Plant Disease Classification

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Plant disease classification studies how to identify diseases from leaf, canopy, and other crop images, helping growers detect problems and guide treatment. Common approaches include convolutional neural networks, vision transformers, ensembles, and vision-language models, with transfer learning, few-shot learning, and semi-supervised methods used when labeled examples are scarce. Lightweight models, distillation, and quantization target mobile and edge devices, while attention maps, segmentation, and multimodal sensing can help explain or ground predictions. A central challenge is that strong results on controlled datasets often do not transfer to varied field conditions, where differences in lighting, crops, labels, and data collection can undermine accuracy and reliability.

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62 papers11 papers in the last four weeks, up 38% on the four weeks before. 0.1% of all new papers.

CLAP Convolutional Lightweight Autoencoder for Plant Disease Classification
Feb 21, 2026Asish Bera, Subhajit Roy, Sudiptendu BanerjeeDepthwise Separable Convolution Plant Disease Classification
Department of Computer Science and Information Systems, Birla Institute of Technology and Science Pilani, Rajasthan, India
U-FedTomAtt: Ultra-lightweight Federated Learning with Attention for Tomato Disease Recognition
Feb 18, 2026Romiyal George, Sathiyamohan Nishankar, Selvarajah Thuseethan +4Lightweight Neural Networks Federated Learning
Department of Computer Engineering, University of Peradeniya, Sri Lanka. · Faculty of Science and Technology, Charles Darwin University, Australia. · Canadian Institute for Cybersecurity, University of New Brunswick, Canada. +1

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CLAP Convolutional Lightweight Autoencoder for Plant Disease Classification

Asish Bera, Subhajit Roy, Sudiptendu Banerjee

Department of Computer Science and Information Systems, Birla Institute of Technology and Science Pilani, Rajasthan, India

Depthwise Separable Convolution Plant Disease Classification Convolutional Autoencoder Convolutional Neural Networks Lightweight CNNs

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Convolutional neural networks have remarkably progressed the performance of distinguishing plant diseases, severity grading, and nutrition deficiency prediction using leaf images. However, these tasks become more challenging in a realistic in-situ field condition. Often, a traditional machine learning model may fail to capture and interpret discriminative characteristics of plant health, growth and diseases due to subtle variations within leaf subcategories. A few deep learning methods have used additional preprocessing stages or network modules to address the problem, whereas several other methods have utilized pre-trained backbone CNNs, most of which are computationally intensive. Therefore, to address the challenge, we propose a lightweight autoencoder using separable convolutional layers in its encoder decoder blocks. A sigmoid gating is applied for refining the prowess of the encoders feature discriminability, which is improved further by the decoder. Finally, the feature maps of the encoder decoder are combined for rich feature representation before classification. The proposed Convolutional Lightweight Autoencoder for Plant disease classification, called CLAP, has been experimented on three public plant datasets consisting of cassava, tomato, maize, groundnut, grapes, etc. for determining plant health conditions. The CLAP has attained improved or competitive accuracies on the Integrated Plant Disease, Groundnut, and CCMT datasets balancing a tradeoff between the performance, and little computational cost requiring 5 million parameters. The training time is 20 milliseconds and inference time is 1 ms per image.

Latest papers

CLAP Convolutional Lightweight Autoencoder for Plant Disease Classification

U-FedTomAtt: Ultra-lightweight Federated Learning with Attention for Tomato Disease Recognition