Machine Learning-Driven Classification of Indian Green Bean Varieties: CNN and AdaBoost Approaches

This study looks into the use of sophisticated learning techniques, like Convolutional Neural Networks (CNN) and AdaBoost for accurately sorting different kinds of Green Beans common in India. A carefully made group of four types of green beans - Anupama, Selection-9, Pusa Parvati, and Contender were used to teach and check the models. The study got a really good correct rate of 94.82%. It showed that the models can tell fine differences between these different plant types. Precision, recall, and F1-score are measures of how well the CNN and AdaBoost models sort Green Bean types. They show that these two tools give good results. The suggested results prove that our new models are better and more advanced. This research's results are very important for precision farming and other farm activities in India. Using these models in real situations could help make farming better by allowing farmers to make smart choices and encouraging ways of growing crops that last a long time. But, it needs to work on making models better for different conditions. And also look at ways that they can classify even more types of Green Beans so they are useful in farming areas.