Weed detection and classification in sesame crops using region-based convolution neural networks

Farming has many moving parts, including planting, watering, harvesting, and more. One of their most complex and time-consuming is keeping an eye out for and controlling weeds that might ruin a harvest. Unwanted weeds cause decreased crop productivity by competing with desired agricultural plants for water, sunshine, and soil nutrients. This research aims to use Region-Based Convolutional Neural Networks (RCNNs) to detect weeds in photographs of sesame crops and then classify them into their respective weed families. Object detection is a promising use of deep learning, and the suggested method takes advantage of RCNNs, a prominent method. By applying RCNNs to sesame crop images, we could accurately identify the presence of weeds, achieving an impressive detection accuracy of 96.84%. This high accuracy can significantly aid farmers in pinpointing areas of their fields that require immediate attention and weed management strategies. Furthermore, after successfully detecting weeds, we classified them into different types. This classification step is crucial as different weed species require specific control measures. Our proposed methodology achieved an outstanding weed classification accuracy of 97.79%. By correctly categorizing weeds, farmers better understand the weed composition in their fields, making it easier to use targeted control methods and lessen the use of possibly dangerous chemicals with a wide range of effects.