Transforming Bangladesh agriculture: AI for precision crop disease management

Abstract

The agricultural sector encompasses a large chunk of the economy of Bangladesh as it has the necessary preconditions and factors to be suitable for agriculture. Agriculture is wholly at the whims of the environment and associated natural factors. Innovations from the time man has mastered the art of farming have allowed us to have in control some factors to ensure the desired output however there remains room for improvement and innovation especially in regards to disease detection. Currently even with a large agricultural sector, the methods for disease detection and risk management are lacking due to the inefficiencies in the system which can be very costly. To mitigate this technological innovations such as machine learning and image processing can be used to combat visible signs of disease and achieve early detection. In this paper we have explored the current options available and what can be done to make it suitable to our conditions, which ones are the best for our problem and finally we have proposed a solution we deem feasible. In our reviewed past works we have come across three models, namely Xception, VGG19 and ResNet50 which perform the best for our use cases, giving us the best results for leaf disease detection. These models have been implemented with a transfer learning approach to achieve the best results. Finally we have created a hybrid model approach combining Xception and a Vision Transformer to get the advantage of both a CNN and a Transformer to achieve the best result for our purpose.