Revealing and Categorizing Weeds in Cotton Plants using XAI and RepVGGPlus

About the Project

The main focus of this research project is to develop an explainable AI-based deep learning framework for revealing and categorizing of crop weeds in cotton plants. Weed revealing and categorizing is an essential task in precision agriculture as it enables farmers to apply herbicides selectively, reducing the amount of chemicals used and thus minimizing the influence on the atmosphere and human wellbeing. This framework is designed using image pre-processing techniques to crop relevant structures from the images, a deep neural network RepVggPlus to classify the presence of weeds, and finally explainable AI using LIME to elaborate on predicting why these particular results are shown. Compared to existing techniques, our proposed approach offers several advantages such as it can reveal and categorize multiple weed species simultaneously, which is not possible with most existing methods; moreover, our system provides explainable results, allowing farmers to understand how the decision was made and increasing their trust in the system. The outcome is a model that can be used as a tool that can assist farmers and agricultural workers in identifying and removing weeds, leading to increased crop yields and reduced use of herbicides. The framework designed is validated and tested on various different crop fields, to increase its generalization power, and to make sure it will work under different lighting conditions, angles of view and image resolution. This framework uses RepVggPlus which is compared with other deep learning models and proves to be better in terms of accuracy and prediction rate.