# An attention-augmented lightweight convolutional framework for fine-grained plant leaf disease classification

## TL;DR

This paper introduces ALNet, a lightweight deep learning model that achieves high accuracy in classifying plant leaf diseases while using significantly fewer resources.

## Contribution

The novel ALNet model combines attention mechanisms and lightweight design for efficient and accurate plant leaf disease classification.

## Key findings

- ALNet achieved 99.78% accuracy on a grapevine dataset and 100% on binary classification.
- ALNet uses 18 times fewer parameters than SqueezeNet and trains faster per epoch.
- The model requires 151.98 MFLOPs and is 18 times smaller than SqueezeNet.

## Abstract

In the recent era, the growth of deep learning is inevitable. Various models such as convolutional neural networks (CNNs) and transformers are used widely in images for high classification accuracy. Since the invention of transformers, researchers have widely used novel approaches using transformers to achieve an impressive accuracy. In spite of this, this paper proposes a novel custom lightweight CNN model called Attentive and Lightweight Network (ALNet). ALNet consists of three major blocks: stem, core, and head. The core part is the novel classifier built as an inspiration from various pre-trained models such as ResNet, SENet (Squeeze and Excitation Network), EfficientNet, SqueezeNet, and ShuffleNet. The main objective is to build a model that has a high classification accuracy while reducing the number of parameters. This reduces the size of the model and hence makes it easy to deploy on cloud platforms and use in edge devices. The model was evaluated using 5-fold cross-validation on three different datasets. The primary dataset was a grapevine dataset with an accuracy of 99.78 percent and 100 percent in multi-class and binary classification respectively. To test the robustness of the model, a multi-class classification using the apple dataset achieved an accuracy of 99.95 percent and a binary classification with the cherry dataset achieved an accuracy of 100 percent. ALNet uses only 0.17 million parameters which is 18 times less parameters than the lightest model (SqueezeNet) and it takes only 14 seconds to train each epoch while pretrained models take 17–31 seconds. ALNet requires only 151.98 MFLOPs with a model size of 677.20 KB, making it approximately 18 times smaller than SqueezeNet. On the whole, ALNet is a highly accurate, lightweight model for plant leaf diseases prediction.