Smart Crop Disease Detection with Smart Irrigation System

ABSTRACT: Agriculture is facing major challenges due to crop diseases and inefficient water management techniques, which are affecting productivity and sustainability. This research paper focuses on designing and implementing an AI-based crop disease detection and smart irrigation system to improve the efficiency of agricultural activities. The proposed AI-based system uses deep learning techniques, specifically Convolutional Neural Networks (CNN), to detect crop diseases from images of affected leaves with high accuracy. At the same time, the IoT-based smart irrigation module monitors the moisture level of the soil and controls the water supply automatically using a microcontroller module. The integration of artificial intelligence and IoT technology helps improve the efficiency of agricultural activities by monitoring and controlling diseases and water consumption. The experimental results show that the proposed model achieves 94% accuracy in detecting diseases and reduces water consumption by 25% compared to traditional irrigation systems. This research paper proves that AI and IoT technology can improve the efficiency of agricultural activities significantly by increasing crop yields while conserving resources and improving the decision-making process. Keywords: Artificial Intelligence, Crop Disease Detection, Smart Irrigation, Machine Learning, IoT, Precision Agriculture, Deep Learning.