Intelligent Crop Disease Detection and Classification Using Deep Convolution Neural Network with Honey Badger Algorithm on Image Data

Postdoctoral Fellow, Perdana University, Kuala Lumpur 50490, Malaysia; Amity University Madhya Pradesh, Gwalior 474005, India ,Daniel Daniel,Faculty Of Applied Science And Technology, Perdana University, Kuala Lumpur 50490, Malaysia ,Open Dist Learning Centre, Perdana University, Kuala Lumpur 50490, Malaysia ,Azween Abdullah,Valliappan Raju

doi:10.54216/jisiot.140215

Postdoctoral Fellow, Perdana University, Kuala Lumpur 50490, Malaysia; Amity University Madhya Pradesh, Gwalior 474005, India , Daniel Daniel + Show 4 more

https://doi.org/10.54216/jisiot.140215

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Cotton is the most significant cash crop in India. Each year cotton production is decreasing because of the attack of the disease. Plant diseases are usually produced by pathogens and pest insects and reduce the yield to a large scale if not controlled in time. The hour requires an effective plant disease diagnosis system that can assist the farmers in their farming and cultivation. Nevertheless, cotton production is harmfully affected by the presence of viruses, pests, bacterial pathogens, and so on. For the past decade or so, numerous image processing or deep learning (DL)--based automated plant leaf disease recognition techniques have been established but, unluckily, they infrequently focus on the cotton leaf diseases. Therefore, this article develops an Intelligent Detection and Classification of Cotton Leaf Diseases Using Transfer Learning and the Honey Badger Algorithm (IDCCLD-TLHBA) model with Satellite Images. The proposed IDCCLD-TLHBA technique intends to determine and classify various kinds of cotton leaf diseases using satellite imagery. In the IDCCLD-TLHBA technique, the wiener filtering (WF) model is used to reduce noise and enhance image quality for subsequent analysis. For feature extraction, the IDCCLD-TLHBA technique applies the MobileNetV2 model to capture relevant features from the satellite images while maintaining computational efficiency. In addition, the stacked long short-term memory (SLSTM) method is employed for the classification and recognition of cotton leaf diseases. Eventually, the honey badger algorithm (HBA) is used to optimally select the parameters involved in the SLSTM model to ensure a better configuration of the network to enhance results. The performance validation of the IDCCLD-TLHBA method is carried out against the benchmark dataset and the stimulated results highlight the better results of the IDCCLD-TLHBA model across the existing techniques.

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