PDF(Pubmed)
Abstract：
Plant diseases annually cause damage and loss of much of the crop, if not its complete destruction, and this constitutes a significant challenge for farm owners, governments, and consumers alike. Therefore, identifying and classifying diseases at an early stage is very important in order to sustain local and global food security. In this research, we designed a new method to identify plant diseases by combining transfer learning and Gravitational Search Algorithm (GSA). Two state-of-the-art pretrained models have been adopted for extracting features in this study, which are MobileNetV2 and ResNe50V2. Multilayer feature extraction is applied in this study to ensure representations of plant leaves from different levels of abstraction for precise classification. These features are then concatenated and passed to GSA for optimizing them. Finally, optimized features are passed to Multinomial Logistic Regression (MLR) for final classification. This integration is essential for categorizing 18 different types of infected and healthy leaf samples. The performance of our approach is strengthened by a comparative analysis that incorporates features optimized by the Genetic Algorithm (GA). Additionally, the MLR algorithm is contrasted with K-Nearest Neighbors (KNN). The empirical findings indicate that our model, which has been refined using GSA, achieves very high levels of precision. Specifically, the average precision for MLR is 99.2%, while for KNN it is 98.6%. The resulting results significantly exceed those achieved with GA-optimized features, thereby highlighting the superiority of our suggested strategy. One important result of our study is that we were able to decrease the number of features by more than 50%. This reduction greatly reduces the processing requirements without sacrificing the quality of the diagnosis. This work presents a robust and efficient approach to the early detection of plant diseases. The work demonstrates the utilization of sophisticated computational methods in agriculture, enabling the development of novel data-driven strategies for plant health management, therefore enhancing worldwide food security.
摘要：
植物病害每年都会造成大部分作物的破坏和损失，如果不是完全毁灭,这对农场主来说是一个巨大的挑战，政府,和消费者一样。因此,早期识别和分类疾病对于维持当地和全球粮食安全非常重要。在这项研究中,结合迁移学习和引力搜索算法（GSA），设计了一种识别植物病害的新方法。在这项研究中，采用了两种最先进的预训练模型来提取特征，分别是MobileNetV2和ResNe50V2。在这项研究中应用了多层特征提取，以确保从不同抽象级别对植物叶片进行精确分类。然后将这些功能连接并传递给GSA以进行优化。最后,优化的特征被传递到多项式逻辑回归(MLR)进行最终分类。这种整合对于对18种不同类型的感染和健康叶片样品进行分类至关重要。通过比较分析，结合了遗传算法（GA）优化的功能，增强了我们方法的性能。此外,MLR算法与K近邻(KNN)进行了对比。实证结果表明，我们的模型，它已经用GSA改进了，达到非常高的精度水平。具体来说,MLR的平均精度为99.2%，而KNN则为98.6%。由此产生的结果大大超过了GA优化功能所实现的结果，从而突出了我们建议战略的优越性。我们研究的一个重要结果是，我们能够将特征数量减少50%以上。这种减少在不牺牲诊断质量的情况下极大地降低了处理要求。这项工作为植物病害的早期检测提供了一种可靠有效的方法。这项工作展示了复杂的计算方法在农业中的应用，能够开发新的数据驱动的植物健康管理策略，从而加强全球粮食安全。
