Deep Wavelet Neural Network Model for Weed Detection in Plant Images

A weed is an undesirable plant which grows along with the essential plants in a field which results in reduced crop yields and some weeds may transfer plant diseases to crops. It is necessary to identify the weeds and localizing the weeds species within crop fields and remove the weeds without harming the crop. This involves the usage of technologies, which are deep learning neural networks, machine learning techniques for creating a weed detection model using Deep Wavelet Neural Network (DWNN). DWNN variant is proposed for weed detection is a reliable methodology of agricultural image analysis. It is necessary to compose and preprocess the various dataset which includes different weed types and backgrounds. Moreover, rely on the DWNN network model which was elaborated for weed detection, and its performance was enhanced by dataset transformations that make the model more general. The performance of the algorithm in identifying the weeds and localizing the weeds within crop fields was evaluated using many datasets of crops with weeds species. And the algorithm achieved a good accuracy, mean average precision in weed identification in the real time agricultural environment, with minimum false positives. It exhibits excellent processing speeds, making it suitable for practical field applications. The findings demonstrate the potential of deep learning techniques for robust and real-time weed detection in agricultural settings. The weed management practices can be benefited from the help of machine vision and deep learning techniques and contribute towards sustainable development.