Automatic Plant Disease Detection System Using Advanced Convolutional Neural Network-Based Algorithm

Abstract

With technology innovations such as Artificial Intelligence (AI) and Internet of Things (IoT), unprecedented applications and solutions to real world problems are made possible. Precision agriculture is one such problem which is aimed at technology driven agriculture. So far, the research on agriculture and usage of technologies are at government level to reap benefits of technologies in crop yield prediction and finding the cultivated areas. However, the fruits of technologies could not reach farmers. Farmers still suffer from plenty of problems such as natural calamities, reduction in crop yield, high expenditure and lack of technical support. Plant diseases is an important problem faced by farmers as they could not find diseases early. There is need for early plant disease detection in agriculture. From the related works, it is known that deep learning techniques like Convolutional Neural Network (CNN) is best used to process image data to solve real world problems. However, as one size does not fit all, CNN cannot solve all problems without exploiting its layers based on the problem in hand. Towards this end, we designed an automatic plant disease detection system by proposing an advanced CNN model. We proposed an algorithm known as Advanced CNN for Plant Disease Detection (ACNN-PDD) to realize the proposed system. Our system is evaluated with PlantVillage, a benchmark dataset for crop disease detection result, and real-time dataset (captured from live agricultural fields). The investigational outcomes showed the utility of the proposed system. The proposed advanced CNN based model ACNN-PDD achieve 96.83% accuracy which is higher than many existing models. Thus our system can be integrated with precision agriculture infrastructure to enable farmers to detect plant diseases early.