Smart Crop Diagnostics: An AI-Driven Web Platform for Crop Disease Detection and Management
Keywords:
Convolutional neural networks, Crop disease detection, Deep learning, Precision agriculture, Web applicationAbstract
Plant diseases greatly affect how much food is produced, the security of food supplies, and the income of farmers, especially in poorer countries where it's hard to get help from experts or use lab facilities. New developments in deep learning and computer vision have made it possible to create systems that can automatically detect plant diseases through images, helping farmers make quick and correct decisions. This paper introduces Smart Crop Diagnostics, an online smart system that helps identify leaf diseases in crops and suggests tailored treatments and ways to prevent them. The system uses Convolutional Neural Networks (CNNs) to classify diseases based on images and combines advanced Application Programming Interfaces (APIs) to improve accuracy and provide better, situation-aware advice. Farmers can upload leaf pictures, get immediate disease diagnoses along with confidence levels, and find organic and chemical solutions based on data. Studies show that the system is fast and dependable, lessens the need for expert help, and helps in farming in a more sustainable way. The framework shows great potential for growing and connecting with environmental and IoT-based data sources, making it a good choice for precision agriculture.
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