Intelligence Pest Detection and Control in Agriculture using Computer Vision and Deep Learning

One of the most significant challenges in agriculture over the years has been the detection and also control of pests and while it continues to this day, the advent of technology in the agricultural sector is providing solutions that are incredibly promising. Pests cause significant worldwide crop loss and represent a threat to food security and farmer livelihoods. Conventional pest management approaches revolve around labour-intensive strategies, large applications of pesticides, and reactionary measures, resulting in environmental degradation and sub-optimal crop yields. This work utilizes the power of AI(Artificial Intelligence),computer vision and deep learning to create a smart pest detection system. It treats pest identification and damage detection as real-time visual data processing tasks with machine learning approaches, making it possible to swiftly and accurately locate pests and their damage in the photos taken through the camera, facilitating extra prompt preventive measures. Working under the hood, the algorithms adapt to changing environmental conditions and shifting pest behaviours, learning as they go. Most of the pest detection systems today are manual, requiring manual visual inspection and image recognition approaches, this solution not only provides the next level of system but it also paves the way for pest management in the most efficient, accurate & sustainable way.