Image Processing based Robotic Car for Agricultural Ploughing using Machine Learning Approach - Deepak, A.; Sharma, Kamal; Naik, Guruprasad Ramakrishna; Shah, Devangkumar Umakant; Modi, Gajanand; Poonguzhali, S.; Singh, Durgeshwar Pratap (International Journal of Intelligent Systems and Applications in Engineering, 2023)
For a significant amount of time, agriculture was conducted in a traditional way; only more recently have mechanical technology been utilized to aid. The adoption of intelligent farming practices made possible by the development of robotic technology and sensors is the area on which the experts are concentrating their efforts. With a focus on a heterogeneous robotic system, we provide improved algorithms in this study for both the categorization of fields and the detection of viruses in leaf samples. The basic machine learning approach known as k-means clustering was used to identify the field and image processing techniques were used to the plant leaves. This was done in order to determine the proportion of affected crops. In order to provide a variety of crops utilizing the mixed cropping approach, which has an advantage over other farming techniques, the agricultural sector has been classified. Because of this, agriculture has been categorized. Early diagnosis of a disease may aid in the creation of more effective preventative measures while it is still in its early stages. We have skillfully combined 3,150 photos of crop illnesses for three different types of crops using a variety of tried-and-true techniques. This study's main goals are to do a qualitative examination of infection detection algorithms and to offer additional details about the proposed work's possible uses in intelligent farming.