Smart Agriculture: Enhancing Crop and Weed Detection Using MobileNetV2 for Autonomous Farming Systems

Agriculture is vital to food security and economic stability, but weeds reduce crop yields by competing for sunlight, water, and nutrients. Manual weeding and herbicides are often ineffective and environmentally harmful. This study presents an autonomous weeding robot that utilizes MobileNetV2 for real-time crop and weed detection, leveraging its lightweight architecture for efficient performance on devices like the NVIDIA Jetson Nano. The system achieves 85% accuracy, a 0.72 IoU score, and 25 FPS processing speed, with field tests showing detection accuracy of up to 90% in ideal conditions. By reducing herbicide use and improving resource efficiency, this approach supports sustainable agriculture, with future advancements such as multispectral imaging and pest detection further enhancing its role in precision agriculture.