Novel weed detection algorithm for sesame crop using Region-Based CNN with Support Vector Machine

Farming involves various activities such as cultivation, irrigation, harvesting, and more. In these activities, searching for weeds that affect the crops all over the field is a tedious process. Weeds are unwanted plants that grow along with crops. Many weeds look very similar to crops which makes it hard for farmers to categorize weeds among crops. We have various crops all over the globe in which we are going to take sesame as the main crop, and other unwanted plants that affect sesame are considered weeds. Our solution is to detect the weeds and crops using Region-Based Convolutional Neural Networks(RCNN). In our dataset, we have 1300 images of sesame crops and other crops (weeds). We will use, Tensorflow Keras model for image classification, in which we will have background, crop, and weeds as classes. We Train our model using RCNN to get fine-tuned images and SVM to improve the model's overall prediction.