Ai Driven Interactive Agri Bot Providing Realtime Assistance in Cultivation and Market Linkages

Abstract

This project introduces an integrated system for smart agriculture, employing Internet of Things (IoT) technology for soil type analysis and deep learning methodologies for pest detection. The proposed system leverages a specialized NPK sensor for real-time measurement of soil nutrient levels, facilitating precision agriculture practices. Additionally, a Convolutional Neural Network (CNN) algorithm is employed to detect pests in crops, enhancing crop management efficiency and yield optimization. The IoT-based NPK sensor enables farmers to monitor essential soil nutrients such as nitrogen (N), phosphorus (P), and potassium (K) levels remotely and in real-time. This data empowers farmers to make informed decisions regarding fertilization strategies, ensuring optimal nutrient balance for healthy plant growth while minimizing resource wastage and environmental impact. To the deep learning framework, specifically CNN, is utilized for pest detection in crops. By analyzing images captured from smart agricultural cameras, the CNN model can identify and classify various pests and diseases affecting crops. This enables early detection and intervention, thereby mitigating potential crop damage and yield losses. The integration of CNN-based pest detection with IoT infrastructure enables timely and targeted pest management actions, reducing reliance on chemical pesticides and promoting sustainable agricultural practices.