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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Gopalakrishna, Kavitha Magadi
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Computer Science & IT Electrical & Electronics Engineering Engineering

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A prediction of coconut and coconut leaf disease using MobileNetV2 based classification Gopalakrishna, Kavitha Magadi; Lingaraju, Raviprakash Madenur
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2834-2844

Abstract

This research is aimed at effectively predicting coconut and coconut leaf disease using enhanced MobileNetV2 and ResNet50 methods. The stages involved in this implemented method are data collection, pre-processing, feature extraction, and classification. At first, data is collected from coconut and coconut leaf datasets. Gaussian filter and data augmentation techniques are applied on these images to eliminate noise during the pre-processing phase. Then, features are extracted using ResNet50 technique, while the diseases are classified using MobileNetV2 approach. In comparison to the existing methods namely, EfficientDet-D2, DL-assisted whitefly detection model (DL-WDM), and modified inception net-based hyper tuning support vector machine (MIN-SVM), the proposed method achieves superior classification values with 99.99% and 99.2% accuracy for coconut leaf and for coconuts, respectively.
Gradient-based stochastic depth with convolutional neural network for coconut tree leaf disease classification Gopalakrishna, Kavitha Magadi; Lingaraju, Raviprakash Madenur; Jayachandra, Ananda Babu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1155-1165

Abstract

The coconut palm (Cocos nucifera) is vital plantation crop, valued for their different uses, ranging from their fruit to its trunk. In recent times, it has been observed that many coconut trees are affected by diseases that reduce production and weaken the strength of the coconut. The classification of coconut leaf diseases is challenging because of intra-class and inter-class variability. This research introduces the gradient-based stochastic depth (GSD) with convolutional neural network (CNN) technique to coconut leaf disease classification to overcome these challenges. The GSD technique is incorporated into every layer of the CNN, where it calculates the probability using gradient magnitudes and skips layers that contribute minimally to the classification. The images are segmented using the GrabCut segmentation algorithm, which isolates the leaf from the background using graph-based segmentation, helping to differentiate between various disease classes. The GSD with CNN algorithm obtains an accuracy of 96.42%, precision of 96.15%, recall of 95.87%, and F1-score of 95.93%, while comparing with existing algorithms.
Co-Authors Jayachandra, Ananda Babu Lingaraju, Raviprakash Madenur