AI in Agriculture: Precision Farming and Crop Monitoring

Abstract

This research delves into the application of artificial intelligence in precision farming and crop monitoring, focusing on four AI algorithms: “Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), and K-Nearest Neighbors (KNN)”. The objective of improving crop disease detection accuracy and prediction efficiency, along with yield prediction and resource optimization, is targeted. The models were trained to predict crop health and optimize agricultural practices, using a dataset of crop images and environmental data. For the test result, CNN took the leads with an accuracy of 92.5% in disease detection, followed by RF with an accuracy of 89.3%, SVM with an accuracy of 86.7%, and KNN with an accuracy of 81.5%. Additionally, crop yield prediction using a hybrid AI model incorporating meteorological and soil data showed an R-squared value of 0.88, demonstrating strong prediction capabilities. The integration of AI with UAVs and remote sensing technologies allowed for real-time monitoring of crops, providing farmers with actionable insights to optimize resource use. These results demonstrate the possible significant impact of AI on facilitating sustainable farming practices through cost savings, reduced environmental impact, and improved productivity. In general, AI applications in agriculture will revolutionize precision farming by coming up with intelligent data-driven solutions for crop management.