Publication

Abstract : Effective plant differentiation plays a vital role in agriculture, wherein this includes identifying crops and weeds in the farmland. The common method used for weed control is herbicides. However, the excessive use of herbicides will result in herbicides-resistance in weeds. An effective plant differentiation can decrease the expense of agriculture and increase crop quality, yield, and weed control. Henceforth, this research work has proposed a deep learning-based convolutional neural network to efficiently classify the plant leaves of three crops like canola, corn, and radish. In the experiment, the dataset used here is “bccr-segset,” which includes four categories like background, radish, corn, and canola. The dataset contains 30,000 images of these subclasses in four different growth stages.