Shafik Wasswa, Tufail Ali, Liyanage De Silva Chandratilak, Awg Haji Mohd Apong Rosyzie Anna
Dig Connectivity Research Laboratory (DCRLab), P.O. Box. 600040, Kampala, Uganda.
School of Digital Science, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong, BE1410, Negara Brunei Darussalam.
Sci Rep. 2025 Jan 31;15(1):3936. doi: 10.1038/s41598-024-82857-y.
Plants are essential at all stages of living things. Plant pests, diseases, and symptoms are most regularly visible in plant leaves and fruits and sometimes within the roots. Yet, their diagnosis by experts in the laboratory is expensive, tedious, and time-consuming if the samples involve laboratory analysis. Failure to detect early plant symptoms and diseases is the core biotic cause of increased plant stresses, structure, health, reduced subsistence farming, and threats to global food security. To mitigate these problems at a social, economic, and environmental level, inappropriate herbicide application reduction and early plant disease detection and classification (PDDC) are significant solutions in this case. Advancements in transfer learning techniques have resulted in effective results in smart farming and have become extensively used in disease identification and classification research studies. This study presents a novel hybrid inception-xception (IX) using a convolution neural network (CNN). The presented model combines inception and depth-separable convolution layers to capture multiple-scale features while reducing model complexity and overfitting. In contrast to ordinary CNN architectures, it extends the network for better feature extraction, improving PDDC performance that demands diverse feature competencies. It further presents a real-time artificial intelligence (AI) application available in MATLAB, Android, and Servlet to automatically identify and classify diseases based on the leaf environment using improved CNN, machine learning (ML), and computer vision techniques. To assess the presented IX-CNN model performance, different classifiers, namely, support vector machine (SVM), decision tree (DT) and random forest (RF), were used. The experiments used six datasets, including PlantVillage, Turkey Disease, Plant Doc, Rice Disease, RoCole, and NLB datasets. Plant Doc, PlantVillage, and Turkey Disease datasets demonstrated an accuracy of 100%. Rice Disease, RoCole, and NLB attained an accuracy of 99.79%, 99.95%, and 98.64%, respectively.
植物在生物的各个阶段都至关重要。植物害虫、疾病及其症状最常出现在植物的叶子和果实上，有时也出现在根部。然而，如果样本需要实验室分析，那么专家在实验室进行诊断既昂贵又繁琐且耗时。未能及早发现植物症状和疾病是导致植物压力增加、结构受损、健康状况下降、自给农业减少以及全球粮食安全受到威胁的核心生物因素。为了在社会、经济和环境层面缓解这些问题，减少不适当的除草剂使用以及早期植物疾病检测与分类（PDDC）是解决此类问题的重要方法。迁移学习技术的进步在智能农业中取得了显著成效，并已广泛应用于疾病识别和分类研究。本研究提出了一种使用卷积神经网络（CNN）的新型混合Inception-Xception（IX）模型。所提出的模型结合了Inception和深度可分离卷积层，以捕获多尺度特征，同时降低模型复杂度和过拟合。与普通的CNN架构相比，它扩展了网络以进行更好的特征提取，提高了对具有多种特征能力要求的PDDC性能。它还展示了一个可在MATLAB、安卓和Servlet中使用的实时人工智能（AI）应用程序，该程序使用改进的CNN、机器学习（ML）和计算机视觉技术，根据叶片环境自动识别和分类疾病。为了评估所提出的IX-CNN模型的性能，使用了不同的分类器，即支持向量机（SVM）、决策树（DT）和随机森林（RF）。实验使用了六个数据集，包括植物村数据集、土耳其疾病数据集、植物文档数据集、水稻疾病数据集、RoCole数据集和NLB数据集。植物文档数据集、植物村数据集和土耳其疾病数据集的准确率达到了100%。水稻疾病数据集、RoCole数据集和NLB数据集的准确率分别为99.79%、99.95%和98.64%。
