A Review on Crop and Weed Identification Using Deep Learning

Agriculture remains the backbone of several economies in the world, especially in underdeveloped countries. With the rapid growth of the population and the increasing demand for food, farmers need to maximize productivity and one possibility is the reduction of losses. Weeds are one of the major dangers in farming. Indeed, they compete vigorously with the crop for nutrients and water. Improved methods are required to get good yields from crops. The proposed model aims to organize a diverse dataset of crop and weed images, leveraging Convolutional Neural Networks (CNN) for crop and weed identification, texture feature extraction, and employing CNN for precise crop and weed identification. The paper introduces a novel deep-learning approach utilizing Residual Neural Networks (ResNet) for effectively identifying and classifying crops and weeds in agriculture. The findings underscore the effectiveness of various deep learning models such as CNNs, in accurately detecting weeds within crops, aided by preprocessing techniques and model optimization. These advancements hold promising prospects for revolutionizing agricultural practices and enhancing productivity in the future.