Crop and Pest Classification Using Deep Learning

Crop pests pose a hazard to agriculture by lowering yields and creating large losses. Timely intervention depends on prompt and precise pest identification. Convolutional Neural Networks (CNNs), a type of deep learning, are used in this study to effectively classify pests. To improve performance, the method places a strong emphasis on image preprocessing, accurate pest segmentation, and transfer learning. The algorithm is trained on a large dataset of photos of pests and non-pests to find distinctive characteristics for precise categorization. With an emphasis on improved image quality, segmentation, and reliable classification for practical agricultural applications, the system seeks to automate pest identification and suggest suitable herbicides.