Affiliations: [a] SRES’s Sanjivani College of Engineering, Kopargaon, India | [b] Aditya College of Engineering, Surampalem, India | [c] Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, India
Correspondence:
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Corresponding author: Devyani Jadhav Bhamare, SRES’s Sanjivani College of Engineering, Kopargaon, India. E-mail: [email protected].
Abstract: Economic growth of country largely depends on crop production quantity and quality. Among various crops, cotton is one of the major crops in India, where 23 percent of cotton gets exported to various other countries. To classify these cotton crops, farmers consume much time, and this remains inaccurate most probably. Hence, to eradicate this issue, cotton crops are classified using deep learning model, named LeNet in this research paper. Novelty of this paper lies in utilization of hybrid optimization algorithm, named proposed sine tangent search algorithm for training LeNet. Initially, hyperspectral image is pre-processed by anisotropic diffusion, and then allowed for further processing. Also, SegNet is deep learning model that is used for segmenting pre-processed image. For perfect and clear details of pre-processed image, feature extraction is carried out, wherein vegetation index and spectral spatial features of image are found accurately. Finally, cotton crop is classified from segmented image and features extracted, using LeNet that is trained by sine tangent search algorithm. Here, sine tangent search algorithm is formed by hybridization of sine cosine algorithm and tangent search algorithm. Then, performance of sine tangent search algorithm enabled LeNet is assessed with evaluation metrics along with Receiver Operating Characteristic (ROC) curve. These metrics showed that sine tangent search algorithm enabled LeNet is highly effective for cotton crop classification with superior values of accuracy of 91.7%, true negative rate of 92%, and true positive rate of 92%.
Keywords: Segmentation network, deep learning, anisotropic diffusion, vegetation index features, hyperspectral image
