外文摘要：Accurate diagnosis of plant diseases by the assessment of pathogen presence to reduce disease-related production loss is one of the most fundamental issues for farmers and specialists. This will improve product quality, increase productivity, reduce the use of fungicides, and reduce the final cost of agricultural production. Today, new technologies such as image processing, artificial intelligence, and deep learning have provided reliable solutions in various fields of precision agriculture and smart farm management. In this research, microscopic image processing and machine learning have been used to identify the spores of four common tomato fungal diseases. A dataset including 100 microscopic images of spores for each disease was developed, followed by the extraction of the texture, color, and shape features from the images. The classification results using random forest revealed an accuracy higher than 98%. Besides, as a reliable feature selection algorithm, the butterfly optimization algorithm (BOA) was used to detect the effective image features to identify and classify diseases. This algorithm recognized image textural features as the most effective features in the diagnosis and classification of disease spores. Considering only the eight most effective features selected with BOA resulted in an accuracy of 95% in disease detection. To further investigate the performance of the proposed method, its accuracy was compared with the accuracies of convolutional neural networks and EfficientNet as two reliable deep learning algorithms. Not only the prediction accuracy of these methods was not favorable (65 and 83.55%, respectively), they were very time-consuming. According to the findings, the proposed framework has high reliability in disease diagnosis and can help in the management of tomato fungal diseases.
外文关键词：artificial intelligence;Disease diagnosis;Butterfly optimization algorithm;Microscopic image processing;Morphological features;Tomato disease spores
作者：Banakar, Ahmad;Javidan, Seyed Mohamad;Vakilian, Keyvan Asefpour;Ampatzidis, Yiannis;Rahnama, Kamran
作者单位：Univ Florida;Tarbiat Modares Univ;Gorgan Univ Agr Sci & Nat Resources
期刊名称：MULTIMEDIA TOOLS AND APPLICATIONS
期刊影响因子：0.0
出版年份：2024
出版刊次：83(26)
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台