Weed and Crop Detection Using YOLOv7: A Step Toward Smarter Precision Agriculture
Subramanya Bharathvamsi Koneti
Abstract
Precision agriculture is rapidly transforming traditional farming practices by integrating cutting-edge technologies to enhance productivity and sustainability. One of the key challenges in agriculture has been the effective identification and removal of weeds, which compete with crops for nutrients, water, and space, often resulting in reduced yields. Conventionally, herbicides have been used indiscriminately, which not only raises environmental concerns but also increases input costs for farmers. With the advent of artificial intelligence and the rise of deep learning, especially in the domain of computer vision, automated weed detection has emerged as a promising solution. Object detection and classification models can now be utilized to distinguish between weeds and healthy crops, enabling targeted herbicide application. This selective spraying approach contributes to reduced chemical usage, lower costs, and improved soil health, ultimately leading to better agricultural outcomes. In this study, we focused on implementing a deep learningbased solution to weed detection using the YOLOv7 object detection framework. The model was trained on the CropAndWeed dataset, an extensive image dataset provided by the vitro-testing community, comprising approximately 8,000 annotated images of crops and weeds under various conditions. Our approach involved preprocessing the dataset, configuring the YOLOv7 architecture for optimal performance, and evaluating the model's accuracy and efficiency in identifying weeds. The results of our experiments highlight the potential of deep learning in precision agriculture, showcasing how intelligent systems can assist farmers in making data-driven decisions. The trained model demonstrated promising accuracy in real-time weed identification, supporting the goal of precise and sustainable herbicide application. This research contributes to the growing body of work in smart farming and provides a foundation for future advancements in AI-powered agricultural tools. Through continued development and refinement, such technologies can revolutionize modern farming practices and promote environmentally responsible agriculture.