A Real-Time Multi-Crops Classification Using Deep Learning Method For Pesticide Spraying in Sustainable Agriculture

The incorporation of state-of-the-art technologies, such as deep learning algorithms and computer vision, has paved the path for a revolutionary approach to precision agriculture, enabling farmers to reduce the environmental effects of pesticide application while simultaneously increasing crop yields. Researchers can create reliable computer vision systems that can precisely identify and classify various crop varieties in real time by utilizing deep learning techniques. Furthermore, the use of Convolutional Neural Networks (CNNs) in the field of plant image recognition and classification has been investigated, indicating the efficacy of deep learning-based methods in maximizing crop yield.The suggested system classifies various crop varieties in the field in real time by utilizing deep learning techniques. This entails using massive crop picture datasets to train CNN models so they can recognize different crops accurately under varying settings. With the ability to classify data in real-time, pesticide spraying equipment may be dynamically adjusted to ensure accurate application that is customized to meet the unique requirements of individual crop types.