Title: Yolov5-based convolutional feature attention neural network for plant disease classification

Authors: Jameer Gulab Kotwal; Ramgopal Kashyap; Pathan Mohd Shafi

Addresses: Amity University Chhattisgarh, Raipur, 493225, India ' ASET, Amity University Chhattisgarh, Raipur, 493225, India ' MITSOC, MIT ADT University, Pune, 412201, India

Abstract: This article employs pre-processing feature extraction and classification to identify plant diseases. Pre-processing involves rescaling, contrast enhancement and filtering based on a bilateral new fast filter (BNFF). Rescaling and contrast enhancement improve contrast, and a bilateral new quick filter filters the image without degrading the image quality. Feature extraction and classification are performed using a hybrid classification network, the Yolov5-based convolutional feature attention network (Yolov5-CFAN). YOLO V5 determines the diseased portion, and CFAN is used to perform feature extraction from the detected portion and image classification. The experimental results section compares existing models to the proposed model using accuracy, precision, recall, specificity, and F1-score. The proposed model attained an accuracy of 99.55%, a precision of 97.55%, a specificity of 99.99% and a sensitivity of 97.5%. The research also trained the suggested model to recognise early, healthy, and late blight.

Keywords: contrast features; spatial domain; denoising; overfitting; attention layer; feature map.

DOI: 10.1504/IJISTA.2024.140949

International Journal of Intelligent Systems Technologies and Applications, 2024 Vol.22 No.3, pp.237 - 259

Received: 14 Sep 2023
Accepted: 12 Nov 2023
Published online: 04 Sep 2024 *

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