Abstract:
Bangladesh is a land of agriculture, where people consumes rice as the main meal for three times a day. Rice is undoubtedly the most cultivated crop in Bangladesh. Like every other crops, rice also gets affected by a lots of diseases. These diseases differ from region to region and season to season. Although a number of implementation of different technology in agricultural field is increasing at an enormous rate, the farmers of our country still depends on the ancient techniques of disease identification. By keeping this very thing in our mind, we have conducted this research where we have tried to develop a model which can recognize rice diseases by deploying machine learning. We have worked with six main disease that is commonly seen in the paddy fields of Bangladesh. Authentic dataset of these six diseases were collected very carefully so that our model can render us the highest accuracy rate. BRRI(Bangladesh Rice Research Institute) has assisted us a lot in this matter. Three vastly popular pre-trained models of CNN such as Inception -v3, MobileNet-v1 and Resnet50, have been used to carry out this research. Necessary augmentation and scaling was done in the dataset before employing them. The research yields gratifying outcome. Hence, it proves that how effectively machine learning can collide with the agriculture. This research will pave machine learning techniques a path to enter in the agricultural sector of our country as well as help the young generation immensely who will enter into the agriculture in the future.
