CHAPTER 5
Artificial Intelligence in Agriculture and Environmental Life Sciences
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5.1 AI IN PRECISION AGRICULTURE AND CROP MONITORING
Artificial Intelligence (AI) is increasingly transforming agriculture from a conventional, experience-based activity into a data-driven and precision- oriented production system. Precision agriculture refers to the use of digital technologies to observe, measure, analyze, and manage agricultural variability at a field or sub-field level. AI strengthens this approach by processing large volumes of information collected through satellites, unmanned aerial vehicles (UAVs), Internet of Things (IoT) sensors, weather stations, farm machinery, and field observations. By converting these data into actionable insights, AI can support farmers in making timely decisions related to irrigation, fertilization, crop protection, weed management, and harvesting. The Food and Agriculture Organization (FAO) identifies soil and crop monitoring and predictive analytics among important areas where AI can contribute to agricultural productivity and efficiency (FAO, 2017). (Figure 5.1)
Figure 5.1: AI in Precision Agriculture and Crop Monitoring
5.1.1 Role of AI in Precision Agriculture
Traditional agricultural management often applies the same quantity of water, fertilizers, pesticides, and other inputs across an entire field. However, crop
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conditions can vary considerably because of differences in soil fertility, moisture, topography, pests, diseases, and microclimatic conditions. Precision agriculture attempts to identify these variations and provide site-specific management. AI algorithms can analyze spatial and temporal data to identify patterns that may not be easily recognized through conventional field inspection.
Machine learning (ML) algorithms such as support vector machines (SVM), random forests (RF), artificial neural networks (ANN), and gradient-based models are widely used for agricultural classification, prediction, and decision support. A 2024 review of remote sensing and machine learning applications in precision agriculture found that SVM and random forest were among the most frequently applied algorithms, while hyperspectral and UAV-based remote sensing were particularly important data sources (Zhang et al., 2024).
Deep learning has further expanded these capabilities. Convolutional neural networks (CNNs) can automatically extract visual features from agricultural images, making them useful for crop classification, disease recognition, weed detection, and plant phenotyping. More recently, transformer-based architectures and self-supervised learning approaches have gained attention because they can learn complex representations from large volumes of remote-sensing data (Silva et al., 2025).
5.1.2 AI-Based Crop Monitoring
Crop monitoring involves continuous observation of crop growth, health, water status, nutrient conditions, and environmental stress throughout the growing season. AI enables this monitoring to move beyond periodic manual inspection toward automated and near-real-time assessment.
Satellite imagery is one of the most important sources of information for large-scale crop monitoring. Platforms such as Sentinel-2 provide multispectral imagery that can be used to calculate vegetation indices, including the Normalized Difference Vegetation Index (NDVI). These indices provide information about vegetation vigor and can be combined with AI models to estimate crop growth, biomass, and yield. Recent research shows that AI combined with Sentinel-2 imagery is increasingly being used for field-level crop monitoring and yield estimation, although cloud cover, limited ground-
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truth observations, and differences between geographical regions remain important challenges (Demissie et al., 2026).
UAVs or drones provide another important source of high-resolution crop information. Unlike satellites, UAVs can collect imagery at very fine spatial resolution and can be deployed when specific fields require inspection. RGB, multispectral, hyperspectral, and thermal cameras mounted on UAVs can capture information about plant structure, chlorophyll activity, water stress, disease symptoms, and temperature variation. AI-based image-processing systems can then identify individual plants, classify crop conditions, and detect abnormal areas. UAV imagery therefore provides a useful balance between high spatial resolution and relatively flexible field-level deployment (Pérez-Ortiz et al., 2024).
5.1.3 Detection of Crop Diseases, Pests, and Stress
One of the most significant applications of AI in crop monitoring is the early identification of diseases and pests. Conventional disease identification depends heavily on farmer experience and physical inspection, which can be time-consuming and difficult over large farms. AI-based computer vision can analyze leaf, stem, fruit, and canopy images and identify visual symptoms associated with different diseases.
CNN-based models can detect characteristics such as discoloration, lesions, fungal spots, abnormal growth, and insect damage. AI systems can potentially detect symptoms at an early stage, allowing farmers to take corrective action before the problem spreads throughout a field. A recent systematic review found CNNs to be among the dominant approaches for AI-based crop disease detection, while satellite and drone-based sensing are increasingly being combined with machine learning for monitoring both biotic and abiotic stresses (2025).
AI can also support the detection of abiotic stresses, including drought, salinity, nutrient deficiency, excessive temperature, and water stress. Deep learning models can integrate RGB, thermal, spectral, UAV, satellite, and IoT sensor data to identify and quantify stress conditions. Such systems can help farmers distinguish between healthy and stressed crops and prioritize areas requiring intervention (2025).
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5.1.4 AI for Irrigation, Fertilization, and Resource Optimization
Crop monitoring becomes more valuable when monitoring information is connected to farm management decisions. AI can combine crop images with soil moisture, weather, temperature, humidity, and historical yield information to recommend appropriate irrigation and fertilization strategies. For example, a field can be divided into management zones according to crop vigor and soil moisture. Areas showing signs of water stress may receive additional irrigation, while healthy areas can receive less water.
This approach can reduce unnecessary use of agricultural inputs and contribute to environmental sustainability. FAO notes that agricultural automation and digital technologies can improve resource-use efficiency, productivity, resilience, and environmental sustainability, although unequal access to technology remains an important concern (FAO, 2022).
5.1.5 Yield Prediction and Decision Support
Another important application is crop yield prediction. AI models can integrate historical yield records, weather conditions, soil properties, vegetation indices, crop growth characteristics, and satellite imagery to estimate expected production before harvesting. Early yield estimates can assist farmers, agricultural cooperatives, food processors, traders, and policymakers in planning storage, transportation, marketing, and food-security strategies.
Recent research indicates that the integration of AI with remote sensing is moving toward field-level yield prediction, multimodal data fusion, and integration with crop-growth models. Combining different information sources can improve the reliability of predictions under changing environmental conditions (Demissie et al., 2026).
5.1.6 Challenges and Future Directions
Despite its potential, AI-based precision agriculture faces several limitations. High-quality labelled datasets are often difficult to obtain, and models trained under one climatic, geographic, or crop condition may perform poorly in another. Recent systematic reviews emphasize problems related to dataset diversity, external validation, model transferability, computational requirements, and the cost of field deployment (2026).
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Smallholder farmers may also face barriers related to the cost of drones, sensors, connectivity, computing infrastructure, and technical expertise. Consequently, the future of AI in agriculture should focus not only on improving model accuracy but also on developing affordable, explainable, scalable, and locally adaptable systems. Multimodal AI, edge computing, self-supervised learning, foundation models, and integration of satellite, UAV, IoT, and ground-based observations are likely to become increasingly important.
Overall, AI is changing crop monitoring from periodic observation to continuous, predictive, and site-specific management. Its integration with remote sensing and precision agriculture can improve early stress detection, resource efficiency, yield forecasting, and agricultural sustainability. However, successful adoption will depend on reliable datasets, robust validation, farmer accessibility, appropriate infrastructure, and responsible implementation. The long-term objective should therefore be to develop AI systems that complement farmers' knowledge and decision-making rather than simply replacing human judgment.
5.2 INTELLIGENT SYSTEMS FOR SOIL AND WATER MANAGEMENT
Soil and water are two of the most critical resources determining agricultural productivity, ecosystem stability, and food security. Increasing water scarcity, soil degradation, irregular rainfall, groundwater depletion, and climate variability have created a need for more precise and efficient resource-management approaches. Traditional soil and irrigation management often depends on periodic field observations, fixed irrigation schedules, and generalized recommendations. Although these methods remain useful, they may not adequately capture the spatial and temporal variability of soil and crop conditions. Artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), and remote sensing are therefore emerging as important technologies for developing intelligent soil and water management systems. FAO emphasizes that improving agricultural water management is essential for food security and sustainable resource use, particularly because agriculture accounts for the largest share of global freshwater withdrawals (FAO, 2026). (Figure 5.2)
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Figure 5.2: Intelligent Systems for Soil and Water Management
5.2.1 AI-Based Soil Monitoring and Assessment
Intelligent soil-management systems combine data from soil sensors, laboratory measurements, weather stations, satellite imagery, drones, and geographic information systems (GIS). AI and ML algorithms can analyse these heterogeneous datasets to identify relationships between soil properties, environmental conditions, crop performance, and management practices. Important soil parameters that can be monitored or estimated include soil moisture, temperature, pH, electrical conductivity, organic carbon, salinity, nutrient availability, and soil structure.
Soil-moisture monitoring is particularly important because it directly influences irrigation decisions and plant growth. Machine-learning models can estimate soil moisture from meteorological observations, soil characteristics, vegetation indices, topography, and remote-sensing information. Recent research shows that combining remote sensing with ML can improve the spatial monitoring of soil moisture and support more efficient agricultural water management (Ben Abbes et al., 2024). Instead of measuring moisture only at a limited number of physical sampling points, AI-supported systems
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can generate spatially distributed estimates, helping farmers identify dry and adequately hydrated zones within the same field.
AI can also contribute to digital soil mapping. By integrating historical soil samples with satellite data, elevation, rainfall, temperature, land-use information, and other environmental variables, ML algorithms can predict soil characteristics over areas where direct sampling is unavailable. Such approaches can reduce the cost and time associated with extensive field sampling while providing information useful for site-specific fertilization, irrigation, and land-management decisions.
5.2.2 Intelligent Irrigation and Water Management
One of the most significant applications of AI in agriculture is intelligent irrigation. Conventional irrigation often follows fixed schedules, which can result in over-irrigation during wet periods or water stress during dry periods. Intelligent irrigation systems instead use real-time and predictive information to determine when, where, and how much water should be applied.
A typical AI-enabled irrigation system may integrate soil-moisture sensors, weather forecasts, crop-growth information, evapotranspiration estimates, satellite imagery, and irrigation-system data. ML models analyse these inputs and estimate future crop-water requirements. The irrigation controller can then automatically activate pumps, valves, or drip systems according to the predicted requirement. A systematic review of smart irrigation research found that ML techniques have demonstrated considerable potential for improving irrigation decision-making, although issues concerning generalizability and field-level implementation remain (Abioye et al., 2024).
AI can also support irrigation scheduling by predicting soil-moisture depletion and crop water demand. More advanced systems use reinforcement learning, in which an algorithm learns irrigation strategies from environmental feedback and attempts to balance crop requirements with water conservation. Such approaches can consider both present soil conditions and expected future moisture losses rather than responding only to current sensor readings.
5.2.3 Integration of Remote Sensing, IoT, and AI
The effectiveness of intelligent soil and water management increases when AI is combined with IoT and remote sensing. IoT networks allow sensors
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distributed across agricultural fields to continuously collect information about soil moisture, temperature, humidity, rainfall, water flow, and other variables. This information can be transmitted to cloud-based or edge-computing platforms where AI models analyse the data and generate recommendations.
Satellite and drone-based remote sensing provide another important information layer. Multispectral and hyperspectral imagery can help identify variations in vegetation health, soil conditions, moisture levels, and water stress. When these observations are combined with ground-based measurements, AI models can create detailed maps showing areas requiring irrigation or further investigation. A recent review of digital agricultural water-management technologies identified AI and remote sensing among the most versatile technologies for improving irrigation efficiency, identifying water-related problems, and supporting resource management (Parra-López et al.,
2025).
5.2.4 Environmental Benefits and Sustainability
Intelligent soil and water management can produce benefits beyond increasing agricultural productivity. More precise irrigation can reduce unnecessary water extraction, minimize runoff, and decrease the risk of waterlogging and salinity. Better soil monitoring can support conservation practices, improve nutrient- use efficiency, and help maintain soil organic matter. These outcomes contribute to the broader objectives of sustainable and climate-resilient agriculture.
FAO notes that modern irrigation management increasingly requires technical and managerial improvements that enhance water, labour, economic, and environmental resource utilization. Decision-support tools can help determine appropriate irrigation timing, water allocation, and crop-water requirements (FAO, 2026). In this context, AI should be viewed not as a replacement for agronomic knowledge but as a decision-support technology that strengthens farmers' ability to interpret complex information.
5.2.5 Challenges and Future Directions
Despite its potential, intelligent soil and water management faces several challenges. AI models require reliable, representative, and sufficiently large
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datasets. Sensor failures, missing observations, differences in soil types, regional climatic variations, and limited field data can reduce model reliability. The cost of sensors, drones, connectivity, computing infrastructure, and technical maintenance can also restrict adoption, particularly among smallholder farmers. Recent research highlights data availability, model complexity, infrastructure limitations, and the need for more generalizable models as continuing challenges in AI-based agricultural applications (Ben Abbes et al., 2024; Abioye et al., 2024).
Future intelligent systems are likely to combine AI with physical soil-water models, satellite observations, IoT sensors, digital twins, and real-time weather forecasting. Hybrid models that integrate physical knowledge with data-driven learning may improve reliability and interpretability compared with purely data-driven approaches. Ultimately, the goal is to develop affordable, transparent, and locally adaptable systems capable of providing farmers with actionable recommendations while conserving soil and water resources.
5.3 AI-BASED PEST AND DISEASE DETECTION
Pests and diseases are among the major biological constraints affecting agricultural productivity, crop quality, and food security. Conventional identification generally depends on visual inspection by farmers, agricultural extension workers, or plant pathologists. Although expert diagnosis can be highly reliable, it is often time-consuming, subjective, and difficult to scale across large agricultural areas. Smallholder farmers may also lack timely access to specialists, particularly in remote regions. Artificial intelligence (AI), especially machine learning, deep learning, and computer vision, is increasingly being applied to automate the detection and identification of crop pests and diseases from images and other environmental data (Mohanty et al., 2016; Pacal et al., 2024). (Figure 5.3)
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Figure 5.3: AI-Based Pest and Disease Detection
5.3.1 Role of AI in Pest and Disease Detection
AI-based detection systems generally operate by analyzing visual or sensor-based characteristics associated with crop stress. Images of leaves, stems, fruits, flowers, or insects can be captured using smartphones, digital cameras, drones, or field-based imaging systems. These images are processed by AI algorithms to identify symptoms such as discoloration, lesions, spots, wilting, deformation, holes, fungal growth, or the physical presence of insects. The system can subsequently classify the crop condition as healthy or affected by a particular pest or disease.
The development of deep learning, particularly convolutional neural networks (CNNs), has significantly improved automated plant disease recognition. CNNs can automatically learn relevant visual features from images instead of relying entirely on manually designed features. A landmark study by Mohanty et al. (2016) trained a deep convolutional neural network using 54,306 images representing 14 crop species and 26 diseases or healthy conditions. The model achieved 99.35% accuracy on a controlled test dataset, demonstrating the potential of deep learning for automated plant disease diagnosis. However, its
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performance dropped substantially when tested on images collected under different real-world conditions, highlighting the importance of diverse and representative training data.
5.3.2 AI Techniques Used in Detection
Several AI approaches are currently used for agricultural pest and disease detection. Image classification models determine whether an image belongs to a particular disease category. Object-detection models, including YOLO-based architectures, can identify and localize multiple pests or disease symptoms within an image. Image segmentation techniques go one step further by identifying the precise region of an affected leaf or fruit. Recent systematic reviews show that CNNs, transfer learning, object detection, segmentation, and transformer-based approaches are among the major techniques being investigated for plant disease recognition (Pacal et al., 2024).
Transfer learning is particularly useful when agricultural datasets are relatively small. In this approach, a model previously trained on a large image dataset is adapted to recognize crop-specific symptoms. This reduces the amount of training data and computational resources required. Lightweight architectures can also be deployed on smartphones and edge devices, allowing farmers to receive diagnostic assistance directly in the field.
5.3.3 Data Sources and Detection Workflow
An AI-based pest and disease detection system generally follows a sequence of steps:
Image/Data Acquisition → pre-processing → Feature Learning → Classification/Detection → Diagnosis → Decision Support
During data acquisition, images may be collected under laboratory, greenhouse, or field conditions. Pre-processing can include resizing, noise reduction, normalization, background removal, and image augmentation. The processed images are then supplied to a trained AI model. Depending on the application, the model may classify the disease, identify an insect, estimate disease severity, or locate affected regions.
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The quality and diversity of datasets are critical. Images collected only under controlled backgrounds and lighting conditions may produce excellent laboratory results but fail when exposed to complex field environments. Variations in sunlight, shadows, leaf orientation, soil background, crop varieties, camera quality, and disease severity can substantially affect model performance. Recent reviews emphasize dataset quality, generalization, and realistic field validation as continuing challenges in AI-based plant disease detection (Ngugi et al., 2024; Bagga, 2024).
5.3.4 Integration with Precision Agriculture
AI-based detection becomes more valuable when integrated with precision agriculture technologies. A farmer can use a smartphone to photograph an affected crop, while the AI system identifies a probable pest or disease and provides a confidence score. Drone-based systems can survey large fields and identify areas showing abnormal vegetation patterns. Satellite imagery and hyperspectral sensors can provide additional information about crop stress that may not be visible to the human eye.
AI can also combine image-based observations with weather, soil, crop-growth, and historical disease data. Such multimodal systems can move beyond simple diagnosis toward disease-risk prediction. For example, temperature, humidity, rainfall, and leaf-wetness conditions can be analyzed together with visual symptoms to estimate the likelihood of disease development. This can support earlier intervention and more targeted crop-management decisions.
5.3.5 Benefits for Sustainable Agriculture
The application of AI to pest and disease detection offers several potential benefits. Early identification can allow farmers to isolate affected plants or apply treatment before an outbreak spreads. Accurate detection can also reduce unnecessary pesticide application by enabling targeted interventions rather than treating entire fields indiscriminately. This may reduce input costs, environmental contamination, and risks associated with excessive chemical use.
AI can additionally improve agricultural monitoring by providing rapid and standardized assessments over large areas. For agricultural researchers and
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extension agencies, digital disease records can contribute to surveillance systems and help identify emerging patterns. In this sense, AI-based detection is not simply a replacement for human diagnosis; it can function as a decision-support technology that extends the reach of agricultural expertise.
5.3.6 Challenges and Future Directions
Despite considerable progress, several challenges remain. Models trained on limited datasets may perform poorly on unfamiliar crops, disease stages, geographical regions, or environmental conditions. Some diseases also produce visually similar symptoms, making accurate classification difficult. Furthermore, AI predictions can be difficult for farmers to interpret when models operate as “black boxes.” Explainable AI (XAI) techniques are therefore becoming increasingly important because they can indicate which portions of an image influenced a model's prediction.
Future systems are likely to combine computer vision with multimodal AI, edge computing, drones, Internet of Things (IoT) sensors, and geospatial data. Vision transformers and other advanced architectures are also expanding the range of possible detection methods. However, technological accuracy must be accompanied by field validation, affordability, multilingual interfaces, farmer training, and reliable agricultural recommendations. The ultimate objective should be an accessible AI-assisted crop protection ecosystem that enables earlier diagnosis, more precise intervention, reduced resource use, and improved agricultural sustainability.
5.4 ENVIRONMENTAL MONITORING AND CLIMATE PREDICTION
Environmental monitoring and climate prediction have become increasingly important as climate change, extreme weather events, biodiversity loss, water scarcity, and land degradation create complex challenges for agriculture and ecosystem management. Traditional environmental monitoring depends heavily on field observations, weather stations, laboratory measurements, and periodic surveys. Although these approaches provide valuable information, they can be expensive, spatially limited, and difficult to scale. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), is transforming this landscape by enabling the rapid analysis of large and heterogeneous environmental datasets generated by satellites, drones, Internet
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of Things (IoT) sensors, weather stations, and geographic information systems (GIS). AI-based approaches can identify patterns, detect anomalies, estimate environmental variables, and generate predictions that support timely decision-making (Tuia et al., 2023; Han et al., 2024). (Figure 5.4)
Figure 5.4: Environmental Monitoring and Climate Prediction
5.4.1 AI for Environmental Monitoring
Environmental monitoring involves continuous or periodic observation of variables such as temperature, precipitation, soil moisture, vegetation health, air quality, water quality, land use, forest cover, and ecosystem conditions. AI improves this process by combining information from multiple sources and automatically identifying meaningful patterns.
Remote sensing is one of the most important data sources for AI-enabled environmental monitoring. Satellite imagery provides repeated observations over large geographical areas, while unmanned aerial vehicles (UAVs) can provide high-resolution information at the field or local scale. Machine learning algorithms can process multispectral, hyperspectral, thermal, and radar imagery to classify land cover, identify vegetation stress, monitor forest
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changes, and estimate soil and water characteristics. The integration of remote sensing and machine learning has therefore become an important component of precision agriculture and environmental assessment (Han et al., 2024; Wang et al., 2024).
AI is particularly useful for monitoring vegetation and crop conditions. Algorithms such as Random Forest, Support Vector Machines, convolutional neural networks (CNNs), and other deep-learning architectures can identify changes in vegetation indices and imagery associated with drought stress, disease, nutrient deficiencies, or changes in crop growth. This enables agricultural managers to detect problems earlier and apply water, fertilizers, or other interventions more efficiently. Recent research indicates that CNN-based approaches have become particularly prominent in automated crop monitoring, although limitations related to annotated datasets and model generalization remain important challenges (Jabed et al., 2024).
Soil and water monitoring also benefit from AI. Soil moisture is a critical environmental variable because it affects crop growth, irrigation requirements, hydrological processes, and drought assessment. Machine learning models can estimate soil moisture from satellite observations and other environmental variables, potentially providing more detailed spatial information than conventional measurements alone. Recent research highlights the value of combining physical models with machine learning to improve the accuracy, scalability, and robustness of remote-sensing-based soil-moisture estimation (Ben Abbes et al., 2024).
AI can similarly support water-quality monitoring by analysing measurements such as temperature, turbidity, dissolved oxygen, nutrient concentrations, and other indicators. Predictive models can identify abnormal conditions and provide early warnings of pollution or ecological deterioration. When integrated with sensor networks, AI can support near-real-time environmental surveillance rather than relying exclusively on periodic manual sampling.
5.4.2 AI for Climate Prediction
Climate prediction involves estimating future atmospheric and environmental conditions using historical observations, physical relationships, and numerical models. Conventional climate models are based primarily on physical
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equations describing the atmosphere, oceans, land surface, and cryosphere. AI does not necessarily replace these models; instead, it can complement them by learning complex relationships from large datasets and improving computational efficiency.
Machine learning can be applied to predict variables such as temperature, precipitation, drought probability, extreme rainfall, soil moisture, and agricultural climate risks. Time-series models can analyse historical weather observations to identify trends and recurring patterns, while deep-learning models can process high-dimensional spatial and temporal datasets. In agroclimatic research, ML and DL are increasingly being investigated for understanding climate impacts and developing adaptation strategies (Hassan et al., 2024).
One important application is extreme-event prediction. Droughts, floods, heatwaves, storms, and excessive rainfall can severely affect agricultural productivity and ecosystems. AI systems can analyse historical weather records, satellite observations, soil characteristics, topography, and atmospheric variables to estimate the probability of such events. Early-warning systems based on these predictions can help farmers and environmental authorities take preventive measures.
AI can also contribute to local-scale weather and microclimate prediction. Conventional weather forecasts may not capture environmental differences between individual farms or small geographical areas. Combining IoT sensors with AI models can provide high-resolution information about temperature, humidity, rainfall, and other local conditions. Such systems can support decisions concerning irrigation, planting, harvesting, and protection against weather-related risks.
5.4.3 Applications in Agriculture and Environmental Management
The combination of environmental monitoring and climate prediction creates several practical applications. Farmers can use AI-generated information to determine irrigation schedules, identify drought-prone areas, select appropriate planting periods, and anticipate climate-related crop losses. Environmental agencies can use AI to monitor deforestation, land degradation, ecosystem changes, and water resources. AI-supported soil-carbon mapping can also
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reduce dependence on extensive physical sampling by using environmental and remote-sensing variables to estimate soil organic carbon across larger areas.
Another important application is climate-resilient agriculture. Crop-yield prediction models can integrate historical yields, weather conditions, soil characteristics, satellite imagery, and management information to estimate expected production. Such predictions can assist farmers, governments, insurers, and food-supply organizations in preparing for climate variability. Recent reviews demonstrate the growing use of ML, DL, ensemble learning, and explainable AI for crop-yield prediction under abnormal climatic conditions (Jabed et al., 2024).
5.4.4 Challenges and Future Directions
Despite its potential, AI-based environmental monitoring faces several challenges. Environmental datasets may contain missing observations, measurement errors, inconsistent spatial resolutions, and geographical biases. Models trained in one region may perform poorly in another because climate, soil, vegetation, and agricultural practices differ. The lack of high-quality labelled datasets and difficulties in combining satellite, sensor, meteorological, and field data can also limit model performance.
Interpretability is another important concern. Environmental decisions can have significant economic and ecological consequences, making it necessary to understand why an AI system produces a particular prediction. Explainable AI, physics-informed machine learning, and hybrid models that combine physical knowledge with data-driven algorithms are therefore likely to become increasingly important.
Overall, AI is emerging as a powerful component of environmental monitoring and climate prediction. Its greatest value lies not simply in generating predictions but in transforming large volumes of environmental data into actionable information. When combined with remote sensing, IoT, GIS, physical climate models, and expert knowledge, AI can support more accurate monitoring, earlier warnings, efficient resource management, and climate-resilient agriculture. Future systems are likely to emphasize multimodal data integration, explainability, physics-aware learning, edge computing, and real-
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time decision support, making AI an increasingly important technology for sustainable management of agricultural and environmental life sciences.
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