This research presents a comprehensive study on the application of Convolutional Neural Networks (CNNs) for precision agriculture, with a focus on the classification of crop and weed species. By leveraging deep learning techniques, we aim to optimize resource management in agriculture, thereby reducing environmental impact and maximizing crop yield. Our study addresses the challenges inherent in current agricultural practices, particularly the need for more efficient methods of classification and population density estimation to optimize fertilizer and pesticide application. We developed a CNN model that demonstrates high accuracy in identifying key crop and weed species, providing a robust tool for data-driven agricultural decision-making. The paper outlines the methodology, experimental setup, and model evaluation, and discusses the interpretation of results, which underscore the model`s potential to revolutionize agricultural practices. The implications for agricultural sustainability are significant, as our automated system facilitates precise and efficient crop and weed identification, contributing to more informed and sustainable farming practices.

Precision Agriculture, Convolutional Neural Networks, YOLO, Transfer Learning, Deep Learning, Crop Classification, Weed Detection, Transfer Learning, Image Processing, Resource Management, Sustainable Agriculture.

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