ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
This article is part of the Research TopicSmart Plant Pest and Disease Detection Machinery and Technology: Innovations for Sustainable AgricultureView all 16 articles
ADAPTIVE PREPROCESSING AND CASCADED CANNY EDGE SEGMENTATION FOR CASSAVA DISEASE IDENTIFICATION USING HYPERCAPSINCEPTION-RESNET-V2-CNN
Provisionally accepted
Sathishkumar M*
Geetha K- Excel Engineering College, Komarapalayam, India
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Cassava is one of the most widely cultivated crops worldwide, renowned for its rich natural ingredients and numerous nutritional benefits. However, the complex interdependencies among its features often pose challenges in image restoration and segmentation, particularly when identifying disease regions. In previous work, this manifests as higher false positives and the misidentification of non-relevant areas, leading to a decline in precision and accuracy. To address these issues, this study proposes an efficient artificial intelligence-powered image analysis system that leverages optimal feature selection with a HyperCapsInception-ResNet-V2-CNN model to enhance disease detection accuracy. Initially, the dataset is collected from the kaggle repository, the dataset name is Cassava Leaf Disease Classification, and it comprises 21,367 different images. The approach begins by normalizing cassava plant disease data using adaptive Gaussian Otsu thresholding. Histogram color evaluation and iterative clustering fragmentation are then applied to isolate disease variations better and improve precision. Subsequently, the Cascaded Canny Edge Segmentation (CCES) is used to segment the disease region effectively. The disease variation properties are further evaluated using the Optimal Spider Swarm Intelligence Technique (OSSIT) to reduce irrelevant feature dimensions. For classification, the HyperCapsInception-ResNet-V2-CNN model is employed to categorize cassava diseases, including Cassava Bacterial Blight (CBB), Cassava Mosaic Disease (CMD), Cassava Green Mite (CGM), Cassava Brown Streak Disease (CBSD), and regular and abnormal leaf states. Therefore, the proposed method's simulation results achieve 98.15% accuracy, 97.22% F1-score, and 96.02% precision, outperforming other traditional EfficientNetB3, AlexNet, Faster-RCNN, and InceptionV3methods.
Keywords: cassava disease, Feature optimization, Classification, enhance contrast, segmentation, Affected region, deep learning
Received: 10 Sep 2025; Accepted: 05 Nov 2025.
Copyright: © 2025 M and K. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Sathishkumar M
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