Article: Grape cluster and disease detection with hybrid fuzzy residual maxout network Journal: International Journal of Signal and Imaging Systems Engineering (IJSISE) 2024 Vol.13 No.4 pp.244 - 262 Abstract: This paper proposes a novel deep-learning method for grape cluster detection. Primarily, the grape cluster image is pre-processed using a bilateral filter to remove noise and the image is enhanced by the decorrelation stretching. Then, segmentation is performed using Mobile U-Net to segment the required regions. Later, features from the segmented images are extracted using different feature extractors. Afterwards, features fed into the hybrid fuzzy residual maxout network (HFRMN) model for the detection of grape clusters. Here, the HFRMN model is designed by the incorporation of deep maxout network (DMN), deep residual network (DRN), and the Fuzzy concept. Finally, disease detection is accomplished by utilising the proposed HFRMN. Moreover, HFRMN attained superior values of 90.0% for true positive rate (TPR), 91.3% for true negative rate (TNR), 91.4% for accuracy, 92.4% for F1-score, 71.3% for computational efficiency, 1.04% for mean squared error (MSE), and 89.6% for mean average precision (MAP). Inderscience Publishers - linking academia, business and industry through research

Title: Grape cluster and disease detection with hybrid fuzzy residual maxout network

Authors: Rajkumar Bhimrao Pawar; Madhuri Rao

Addresses: Department of Computer Engineering, Gharda Institute of Technology, A/P Lavel Tal Khed Dist Ratnagiri, 415708, Maharashtra, India; Department of Information Technology, Thadomal Shahani Engineering College, Off Linking Rd., Bandra West, Mumbai, University of Mumbai, Maharashtra, 400050, India ' Department of Artificial Intelligence and Data Science, Thadomal Shahani Engineering College, Off Linking Rd, Bandra West, Mumbai, University of Mumbai, Maharashtra, 400050, India

Abstract: This paper proposes a novel deep-learning method for grape cluster detection. Primarily, the grape cluster image is pre-processed using a bilateral filter to remove noise and the image is enhanced by the decorrelation stretching. Then, segmentation is performed using Mobile U-Net to segment the required regions. Later, features from the segmented images are extracted using different feature extractors. Afterwards, features fed into the hybrid fuzzy residual maxout network (HFRMN) model for the detection of grape clusters. Here, the HFRMN model is designed by the incorporation of deep maxout network (DMN), deep residual network (DRN), and the Fuzzy concept. Finally, disease detection is accomplished by utilising the proposed HFRMN. Moreover, HFRMN attained superior values of 90.0% for true positive rate (TPR), 91.3% for true negative rate (TNR), 91.4% for accuracy, 92.4% for F1-score, 71.3% for computational efficiency, 1.04% for mean squared error (MSE), and 89.6% for mean average precision (MAP).

Keywords: grape cluster; bilateral filter; decorrelation stretching; DMN; deep maxout network; DRN; deep residual network.

DOI: 10.1504/IJSISE.2024.146211

International Journal of Signal and Imaging Systems Engineering, 2024 Vol.13 No.4, pp.244 - 262

Received: 27 May 2024
Accepted: 24 Sep 2024
Published online: 12 May 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: Grape cluster and disease detection with hybrid fuzzy residual maxout network

Keep up-to-date