AI revolutionizing early disease detection in protected crops

Kshetrimayum Somendro Singh and Dr Aruna Beemrote
Contd from previous issue
For example, early symptoms of powdery mildew can be detected as slight white patches on leaves, which may not be easily visible to farmers. AI models can identify these patterns with high precision, allowing early control measures.
Sensor-Based Monitoring
Environmental factors such as temperature, and humidity play a crucial role in disease development. Sensors placed inside protected structures continuously collect these data. AI models analyze these parameters to predict the likelihood of disease occurrence. For instance, high humidity and moderate temperatures create favourable conditions for the growth of fungal diseases such as blight, botrytis, powdery mildew, and downy mildew. AI systems can generate alerts when such conditions persist for a certain period.
IoT and Real-Time Data Integration
By integrating AI with IoT devices, we can gather and analyze data in real time. Sensors and cameras link up through a network for nonstop monitoring and smart, automatic decisions. If the system spots a potential disease, it jumps into action—like adjusting ventilation, reducing humidity, or applying targeted treatments. This cuts out a lot of manual work and speeds up response time.
Spectral and Thermal Imaging
Advanced imaging methods like hyperspectral and thermal imaging are increasingly used for early detection of plant diseases. These techniques can sense subtle physiological changes in plants, such as shifts in chlorophyll levels or slight differences in leaf temperature, even before any visible symptoms develop.
Applications in Protected Crops: Early Detec- tion of Fungal Diseases
Fungal diseases are common in protected environments due to high humidity. AI-based systems can detect early signs of diseases like powdery mildew, downy mildew, and botrytis.
Early detection helps in timely application of fungicides, reducing crop damage.
Disease Forecasting and Risk Assessment
AI models can predict disease outbreaks based on environmental conditions and historical data. This helps farmers take preventive measures rather than reactive ones. For example, if conditions are favourable for a particular disease, the system can alert the farmer in advance.
Precision Crop Protection
Instead of applying pesticides uniformly, AI enables targeted treatment of affected areas. This reduces chemical usage, lowers production costs, and minimizes environmental impact.
Automated Decision Support Systems
AI-powered platforms provide farmers smart recommendation tips on handling crop diseases, like the best times to spray, irrigation adjustment, and optimizing climate controls like temperature and humidity. These tools help farmers in making solid, informed choices based on real data.
Advantages of AI in Disease Detection
• Early and reliable detection : Diseases can be identified at a very early stage, often before clear symptoms are visible
• Lower crop losses: Early warning allows farmers to take timely action and prevent serious damage
• Better use of inputs: Helps in applying water and pesticides more precisely, avoiding unnecessary use
• Less labour required: Reduces the need for frequent manual checking of crops
• Suitable for large systems: Can be effectively used in large greenhouse or protected cultivation setups
Challenges and Limitations: Even though AI offers many benefits, its use in protected cultivation still faces some practical difficulties
• High initial investment: Setting up sensors, cameras, and AI-based systems can be costly
• Need for large datasets: Accurate performance depends on the availability of goodquality data for training
• Requirement of technical skills: Farmers may need proper training to operate and manage these systems
• Dependence on internet connectivity: Stable internet is important for real-time monitoring and data transfer
• Limited flexibility: AI models may not work equally for all crops and regions without proper adjustments.
The writer is Agriculture Officer, Department of Horticulture & Soil Conser- vation, Government of Manipur and Scientist (SS), ICAR Research Complex for NEH-Region, Manipur Centre, Lamphelpat