Deep Learning and Internet of Things based Detection of Diseases and Prediction of Pesticides in Fruits

Agriculture plays a vital role in the Indian economy, being one of the largest employers and contributing significantly to the country's Gross Domestic Product (GDP). The prevalence of diseases in fruits can have a severe impact on crop yield, leading to financial losses for farmers and affecting the entire agriculture industry and economy. Early detection and prevention of diseases are critical to minimizing their impact on crop yield. An IoT and Deep Learning-based fruit disease detection system can provide a solution to this pressing issue. The proposed system automatically detects fruit diseases and suggests preventive measures to minimize their impact on the yield. This improves crop monitoring efficiency and saves time and resources that are required for manual monitoring. The use of deep learning algorithms in the system ensures high accuracy in disease detection and prevention, ultimately leading to improved crop production and increased financial benefits for farmers. This study discusses about the process of image classification using deep learning algorithms like modified CNN, ResNet50, and VGG19. In conclusion, the IoT-based fruit disease detection system using deep learning is a valuable tool for farmers and others in the agriculture industry to ensure optimal crop production and improve the overall economy.