Revolutionizing Agriculture Machine and Deep Learning Solutions for Enhanced Crop Quality and Weed Control - Dudekula Basha; Telugu Nagendra; Gunjara Sandeep Kumar; Rotikadi Venkat Reddy; A. Emmanuel Raju (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025)
Deep learning is the nucleus in machine learning discipline which uses knowledge representation of learning. Learning can be supervised or unsupervised. Much Deep learning architecture are available which includes deep belief networks, deep neural networks and recurrent neural networks of which it has been applied to most of the fields. The commonly used applications of deep learning are vision related, audio, video, language processing, social media, medical, game and many more programs where they have produced promising accurate results comparable to and in few cases superior to human experts. Smart agriculture is an area that can benefit from the latest advances in expert systems. One of the objective is to remove the weeds by reducing the use of herbicides used, the risk of pollution of crop and water. The image of crop field is given as input training examples. By using the extracted feature, the images with weeds are detected and classified. A deep learning model is developed using convolution neural network to detect weeds with a good accuracy so that the model could be used to detect the weeds in the cucumber crop field ina shorter time.