GANet: A Lightweight Group Attention Network for Plant Disease Identification on Embedded Systems

Anubhab Tripathi; Bibek Das; Asfak Ali; Sheli Shinha Chaudhuri

cs.LGOct 21, 2026

GANet: A Lightweight Group Attention Network for Plant Disease Identification on Embedded Systems

Authors: Anubhab Tripathi, Bibek Das, Asfak Ali, Sheli Shinha Chaudhuri

Organizations: Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India

Abstract

Global warming and climate change are major catalysts for plant diseases, significantly affecting food production worldwide. Addressing this issue requires continuous monitoring and early detection of plant diseases. While recent advancements in deep learning offer potential solutions, most models demand high processing power and struggle to perform efficiently on low-end electronic hardware. Furthermore, many existing works focus on specific crop diseases, limiting their broader applicability. This paper presents a lightweight Group Equivariance Attention Network (GANet) designed for the prediction and monitoring of 38 types of plant diseases. GANet introduces a novel Group Attention block and Group-Wise Feature Fusion block, making it both sparse and efficient. With only 0.001M parameters, the model achieves an impressive 99.99% accuracy on the PlantVillage dataset. Additionally, GANet is optimized for deployment on edge devices such as the Raspberry Pi 4b and Jetson Nano, where it reaches 5 FPS and 11 FPS, respectively, demonstrating its potential for real-time plant disease monitoring on low-power devices.

Figures & tables

Fig. 1: Overall Block Diagram of the Leaf Disease Multi-class Classification Model.

Fig. 2: Block Diagram of Feature Fusion Block

Fig. 3: Block Diagram of Attention Mechanism system.

Configuration	Specification
Optimizer	Adam
Loss function	CrossEntropyLoss
Initial learning rate	0.001
Batch size	128
Learning rate schedule	ReduceLRonPlateau
Training epochs	100

TABLE I: Experimental Parameter Configuration

Model	No. of Parameters	Size (MB)	FLOP	Depth	Accuracy	Precision	Recall	F1-Score	AuC
VGG-19	143,667,240	549	1.96E+10	19	0.96	0.94	0.92	0.92	0.97
ResNet-152	58,772,356	384	1.13E+10	152	0.95	0.95	0.91	0.93	0.96
DenseNet-201	20,242,984	80	4.37E+09	201	0.94	0.95	0.92	0.93	0.94
MobileNetV2	3,538,984	14	3.14E+08	53	0.90	0.94	0.90	0.88	0.89
ResNet-50	25,636,712	89	1.10E+10	50	0.90	0.90	0.89	0.88	0.89
VGG-16	138,357,544	528	1.58E+10	23	0.96	0.94	0.92	0.90	0.93

TABLE II: Comparison of results between Our and other studies on the PlantVillage dataset.

Fig. 5: GradCAM Images of two leaves present in the dataset.

Fig. 6: Multiclass ROC curves for all the classes along with their AUCs.

Fig. 7: t-SNE Visualization of all 38 classes of the PlantVillage dataset.

Plant Disease Classification

Plant disease classification uses image and sensor data to identify crop diseases and support timely, targeted agricultural intervention.

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Energy-Efficient Plant Monitoring via Knowledge Distillation

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LIRMM, Univ Montpellier, CNRS, Inria, Montpellier, France · IMT Atlantique, Brest, France. Polytechnique Montréal, Canada · Institut National de l’Audiovisuel, Paris, France+2

Plant Disease Classification Energy-Efficient ML Knowledge Distillation

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Recent advances in large-scale visual representation learning have significantly improved performance in plant species and plant disease recognition tasks. However, state-of-the-art models, often based on high-capacity vision transformers or multimodal foundation models, remain computationally expensive and difficult to deploy in resource-constrained environments such as mobile or edge devices. This limitation hinders the scalability of automated biodiversity monitoring and precision agriculture systems, where efficiency is as critical as accuracy. In this work, we investigate knowledge distillation as an effective approach to transfer the representational capacity of large pretrained models into smaller, more efficient architectures. We focus on plant species and disease recognition, and conduct an extensive empirical study on two challenging benchmarks: Pl@ntNet300K-v2 and Deep-Plant-Disease. We evaluate four representative architectures, including two ConvNeXt models and two vision transformers, under multiple training regimes: from-scratch training and pretrained initialization, each with and without distillation. In total, we train and evaluate 70 models. Our results show that knowledge distillation consistently improves performance across tasks and architectures. Distilled models are able to match the performance of significantly larger models while maintaining substantially lower computational cost. These findings demonstrate the potential of knowledge distillation techniques to enable efficient and scalable deployment of plant recognition systems in real-world environmental applications.

Abstract

Plant Disease Classification

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