{"title":"利用深度神经网络识别大麦病害","authors":"","doi":"10.1016/j.eja.2024.127359","DOIUrl":null,"url":null,"abstract":"<div><p>Plant disease negatively impacts food production and quality. It is crucial to detect and recognise plant diseases correctly. Traditional approaches do not offer a rapid and comprehensive management system for detecting plant diseases. Deep learning techniques (DL) have achieved encouraging results in discriminating patterns and anomalies in visual samples. This ability provides an effective method to diagnose any plant disease symptoms automatically. However, one of the limitations of recent studies is that in-field disease detection is underexplored, so developing a model that performs well for in-field samples is necessary. The objective of this study is to develop and investigate DL techniques for in-field disease detection of barley (<em>Hordeum vulgare</em> L.), one of the main crops in Australia, given visual samples captured at barley trials using a consumer-grade RGB camera. Consequently, A dataset was captured from test-bed trials across multiple paddocks infected with three diseases: net form net blotch (NFNB), spot form net blotch (SFNB), and scald, in various weather conditions. The collected data, 312 images (6000 × 4000 pixels), are divided into patches of 448 × 448 pixels, which are manually annotated into four classes: no-disease, scald, NFNB and SFNB. Finally, the data was augmented using random rotation and flip to increase the dataset size. The generated barley disease dataset is then applied to several well-known pre-trained DL networks such as DenseNet, ResNet, InceptionV3, Xception, and MobileNet as the network backbone. Given limited data, these methods can be trained to detect anomalies in visual samples. The results show that MobileNet, Xception, and InceptionV3 performed well in barley disease detection. On the other hand, ResNet showed poor classification ability. Moreover, Augmenting the data improves the performance of DL networks, particularly for underperforming backbones like ResNet, and mitigates the limited data access for these data-intensive networks. The augmentation step improved MobileNet performance by approximately 6 %. MobileNet achieved the highest accuracy of 98.63 % (the average of the three diseases) in binary classification and an accuracy of 93.50 % in multi-class classification. Even though classifying SFNB and NFNB is challenging in the early stages, MobileNet achieved the minimum misclassification rate among the two diseases. The results show the efficiency of this model in diagnosing barley diseases using complex data collected from the field environment. In addition, the model is lighter and comprises fewer trainable parameters. Consequently, MobileNet is suitable for small training datasets, reducing data acquisition costs.</p></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":null,"pages":null},"PeriodicalIF":4.5000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1161030124002806/pdfft?md5=2cec48c1977ddea1cd255fc4cdac2d15&pid=1-s2.0-S1161030124002806-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Barley disease recognition using deep neural networks\",\"authors\":\"\",\"doi\":\"10.1016/j.eja.2024.127359\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Plant disease negatively impacts food production and quality. It is crucial to detect and recognise plant diseases correctly. Traditional approaches do not offer a rapid and comprehensive management system for detecting plant diseases. Deep learning techniques (DL) have achieved encouraging results in discriminating patterns and anomalies in visual samples. This ability provides an effective method to diagnose any plant disease symptoms automatically. However, one of the limitations of recent studies is that in-field disease detection is underexplored, so developing a model that performs well for in-field samples is necessary. The objective of this study is to develop and investigate DL techniques for in-field disease detection of barley (<em>Hordeum vulgare</em> L.), one of the main crops in Australia, given visual samples captured at barley trials using a consumer-grade RGB camera. Consequently, A dataset was captured from test-bed trials across multiple paddocks infected with three diseases: net form net blotch (NFNB), spot form net blotch (SFNB), and scald, in various weather conditions. The collected data, 312 images (6000 × 4000 pixels), are divided into patches of 448 × 448 pixels, which are manually annotated into four classes: no-disease, scald, NFNB and SFNB. Finally, the data was augmented using random rotation and flip to increase the dataset size. The generated barley disease dataset is then applied to several well-known pre-trained DL networks such as DenseNet, ResNet, InceptionV3, Xception, and MobileNet as the network backbone. Given limited data, these methods can be trained to detect anomalies in visual samples. The results show that MobileNet, Xception, and InceptionV3 performed well in barley disease detection. On the other hand, ResNet showed poor classification ability. Moreover, Augmenting the data improves the performance of DL networks, particularly for underperforming backbones like ResNet, and mitigates the limited data access for these data-intensive networks. The augmentation step improved MobileNet performance by approximately 6 %. MobileNet achieved the highest accuracy of 98.63 % (the average of the three diseases) in binary classification and an accuracy of 93.50 % in multi-class classification. Even though classifying SFNB and NFNB is challenging in the early stages, MobileNet achieved the minimum misclassification rate among the two diseases. The results show the efficiency of this model in diagnosing barley diseases using complex data collected from the field environment. In addition, the model is lighter and comprises fewer trainable parameters. Consequently, MobileNet is suitable for small training datasets, reducing data acquisition costs.</p></div>\",\"PeriodicalId\":51045,\"journal\":{\"name\":\"European Journal of Agronomy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2024-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S1161030124002806/pdfft?md5=2cec48c1977ddea1cd255fc4cdac2d15&pid=1-s2.0-S1161030124002806-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Journal of Agronomy\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1161030124002806\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRONOMY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Agronomy","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1161030124002806","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRONOMY","Score":null,"Total":0}
Barley disease recognition using deep neural networks
Plant disease negatively impacts food production and quality. It is crucial to detect and recognise plant diseases correctly. Traditional approaches do not offer a rapid and comprehensive management system for detecting plant diseases. Deep learning techniques (DL) have achieved encouraging results in discriminating patterns and anomalies in visual samples. This ability provides an effective method to diagnose any plant disease symptoms automatically. However, one of the limitations of recent studies is that in-field disease detection is underexplored, so developing a model that performs well for in-field samples is necessary. The objective of this study is to develop and investigate DL techniques for in-field disease detection of barley (Hordeum vulgare L.), one of the main crops in Australia, given visual samples captured at barley trials using a consumer-grade RGB camera. Consequently, A dataset was captured from test-bed trials across multiple paddocks infected with three diseases: net form net blotch (NFNB), spot form net blotch (SFNB), and scald, in various weather conditions. The collected data, 312 images (6000 × 4000 pixels), are divided into patches of 448 × 448 pixels, which are manually annotated into four classes: no-disease, scald, NFNB and SFNB. Finally, the data was augmented using random rotation and flip to increase the dataset size. The generated barley disease dataset is then applied to several well-known pre-trained DL networks such as DenseNet, ResNet, InceptionV3, Xception, and MobileNet as the network backbone. Given limited data, these methods can be trained to detect anomalies in visual samples. The results show that MobileNet, Xception, and InceptionV3 performed well in barley disease detection. On the other hand, ResNet showed poor classification ability. Moreover, Augmenting the data improves the performance of DL networks, particularly for underperforming backbones like ResNet, and mitigates the limited data access for these data-intensive networks. The augmentation step improved MobileNet performance by approximately 6 %. MobileNet achieved the highest accuracy of 98.63 % (the average of the three diseases) in binary classification and an accuracy of 93.50 % in multi-class classification. Even though classifying SFNB and NFNB is challenging in the early stages, MobileNet achieved the minimum misclassification rate among the two diseases. The results show the efficiency of this model in diagnosing barley diseases using complex data collected from the field environment. In addition, the model is lighter and comprises fewer trainable parameters. Consequently, MobileNet is suitable for small training datasets, reducing data acquisition costs.
期刊介绍:
The European Journal of Agronomy, the official journal of the European Society for Agronomy, publishes original research papers reporting experimental and theoretical contributions to field-based agronomy and crop science. The journal will consider research at the field level for agricultural, horticultural and tree crops, that uses comprehensive and explanatory approaches. The EJA covers the following topics:
crop physiology
crop production and management including irrigation, fertilization and soil management
agroclimatology and modelling
plant-soil relationships
crop quality and post-harvest physiology
farming and cropping systems
agroecosystems and the environment
crop-weed interactions and management
organic farming
horticultural crops
papers from the European Society for Agronomy bi-annual meetings
In determining the suitability of submitted articles for publication, particular scrutiny is placed on the degree of novelty and significance of the research and the extent to which it adds to existing knowledge in agronomy.