Femi David, Mukunthan Manapakkam Anandan
Department of Computer Science & Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India.
Network. 2025 Aug;36(3):480-506. doi: 10.1080/0954898X.2024.2326493. Epub 2024 May 22.
Nowadays, Deep Learning (DL) techniques are being used to automate the identification and diagnosis of plant diseases, thereby enhancing global food security and enabling non-experts to detect these diseases. Among many DL techniques, a Deep Encoder-Decoder Cascaded Network (DEDCNet) model can precisely segment diseased areas from the leaf images to differentiate and classify multiple diseases. On the other hand, the model training depends on the appropriate selection of hyperparameters. Also, this network structure has weak robustness with different parameters. Hence, in this manuscript, an Optimized DEDCNet (ODEDCNet) model is proposed for improved leaf disease image segmentation. To choose the best DEDCNet hyperparameters, a brand-new Dingo Optimization Algorithm (DOA) is included in this model. The DOA depends on the foraging nature of dingoes, which comprises exploration and exploitation phases. In exploration, it attains many predictable decisions in the search area, whereas exploitation enables exploring the best decisions in a provided area. The segmentation accuracy is used as the fitness value of each dingo for hyperparameter selection. By configuring the chosen hyperparameters, the DEDCNet is trained to segment the leaf disease regions. The segmented images are further given to the pre-trained Convolutional Neural Networks (CNNs) followed by the Support Vector Machine (SVM) for classifying leaf diseases. ODEDCNet performs exceptionally well on the PlantVillage and Betel Leaf Image datasets, attaining an astounding 97.33% accuracy on the former and 97.42% accuracy on the latter. Both datasets achieve noteworthy recall, F-score, Dice coefficient, and precision values: the Betel Leaf Image dataset shows values of 97.4%, 97.29%, 97.35%, and 0.9897; the PlantVillage dataset shows values of 97.5%, 97.42%, 97.46%, and 0.9901, all completed in remarkably short processing times of 0.07 and 0.06 seconds, respectively. The achieved outcomes are evaluated with the contemporary optimization algorithms using the considered datasets to comprehend the efficiency of DOA.
如今，深度学习（DL）技术正被用于实现植物病害的自动识别与诊断，从而加强全球粮食安全，并使非专业人员也能检测这些病害。在众多深度学习技术中，深度编码器 - 解码器级联网络（DEDCNet）模型能够从叶片图像中精确分割出患病区域，以区分和分类多种病害。另一方面，模型训练依赖于超参数的恰当选择。此外，这种网络结构在不同参数下的鲁棒性较弱。因此，在本论文中，提出了一种优化的DEDCNet（ODEDCNet）模型用于改进叶片病害图像分割。为了选择最佳的DEDCNet超参数，该模型中纳入了一种全新的澳洲野犬优化算法（DOA）。DOA基于澳洲野犬的觅食特性，它包括探索和开发阶段。在探索阶段，它在搜索区域获得许多可预测的决策，而开发阶段则能在给定区域探索最佳决策。分割精度被用作每个澳洲野犬进行超参数选择的适应度值。通过配置选定的超参数，对DEDCNet进行训练以分割叶片病害区域。分割后的图像进一步输入到预训练的卷积神经网络（CNN），随后使用支持向量机（SVM）对叶片病害进行分类。ODEDCNet在植物村和槟榔叶图像数据集上表现出色，在前者上达到了惊人的97.33%的准确率，在后者上达到了97.42%的准确率。两个数据集都取得了显著的召回率、F值、骰子系数和精确率值：槟榔叶图像数据集显示的值分别为97.4%、97.29%、97.35%和0.9897；植物村数据集显示的值分别为97.5%、97.42%、97.46%和0.9901，所有这些都在分别仅为0.07秒和0.06秒的极短处理时间内完成。使用所考虑的数据集，将所取得的结果与当代优化算法进行评估，以了解DOA的效率。
