In an agricultural-based country like India, farming and farming activities play a vital role in the growth of the economy as it is the main source of GNI (Gross National Income). This dependence of GNI on agriculture makes it important to address the issues faced by the farmers. The main area of concern for farmers revolves around crops and livestock. Precise farming techniques like cattle counting and crop disease detection are the need of the hour. The introduction of computer vision and deep learning has enabled us to make improvements in farming techniques. To accomplish this, a computer vision-based system is proposed which will be implemented using ResNet and YOLOv3-tiny. The proposed system will take images and videos as input and run them on the inference. The output will be updated in the database and the farmer will be notified in case of any inconsistency. The detailed report can be accessed by government agencies. The system will increase efficiency in farming processes like crop monitoring, livestock tracking, crop disease detection by providing fast and efficient solutions for the problems faced by the farmers.
A Post-processing Fusion Framework for Deep Learning Models for Crop Disease Detection