Amudha M, Brindha K
School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nādu, India.
PLoS One. 2024 Dec 5;19(12):e0315031. doi: 10.1371/journal.pone.0315031. eCollection 2024.
Tomato cultivation is expanding rapidly, but the tomato sector faces significant challenges from various sources, including environmental (abiotic stress) and biological (biotic stress or disease) threats, which adversely impact the crop's growth, reproduction, and overall yield potential. The objective of this work is to build deep learning based lightweight convolutional neural network (CNN) architecture for the real-time classification of biotic stress in tomato plant leaves. This model proposes to address the drawbacks of conventional CNNs, which are resource-intensive and time-consuming, by using optimization methods that reduce processing complexity and enhance classification accuracy. Traditional plant disease classification methods predominantly utilize CNN based deep learning techniques, originally developed for fundamental image classification tasks. It relies on computationally intensive CNNs, hindering real-time application due to long training times. To address this, a lighter CNN framework is proposed to enhance with two key components. Firstly, an Elephant Herding Optimization (EHO) algorithm selects pertinent features for classification tasks. The classification module integrates a Hessian-based Optimal Brain Surgeon (HOBS) approach with a pruned Extreme Learning Machine (ELM), optimizing network parameters while reducing computational complexity. The proposed pruned model gives an accuracy of 95.73%, Cohen's kappa of 0.81%, training time of 2.35sec on Plant Village dataset, comprising 8,000 leaf images across 10 distinct classes of tomato plant, which demonstrates that this framework effectively reduces the model's size of 9.2Mb and parameters by reducing irrelevant connections in the classification layer. The proposed classifier performance was compared to existing deep learning models, the experimental results show that the pruned DenseNet achieves an accuracy of 86.64% with a model size of 10.6 MB, while GhostNet reaches an accuracy of 92.15% at 10.9 MB. CACPNET demonstrates an accuracy of 92.4% with a model size of 18.0 MB. In contrast, the proposed approach significantly outperforms these models in terms of accuracy and processing time.
番茄种植正在迅速扩张，但番茄产业面临着来自各种来源的重大挑战，包括环境（非生物胁迫）和生物（生物胁迫或疾病）威胁，这些都会对作物的生长、繁殖和总体产量潜力产生不利影响。这项工作的目标是构建基于深度学习的轻量级卷积神经网络（CNN）架构，用于对番茄植株叶片中的生物胁迫进行实时分类。该模型旨在通过使用优化方法来解决传统CNN的缺点，传统CNN资源密集且耗时，而这些优化方法可降低处理复杂度并提高分类准确率。传统的植物病害分类方法主要利用基于CNN的深度学习技术，这些技术最初是为基本图像分类任务而开发的。它依赖于计算密集型的CNN，由于训练时间长而阻碍了实时应用。为了解决这个问题，提出了一个更轻量级的CNN框架，并通过两个关键组件进行增强。首先，大象群聚优化（EHO）算法为分类任务选择相关特征。分类模块将基于海森矩阵的最优脑外科手术（HOBS）方法与剪枝极限学习机（ELM）集成在一起，在降低计算复杂度的同时优化网络参数。所提出的剪枝模型在植物村数据集上的准确率为95.73%，科恩卡方值为0.881%，训练时间为2.35秒，该数据集包含10个不同类别的番茄植株的8000张叶片图像，这表明该框架通过减少分类层中的无关连接有效地减小了模型大小至9.2Mb和参数数量。将所提出的分类器性能与现有的深度学习模型进行比较，实验结果表明，剪枝后的DenseNet模型大小为10.6MB时准确率为86.64%，而GhostNet在模型大小为10.9MB时准确率为92.15%。CACPNET在模型大小为18.0MB时准确率为92.4%。相比之下，所提出的方法在准确率和处理时间方面明显优于这些模型。
