3000 µmol/m²/s) every 10 min. iii) Ecological sensors: Acoustic sensors (20-20,000 Hz) for insect biodiversity assessment, and 1920 × 1080 resolution RGB cameras (2 m mounting height) capturing crop canopy images every 30 min.

Core preprocessing workflow
Satellite, UAV, and IoT datasets underwent standardized preprocessing to ensure interoperability. Remote-sensing imagery was subjected to cloud masking, geometric correction, and radiometric normalization. IoT datasets were cleaned using statistical filtering procedures, including Z-score-based removal of extreme values and interpolation of missing entries using linear or spline methods. All spatial datasets were resampled to a unified spatial resolution, and temporal synchronization was performed to limit the maximum timestamp mismatch between data streams to one hour. Preprocessing follows four sequential steps to resolve data noise, heterogeneity, and incompleteness, ensuring consistency for AI modeling:

Step 1: Data Cleaning. i) Sensor data: Outliers are identified via Z-score method (Z > 3) and replaced with 7-day moving average of adjacent valid data. ii) RS data²⁸: Cloud and cloud shadow masking uses QA bands (Sentinel-2 QA60, Landsat-9 QA_PIXEL) to ensure < 10% cloud cover in the study area.

Step 2: Spatiotemporal Alignment. Temporal synchronization: All data are unified to UTC time. Missing sensor data (missing rate < 5%) are filled via linear interpolation (equation 3).

    (3)

Where, t_i=target time, t_i-1/ti₊₁=adjacent valid timestamps, x_i-1/x_i+1=corresponding values.

Spatial matching: RS images are resampled to 10 m resolution (Sentinel-2 base) via bilinear interpolation; ground sensor data are geocoded to WGS84 and matched to RS pixels via nearest-neighbor interpolation (registration error < 1 m).

Step 3: Missing Value Handling. For RS data with 10-30% cloud cover, inverse distance weighting (IDW) interpolation fills gaps (equation 4).

   (4)

Where, x_p=target pixel value, x_j=adjacent valid pixel value, d_j=distance between p and j, n=number of adjacent pixels (n=10).

Step 4: Feature Normalization. Min-max normalization eliminates scale differences, scaling all features to [0,1] for AI input (Equation 5).

    (5)

Data Fusion: Spatial alignment, temporal matching, and numerical normalizing processes were used to combine harmonised datasets into a single analytical structure. To ensure cross-modal comparability, all variables were rescaled with either min-max normalization or z-standardization. Prior to fusion, the spatial congruence of the remote-sensing and IoT layers was tested by evaluating control point alignment and ensuring consistent georeferencing.

Data quality control and validation
Quality control methods were included throughout the workflow to ensure data reliability. Sensor connectivity was confirmed using gateway logs, UAV calibration was checked before each flight, and remote-sensing outputs were evaluated to ensure the presence of entire and properly ordered spectral bands. Following fusion, the spatial and temporal alignment of information was evaluated by GIS visualization. AI-generated outputs were evaluated for plausibility and ecological coherence. To ensure data usability, two quality control measures are implemented:

Quantitative validation: In the case of RS data, the Sentinel-2 NDVI is cross-tabulated with the PlanetScope (3 m resolution) in clear regions, and the R2 value must be greater than 0.85. In the case of sensor data, soil moisture data are cross-validated with manual sampling (gravimetric method) where RMSE < 2%.

Process monitoring: A real-time data quality dashboard is used to monitor the important metrics (outlier rate below 3, miss rate less than 5, spatial registration error less than 1 m) to indicate anomalous data transmission or sensor errors in order to perform timely maintenance.

Safety Notes: All UAV operations were conducted with necessary safety procedures, such as preserving line-of-sight, avoiding populated areas, and following manufacturer battery management requirements. Protective equipment was used during sensor installation to prevent injuries, and electronic components were protected from moisture exposure.

Data Handling and Storage: All raw and processed datasets were stored in hierarchical folders based on capture date and location. Backup copies were stored on secure external storage or cloud systems, and metadata records for all sensor deployments, UAV flights, and software configurations were saved to ensure complete repeatability.

AI Model design
AI-based analysis was carried out in Python environments equipped with TensorFlow or PyTorch. Input data was divided into training, validation, and testing subsets (70:15:15 split) and prepared as model-ready tensors. Model hyperparameters such as learning rate, batch size, and number of epochs were chosen using traditional optimization techniques. Training was monitored to ensure consistent convergence, and the final results were utilized to generate ecological indicators such as soil moisture estimates, vegetation metrics, and biodiversity signatures.

To manage the main ecological monitoring activities in digital agriculture (crop health, soil moisture, biodiversity) and improve the interpretability of the models, this section develops a task-specific hybrid AI model structure, incorporating LSTM, Transformer, GNN, and interpretable algorithms (Table 3). All models are adapted to features of multi-source data (time series, multi-modal, network-structured) to provide precision and feasibility of monitoring.

LSTM Model for crop health monitoring
Crop health is evaluated via dynamic changes in NDVI (Normalized Difference Vegetation Index), a key indicator of vegetation vitality. A time-series LSTM model is adopted to capture long-term temporal dependencies in NDVI data:

Model architecture
The model consists of three layers: i) Input layer: 30-day NDVI time series (from Sentinel-2/PlanetScope) and auxiliary meteorological features (daily temperature, precipitation); ii) Hidden layer: 2 LSTM units (64 neurons each) with dropout (rate = 0.2) to prevent overfitting; iii)Output layer: Dense layer predicting 7-day future NDVI values, with ReLU activation for non-negative outputs. Figure 2 depicts an LSTM.

Training and inference
Data input: NDVI time series are resampled to a daily frequency via linear interpolation, forming 30-step input sequences (Equation 6).

    (6)

Where, =sample size, 2=NDVI+meteorological features.

Loss function: Mean Absolute Error (MAE) minimizes prediction deviations (equation 7):

    (7)

Where, y_i=actual NDVI, =predicted NDVI, =sample number. Predicted NDVI is mapped to 3 health grades: Healthy (NDVI>0.7), Stressed (0.3

Health classification: Predicted NDVI is mapped to 3 health grades: Healthy (NDVI>0.7), Stressed (0.3

Transformer regression model for soil moisture inversion
Soil moisture inversion requires fusing spatial features (RS) and temporal features (IoT sensors). A Transformer model with multi-head attention is designed to integrate heterogeneous data:

Model architecture
Encoder module: Two parallel branches process RS and IoT data (Figure 3): i) RS branch: Convolutional layer (3 × 3 kernel) extracts spatial features from Sentinel-2 Band 10 (SWIR) and Landsat-9 thermal images. ii) IoT branch: Dense layer encodes hourly soil moisture, EC, and pH time series into 64-dim temporal features. iii) Multi-head attention layer: 4 attention heads fuse spatial-temporal features, assigning weights to critical inputs. iv) Decoder module: Dense layers (128→64→1 neurons) output soil volumetric water content (θ, 0-50%). The attention score calculates the correlation between RS (S) and IoT (T) features (equation 8):

   (8)

Where, Q=S, K=T, V=T, d_k=feature dimension (64).

GNN ecological network model for biodiversity assessment
Biodiversity (insect populations) is assessed via acoustic and image data, modeled as an ecological network (nodes=species, edges=interaction intensity). A GNN model captures network structure: (1) Node embedding: Acoustic features (MFCCs from insect sounds) and image features (CNN-extracted crop canopy texture) are concatenated into 128-dim node vectors (each node represents a potential insect species). (2) GCN layer: Graph Convolutional Network propagates node information via adjacency matrix (equation 9),

   (9)

Where, X=node features, (identity matrix), (normalized adjacency matrix), =weight matrix: (3) Output layer: Sigmoid activation predicts species presence (1=present, 0=absent), with biodiversity index calculated as the number of present species per hectare (Figure 4).

Model interpretability enhancement
To address AI "black box" issues, three interpretable techniques are integrated: (1) SHAP (SHapley Additive exPlanations): Calculates feature importance for soil moisture/health models. (2) Grad-CAM: Visualizes CNN attention regions in GNN image processing. (3) Attention visualization: Heatmaps show Transformer attention weights, explaining why certain RS/IoT data drive moisture predictions (Table 4).

All UAV operations were conducted with necessary safety procedures, such as preserving line-of-sight, avoiding populated areas, and following manufacturer battery management requirements. Protective equipment was used during sensor installation to prevent injuries, and electronic components were protected from moisture exposure.

System deployment and verification
To validate the practicality, real-time performance, and monitoring accuracy of the "RS-UAV-IoT-AI" integrated system, this section details the hardware deployment scheme, experimental area settings, verification indicator system, and testing workflow. The system is deployed in a distributed manner (edge+cloud) and tested against multi-dimensional indicators to ensure it meets the needs of digital agricultural ecological monitoring.

Hardware deployment and network topology
The system adopts a "edge-cloud collaboration" deployment architecture, with edge devices responsible for real-time data processing and cloud platforms for large-scale data storage and model optimization:

Edge-level deployment
Key edge devices are deployed in the Huang-Huai-Hai Plain experimental area, covering 10 townships (total area ~500 km²) with the following configurations: i) Edge computing nodes: 20 NVIDIA Jetson Xavier NX devices (GPU: 384 CUDA cores, 8 GB RAM) deployed at township monitoring stations. It runs lightweight AI models to process IoT sensor data and UAV images, achieving end-to-end inference latency<500 ms. ii) IoT terminals: 500 ESP32-based soil sensors (power consumption: 80 µA in sleep mode) and 30 LoRaWAN gateways (transmission distance: 3-5 km, communication rate: 0.3-50 kbps) form a low-power sensor network. Data are transmitted to edge nodes via LoRaWAN, with battery life ≥ 8 months. iii) UAV fleets: 15 DJI Matrice 350 RTK UAVs (equipped with MicaSense RedEdge-P multispectral cameras) conduct weekly patrols of key plots (100 ha each), with real-time image transmission to edge nodes via 5G.

Cloud-level deployment
A cloud-based computing platform with high-performance CPU, large-memory capacity, and GPUs acceleration was deployed to support multi-source data storage, large-scale AI model training and updating, and web-based visualization services. The platform stores multi-year satellite imagery data and long-term IoT sensor data, supports periodic model retraining using newly acquired data, and provides online interfaces for end users, such as farmers and agronomists, to access monitoring results and early-warning information.

Network topology
Data flow follows a "terminal→edge→cloud" path: i) IoT sensors/UAVs transmit data to edge nodes via LoRaWAN/5G; ii) Edge nodes preprocess data and run lightweight AI models; iii) Summary data are uploaded to the cloud, while raw data are stored locally (edge) for 1 month to reduce cloud transmission costs.

Experimental area and data collection period
The experimental area is located in the Huang-Huai-Hai Plain (33°N-38°N, 114°E-120°E), a major grain-producing region in China with a winter wheat-summer maize rotation system. Key characteristics include: i) Climate: Temperate monsoon climate, annual precipitation: 600-800 mm, average temperature: 13-14 °C. ii) Soil type: Calcareous cinnamon soil (dominant), with soil moisture content ranging from 15-40% in the growing season. iii) Monitoring period: June 2023-May 2024 (1 full crop rotation cycle), covering wheat sowing, jointing, heading, and maize planting, tasseling, and harvesting stages.

Ground truth data are collected monthly via manual sampling: i) Crop health: 100 sampling plots (10 m × 10 m each) are selected to measure NDVI with a handheld GreenSeeker sensor. ii) Soil moisture: Gravimetric method (sampling depth: 10/30/50 cm) is used to calibrate IoT sensor data, with 5 samples per plot. iii) Biodiversity: Insect specimens are collected via pitfall traps and sweep nets in 50 plots, with species identification by entomologists.

Verification indicator system
Four categories of indicators are used to evaluate system performance, covering data accuracy, model effectiveness, real-time performance, and ecological consistency (Table 5):

Data accuracy indicators
Root Mean Square Error (RMSE): Measures the deviation between system-monitored values and ground truth (Equation 10)

    (10)

Where, y_i=ground truth, =system output, n=sample number. Lower RMSE indicates higher accuracy.

Coefficient of Determination (R²): Evaluates the correlation between system output and ground truth (range: 0-1). R²>0.8 indicates good consistency.

AI model effectiveness indicators
F1-score: Assesses the performance of the GNN biodiversity model (binary classification: species present/absent), balancing precision (P) and recall (R) (Equation 11):

    (11)

MAE: As defined in Equation 6, used to evaluate the LSTM crop health model and Transformer soil moisture model.

Real-time performance indicators
i) System response time: Time from data collection (sensor/UAV) to output of monitoring results, measured separately for edge and cloud deployment. ii) Energy consumption: Average power consumption of edge devices) and cloud servers, measured in kWh per day.

Ecological consistency index (ECI)
A novel indicator to evaluate whether the system's monitoring results align with actual ecological processes. ECI is calculated as the Pearson correlation coefficient between system-monitored ecological indices and environmental factors, with ECI > 0.7 indicating good consistency.

Verification workflow
The verification process is divided into three phases: i) During the calibration phase (June-July 2023), ground-truth data were used to calibrate the IoT sensors and AI models. During the testing phase (August 2023-April 2024), the system was operated continuously, with daily monitoring results collected and monthly ground-truth data acquired to calculate verification indicators. During the optimization phase (May 2024), system parameters were adjusted based on the testing results to improve overall performance.

Results

Comparison of multi-source data fusion effects
To verify the value of integrating RS and IoT data for agricultural ecological monitoring, this section compares three data modes-single RS data, single IoT data, and fused RS-IoT data-from three perspectives: data integrity, estimation accuracy of key ecological indicators, and spatiotemporal coverage. All comparisons are based on 12 months of data (June 2023-May 2024) from the Huang-Huai-Hai Plain experimental area, with statistical significance tested...

Discussion

This research offers a collaborative remote sensing-smart devices-AI architecture that aims to address the typical fragmentation of digital agricultural ecological monitoring. By combining multi-scale satellite data, UAV multispectral imaging, and dense IoT ground sensing with advanced AI models, the suggested workflow improves the spatial and temporal resolution of ecological observations. This integrated method is precisely aligned with the protocol steps indicated in the Protocol, and it addresses the overarching goal...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Remote Sensing Platforms
Multispectral Earth Observation Satellite		Sentinel-2, ESA	Vegetation indices, soil & canopy monitoring Generic description: 10–60 m resolution optical satellite system
Medium-resolution Optical/Thermal Satellite		Landsat-9 OLI/TIRS, USGS	Land surface temperature, soil moisture proxies Generic description: 30 m optical & 100 m thermal satellite
High-resolution Commercial Satellite		PlanetScope, Planet Labs	Fine-scale crop stress detection Generic description: 3 m multispectral commercial satellite
UAV Systems
Multispectral UAV Platform		DJI Matrice 350 RTK	Field-level high-frequency imaging Generic description: UAV equipped with multispectral/thermal sensors
Multispectral Camera		MicaSense RedEdge-P	Crop canopy spectral feature extraction Generic description: High-resolution multispectral imaging unit
IoT Sensors (Ground Devices)
Soil Sensor		ESP32-based soil sensor modules	Soil moisture & salinity monitoring Generic description: Soil moisture, EC, pH sensor
Weather Monitoring Station		Commercial AWS units (e.g., Lufft, Vaisala)	Temperature, humidity, PAR, rainfall Generic description: Automatic meteorological station
Acoustic Biodiversity Sensor		Bioacoustic IoT sensors (20–20,000 Hz)	Biodiversity assessment Generic description: Audio sensor for insect sound monitoring
RGB Crop Camera		1920×1080 IoT camera modules	Crop canopy monitoring Generic description: High-resolution RGB imaging camera
Networking Equipment
LoRaWAN Gateway		LoRaWAN gateways (3–5 km range)	Wireless sensor data transmission Generic description: Long-range low-power IoT communication gateway
5G Network Module		5G NR module (generic)	Real-time UAV/edge data transfer Generic description: High-speed communication module
Edge Computing Devices
Edge AI Computing Unit		NVIDIA Jetson Xavier NX	On-site AI inference (less than 500 ms) Generic description: Low-power GPU-based edge AI processor
Cloud Infrastructure
Cloud Compute Server		Alibaba Cloud ECS / Tesla V100 GPUs	Model training, large-scale data storage Generic description: High-performance cloud computing instance
Software / AI Tools
Deep Learning Environment		Python, TensorFlow/PyTorch	AI modelling (LSTM, Transformer, GCN) Generic description: Python-based AI processing environment
Geospatial Processing Software		QGIS / ArcGIS	Image cleaning, GIS mapping Generic description: Remote sensing preprocessing tool
Reagents & Field Materials
Satellite Data Platforms		Copernicus Hub, USGS EarthExplorer	Data download Generic description: Remote sensing data access platform
Soil Sampling Tools		Standard soil auger kits	Ground-truth moisture & EC validation Generic description: Field soil sampling equipment
Insect Sampling Tools		Standard entomological sampling sets	Biodiversity ground truth collection Generic description: Pitfall traps, sweep nets
Miscellaneous Instruments
PAR Sensor		PAR quantum sensor	Crop photosynthesis-related measurement Generic description: Light intensity sensor
Environmental Logging Equipment		Data loggers compatible with IoT sensors	Continuous monitoring Generic description: Data logging modules