Maize Diseases Image Identification and Classification by Combining CNN with Bi-Directional Long Short-Term Memory Model

• Main Authors: Hasan, Md. Jahid, Hasan, Md Jahid, Alom, Md Shahin, Dina, Umme Fatema, Moon, Mahmudul Hasan
• Format: Conference Proceeding
• Language: English
• Subjects: BiLSTM; CNN; Computer architecture; Diseases; Feature extraction; Image recognition; Logic gates; Maize diseases; Microprocessors; Recurrent Neural Network; RNN for image classification; Training
• ISSN: 2642-6102
• DOI: 10.1109/TENSYMP50017.2020.9230796

Maize is one of the most important agricultural crops in the world which is affected by various pathogenetic diseases. These disease lead to low productivity and huge loss to the farmers. For this reason, detection of these disease in early stage by recognizing its symptomatic patterns will be beneficial for farmers. CNN based technique are widely used for classifying such symptoms which can detect all important features of an image. In this paper we have discussed a hybrid network by combining CNN with Bidirectional Long Short-Term Memory (BiLSTM) model is to detect and classify nine different disease of maize plant which are frequently affected diseases in this subcontinent. Here, BiLSTM has been used to create correlation among extracted features and to accelerate the recognition accuracy. For this reason, we have created a dataset with 29065 maize disease images where 80% sample were used for training and achieved accuracy 99.02%. This ensures that the model is very reliable for AI based disease recognition system and may contribute to increase the productivity of crops.