Smart Agriculture Lab- GITAM

Niche areas of work :

1. Plant Health Monitoring Using IoT and Machine Learning
   At the Smart Agriculture Lab, we focus on leveraging Internet of Things (IoT) sensors to collect real-time data on soil parameters (moisture, pH, NPK, and temperature) and plant health. By integrating this data with advanced machine learning models, we aim to develop predictive tools for disease detection and crop health monitoring, enhancing crop management strategies for farmers.

2. Soil Microbial Analysis for Disease Management
   A unique aspect of our work involves combining soil microbial analysis, hyperspectral imaging, and DNA sequencing to predict soil-borne diseases. We aim to develop advanced predictive models that can help forecast and manage diseases like Fusarium Wilt, reducing the risk for tomato, cucumber, and other crops.

3. Real-Time Crop Disease Detection Using Image Processing
   Using computer vision and deep learning techniques, we focus on automating the detection of diseases in crops through visual data analysis. By capturing and analyzing images of crops, we identify early signs of plant diseases and provide actionable insights for disease management. This work is crucial for timely interventions in crop health, reducing losses and promoting sustainable farming.

4. Hydroponics and Vertical Farming
   We explore the use of hydroponic systems and vertical farming techniques, integrating IoT for real-time monitoring and control of nutrient levels, water usage, and environmental conditions. This niche project investigates the potential for growing crops like herbs and tomatoes in controlled environments, optimizing resource usage and enhancing crop yield efficiency.

5. Cucumber Shelf-Life Prediction and Post-Harvest Analysis
   Using visual features and data analysis, we aim to predict the shelf-life of cucumbers by analyzing changes in their texture and appearance over time. This work supports the development of systems that can help manage post-harvest quality, optimize storage conditions, and reduce food waste.

6. Smart Agricultural Chatbot for Disease Detection
   The development of an AI-powered Smart AgriBot is a key innovation, providing farmers with a tool to receive real-time insights and disease detection alerts. By integrating image and video analysis of crops, this chatbot will serve as a bridge for farmers to receive practical advice and disease management suggestions based on real-time data collected from their fields.

7. Crops Data Modeling and Yield Prediction
   Developing models for predicting crop yield based on a variety of factors, such as weather conditions, soil health, and plant growth. By creating accurate yield prediction models, we aim to help farmers optimize crop production, plan better, and increase food security.