AgroVision AI The Proactive Crop Health Monitoring and Early Disease Detection: A Smart Computer Vision Framework

Abstract

The high global agricultural output has been threatened by crop diseases, pests and weed infestations which result in high loss in yield as well as high operating expenses. Conventional solutions of monitoring are mainly based on manual inspection that is very time consuming and labor consuming as well as is not effective when it comes to detecting at an initial stage. This paper suggests a solution to the mentioned problems, the AgroVision, an intelligent computer vision-based framework that is capable of monitoring crops health in real-time and detecting plant diseases at the earliest stage. The suggested system will make use of the advanced machine learning and deep learning algorithms, specifically Convolutional Neural Networks (CNNs), that will be used to analyze image-based plants automatically and identify the visual symptoms of diseases, pests and weeds. An effective dataset of various types of crop images in different environmental factors is presented to be trained and analyzed. The model is optimized with accuracy and efficiency performance which is high in comparison and the model has better generalization performance in field conditions. The findings of the experimental work indicate that the AgroVision can effectively identify various crop pathogens during early stages hence interventional response can be followed and the losses minimized. Moreover, the system promotes real-time deployment hence is appropriate in the use of precision agriculture. Its major contributions consist of a high-quality and scalable framework on the basis of vision, higher detection rates with the help of the deep learning model, as well as the inclusion of automated monitoring that will reduce human input and maximize the sustainability of agriculture.