Optimizing Crop Management: Customized CNN for Autonomous Weed Identification in Farming

The agricultural industry is increasingly adopting Deep Learning methodologies to tackle obstacles related to weed identification and categorization, with the ultimate goal of enhancing crop productivity. However, the complexity stems from the striking similarity in colours, forms, and textures between weeds and crops, specifically when they are in the process of growing. Automated and precise weed identification is of the utmost importance to minimize agricultural losses and maximize the use of resources. The analysis of the literature under review enhances comprehension of the obstacles, remedies, and prospects associated with weed identification and categorization via CNN models. To address these obstacles, we have devised a solution that entails the construction and refinement of a customized Convolutional Neural Network model. The experiment employs the Four-class weed dataset obtained from Kaggle and utilizes the Adaptive Moment Estimation optimizer during the training process. The accuracy of 96.58% is demonstrated by the proposed model in accurately identifying and categorizing weeds in the fields.