CM-MLP: hybrid convmixer-deep MLP architecture for enhanced identification of corn and apple leaf diseases

This study aims to identify diseases impacting the agricultural sector, specifically focusing on corn and apple leaves. We propose a novel hybrid ConvMixer and Deep MLP architecture called CM-MLP that combines the strengths of ConvMixer with various Deep Multi-layer Perceptron (MLP), like MLP-Mixer,...

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Vydané v:	Neural computing & applications Ročník 37; číslo 17; s. 10757 - 10769
Hlavní autori:	Li, Li-Hua, Tanone, Radius
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Springer London 01.06.2025 Springer Nature B.V
Predmet:	Accuracy Artificial Intelligence Classification Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Computer vision Corn Data Mining and Knowledge Discovery Deep learning Hypotheses Image Processing and Computer Vision Leaves Multilayer perceptrons Multilayers Neural networks Plant diseases Probability and Statistics in Computer Science S.I.: Timely Advances of Deep Learning with applications and Data Driven Modeling Special Issue on Timely Advances of Deep Learning with applications and Data Driven Modeling Unmanned aerial vehicles Gradient Centralization MLP-Mixer gMLP ConvMixer FNet
ISSN:	0941-0643, 1433-3058
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Shrnutí:	This study aims to identify diseases impacting the agricultural sector, specifically focusing on corn and apple leaves. We propose a novel hybrid ConvMixer and Deep MLP architecture called CM-MLP that combines the strengths of ConvMixer with various Deep Multi-layer Perceptron (MLP), like MLP-Mixer, gMLP, and FNet, to tackle the classification challenges in this context. Notably, the final block of the Deep MLP can accommodate both MLP-Mixer and gMLP, as well as other designs. The ConvMixer architecture employs depthwise and pointwise convolution techniques to enrich the input image features, which are subsequently processed by the Deep MLP block to classify corn and apple leaf diseases comprehensively. In this study, we implement gradient centralization (GC) to enhance training performance. The results reveal that training with GC leads to a robust CM-MLP model, which achieves an impressive average accuracy exceeding 99.00 and proves effective in classifying diseases in corn and apple leaves.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	0941-0643 1433-3058
DOI:	10.1007/s00521-024-10774-2