Image-based disease detection and classification in Indian apple plant species by using deep learning - Wani, Sidrah Fayaz; Ashraf, Arselan; Sophian, Ali (Engineering Research Group Universitas Muhammadiyah Surakarta Indonesia, 2022)
Plant diseases pose a significant danger to global food security, yet their timely diagnosis remains difficult across many regions of the world due to the lack of infrastructure. Traditional farming methods are insufficient to address the impending global food crises. As a result, agricultural product growth is critical, and new techniques and methods are required for efficient and sustainable farming practices that balance the supply chain according to customer demand. Even though India is one of the most agriculturally dependent countries, it nevertheless suffers from various agricultural shortages. Plant diseases that go unnoticed and untreated are one such deprivation. Developing a smart technique for plant disease detection is explored in this research. For this, we used deep learning to develop an intelligent system for image-based disease detection in Indian apple plant species. Specifically, this model uses a convolution neural network to identify diseases in apple plants. On the basis of 70% - 30% and 80 % - 20% dataset partition, the proposed model obtained an accuracy of 97.5 % and 98.4 %, respectively. The results obtained from this study illustrate the productive exploration along with the utility of the proposed model for future research by implementing various deep learning models and incorporating additional modules that will provide cure and preventative measures for the detected diseases.