An Analytical Approach and Concept Mapping of Agricultural Issues Using Deep Learning Techniques

Precision agriculture has brought a significant transformation towards optimizing modern agricultural practices to enhance crop yield and reduce resource wastage by leveraging advanced technologies like remote sensing, Geographic Information Systems (GIS), and the Internet of Things (IoT). However, the growing threat of weeds continues to pose a significant challenge of unwanted consumption of nutrient resources by weeds therefore affecting the overall quality and nutritional value of crop yields. The traditional methods of controlling weeds largely rely on the use of herbicides that can have adverse impacts on the crops and environment. This research article includes the study of existing techniques for weed control in modern agriculture and proposes an innovative approach to identify potential weed hazards in agricultural fields through the analysis of images. Leveraging advanced machine learning techniques like CNNs and other cutting-edge neural network structures, we develop a weed detection system that utilizes a comprehensive dataset of weed plants to detect areas within the agricultural landscape where the growth of weeds could pose a risk to crops. By training the model on a diverse range of weed species and conditions, we enable it to differentiate between crops and weed types and accurately predict potential threat zones. Our approach not only aids in early weed detection but also facilitates targeted intervention strategies, reducing the need for blanket herbicide application. The results of this study indicate the effectiveness of utilizing deep learning techniques for weed management, contributing to more efficient agricultural methods.