Background and Problem Statement Agriculture faces numerous challenges, including crop monitoring, disease detection, soil health management, and crop yield prediction. These challenges impact food security and agricultural productivity, which are critical under the United Nations Sustainable Development Goal 2: Zero Hunger. This goal aims to end hunger, achieve food security, improve nutrition, and promote sustainable agriculture by 2030 [7][8]. Purpose The purpose of this study is to explore the application of YOLOv7, a deep learning model, in addressing these agricultural challenges. By leveraging YOLOv7's capabilities in real-time object detection, this research aims to enhance precision agriculture practices, thus contributing to the achievement of SDG 2. Methods The approach taken involves utilizing YOLOv7 for various agricultural tasks. For crop monitoring and disease detection, YOLOv7 is trained on extensive datasets of crop images to identify healthy versus diseased plants, as demonstrated in studies where it successfully detected diseases in tea leaves and other crops with high accuracy [1][2][3]. For soil health management, the model analyzes images of soil to assess quality and nutrient deficiencies [2]. In crop yield prediction, YOLOv7 processes data from multiple sources, including weather patterns and historical yield data, to forecast yields with precision [2][5]. Evaluation The application of YOLOv7 in agriculture offers significant contributions to sustainable farming practices. It enhances the ability to monitor large areas efficiently, reduces the use of pesticides by accurately detecting diseased areas or pests, and supports decisions in soil management and crop production planning. These advancements are pivotal for increasing crop yields and sustainability, thereby supporting the targets set under SDG 2. The integration of YOLOv7 into agricultural practices showcases a shift towards more data-driven and precise farming methods, which are essential for addressing food security globally. Conclusion This study demonstrates that YOLOv7 can significantly contribute to modernizing agriculture by improving the accuracy and efficiency of essential tasks such as disease detection, soil health management, and crop yield prediction. The theoretical and practical implications of this research indicate that further development and integration of advanced AI technologies in agriculture could help in overcoming some of the persistent challenges in achieving Zero Hunger. Future research should focus on refining these models for various crop types across different climatic conditions and further integrating IoT devices for real-time data collection and analysis.

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Background and Problem Statement Agriculture faces numerous challenges, including crop monitoring, disease detection, soil health management, and crop yield prediction. These challenges impact food security and agricultural productivity, which are critical under the United Nations Sustainable Development Goal 2: Zero H