ESDNN: A novel ensembled stack deep neural network for mango leaf disease classification and detection - Gautam, Vinay; Ranjan, Ranjeet Kumar; Dahiya, Priyanka; Kumar, Anil (Multimedia tools and applications, 2024)
The mango crop production and quality are affected by several factors. Plant disease is one of the prime factors that impact crop yield. The disease impacts all parts of plants such as leaves, roots, and stems. Farmers waste most of their energy, time, and effort dealing with it manually and lead to heavy loss in the yield of the crops. Mango production is affected by several types of diseases. Prior recognition of disease would ease and reduce the diagnostic process that enhances the production of quality crops. A huge investigation has been carried out in the field of identifying and classifying leaf disease. Hence, an appropriate artificial intelligence (AI) based solution is needed to support farmers. The objective of the work is to provide an effective and efficient AI-based solution to detect and classify leaf disease earlier. Earlier disease detection in crops is the most prominent way to prevent loss of money and time. The plant leaf image is mainly used as a source to detect disease in the plant. This paper employs an ensemble stacked deep learning model to resolve the problem of automatic identification of mango-leaf diseases. In the proposed approach, initially, the images are segmented for the region of interest and input to a stack of various deep neural networks. The outcome of the deep neural network is aggregated with a machine learning model to identify leaf disease. This model is used to identify various mango leaf diseases such as Powdery mildew, Anthracnose, etc. In the experiment, the deep learning models are stacked and aggregated with machine learning to identify mango leaf diseases. The proposed model is compared with state-of-the-art models and outperforms with 98.57% accuracy.