Machine vision-based automatic disease symptom detection of onion downy mildew
Introduction
Onion is a one of the major functional foods for health. The global onion cultivation area increased by 62.96% from 2 million ha in 2000 to 5.4 million ha in 2016, while production increased from 60.5 million tons to 89.8 million tons during the same period (Hanci, 2018). As the demand for onion increases, large-scale cultivation areas should be managed efficiently. However, the management of these areas has suffered a great setback because of crop diseases due to global warming and because of aging-related reductions in the labor force. In particular, more than 50% of the yield loss is caused by disease damage (Harvey et al., 2014), and for this reason, onion growth and disease monitoring is significant for preventing disease damage and minimizing yield loss (Lu et al., 2017). Most disease monitoring for onion cultivation was conducted manually by visual observation; this approach is not only inefficient because the area per worker is too large but also error-prone because the approach depends on skill and health of the workers (Bock et al., 2010). Therefore, the development of an image-based, automated disease observation technology is needed to replace the conventional method (Lee et al., 2016).
Recently, computer vision systems with image processing technology have been highly developed (Camargo and Smith, 2009), and machine learning approaches have enabled this technology to automatically recognize various objects in a manner similar to the human eye. In particular, deep-learning, which is described as hierarchical learning with a deep neural network layer (LeCun et al., 2015), is the most effective technique in research overall and has shown rapid progress for various intelligence tasks such as visual recognition (Krizhevsky et al., 2012, Simonyan and Zisserman, 2015), image captioning (Zhu et al., 2018), multi-image cued story generation (Kim et al., 2018), medical applications (Kourou et al., 2015), autonomous driving (Chen et al., 2018), and other similarly complex analyses with big data. It also has been applied in the field of agriculture for identification of crop diseases. Crop disease identification with deep-learning has several advantages: to separate the disease symptom from complex image backgrounds through trainable feature learning, to identify multiple instances simultaneously, and possibly to employ low-cost field systems with robust crop monitoring. In this context, the approach of various deep-learning techniques was challenged regarding the automatic diagnosis. Lu et al. (2017) exploited deep multiple instance learning to detect wheat diseases using the in-field wheat disease dataset 2017 (WDD2017) collected by mobile cameras, and the results showed an accuracy of greater than 95%. Ferentinos (2018) studied automatic recognition of plant diseases on leaves using various types of deep learning models and compared their accuracy. An openly available database was used to train the model, and the best performance had a success rate of approximately 99%. Mohanty et al. (2016) also conducted plant disease classification based on a deep convolutional neural network, and they reported that the trained model could identify 26 diseases. In addition, more than 40 studies have already been conducted employing deep learning (Kamilaris et al., 2017) for purposes such as insect detection in stored grain (Shen et al., 2018), identification of interline weeds in unmanned aerial vehicle (UAV) images (Bah et al., 2018), and fruit localization and counting (Chen et al., 2017, Rahnemoonfar and Sheppard, 2017, Sa et al., 2016). Furthermore, several big data analysis practices in agriculture and other types of research have been challenged recently to improve performance or to develop new applications. Generally, previous studies have shown high detection performance. However, in most paper, the image data were collected by capturing specific area (diseased region) or using open database which focused on target (Ferentinos, 2018). The real-time images captured in the field might be focused on other objects not only crops area such as weed (Dyrmann et al., 2017), lawn (Armstrong, 2017), or the other part in cultivation area. So, it is difficult to apply the model trained by intended images to field monitoring system for monitoring unspecified scenes continuously. In addition, it was costly to create training dataset, because it is hard to accurately diagnose most crop diseases without annotation of disease presence and region in the images by a pathologist or crop cultivation expert. Although a weakly supervised-based learning approach that can classify and localize objects with image-level annotation was used to diagnose crop disease (Lu et al., 2017), the diseased area was approximately localized without contour verification due to the ambiguous boundary. For this reason, the deep learning-based agriculture applications to diagnose disease have trouble to apply to the field as unmanned monitoring. To overcome this problem, a low-cost annotation-based approach that can be trained using real-time images captured in field is needed.
Real-time monitoring of the suspected symptom can be evaluated onsite by image-based deep-learning easier than diagnosis and can be applied on unmanned alarm systems for agriculture disease forecasting; this system can reduce the labor force and manage crops efficiently. Conventionally, monitoring of the suspected symptom was conducted by a farmer or an educated forecaster. This method is insufficient and inaccurate for monitoring a wide area with the same labor force (Chou et al., 2019) because it is dependent on the person’s ability (skill, tiredness, etc.) and the environmental conditions (weather, obstacles, etc.); this situation makes it difficult to ensure consistent and accurate monitoring, and it raises the concern about the spread of disease by human.
In this study, to reduce farmers’ burden of disease forecasting and minimizing yield loss by disease infection through early detection of suspected disease symptom, an automatic, consistent, and image-based disease monitoring system available onsite was developed. The purpose of the monitoring system is to collect consistently image data of onion cultivation by machine vision, to identify the disease symptom described by the infected portion of crop measurable using the deep neural network model, and to evaluate the system performance. Our work has advantages: (1) it is a fully automated system that includes large-scale image capturing in real-time for unmanned onion field monitoring and disease warning, and (2) a weakly supervised learning approach with localization using an optimal threshold was used, which can better detect the ambiguous boundary between diseased and healthy crop areas in an onion field.
Section snippets
Field monitoring system
The field monitoring system for monitoring onion growth and the disease symptom, as shown in Fig. 1, was developed to enable periodic capturing of onion field images in real time. The system consists of a pan, tilt, zoom (PTZ) camera (HDWC-S322MIR, Honeywell, USA) capable of high-resolution scanning and a motor system with a linear guide for controlling the vertical axis according to crop elevation. In addition, a limit sensor was installed for restricting vertical movement to prevent damage by
Image data collection
The results of the field monitoring system development and captured sample images are shown in Fig. 5. The collected images from the system were consistently transmitted to the image logging module, and then the captured large-scale cultivation image was cropped to 224x224 RGB images and stored normally. Images were automatically taken at regular intervals. Image collection was conducted under various zoom conditions to determine the zoom value suitable for disease monitoring. The zoom value
Conclusions
This study was conducted to develop an onsite real-time automatic disease monitoring system for early detection of suspected disease symptoms. Our research was focused on full automation of the process of real-time image capturing through disease symptom detection for unmanned yield forecasting. Deep learning-based approaches for disease detection have been proposed, and their results show that diseases were detected automatically with high accuracy. These studies were focused on disease
Declaration of Competing Interest
The authors declared that there is no conflict of interest.
Acknowledgments
This work was supported by research fund of Chungnam National University (2018-0608-01)
References (41)
- et al.
Weather-based decision support reduces the fungicide spraying to control onion downy mildew
Crop Prot.
(2017) - et al.
An image-processing based algorithm to automatically identify plant disease visual symptoms
Biosyst. Eng.
(2009) - et al.
Multi-task learning for dangerous object detection in autonomous driving
Inf. Sci. (Ny)
(2018) - et al.
Prioritization of pesticides in crops with a semi-quantitative risk ranking method for Taiwan postmarket monitoring program
J. Food Drug Anal.
(2019) - et al.
RoboWeedSupport - detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network
Adv. Anim. Biosci.
(2017) Deep learning models for plant disease detection and diagnosis
Comput. Electron. Agric.
(2018)- et al.
Deep learning for plant identification using vein morphological patterns
Comput. Electron. Agric.
(2016) - et al.
A review on the practice of big data analysis in agriculture
Comput. Electron. Agric.
(2017) - et al.
Machine learning applications in cancer prognosis and prediction
Comput. Struct. Biotechnol. J.
(2015) - et al.
An in-field automatic wheat disease diagnosis system
Comput. Electron. Agric.
(2017)
Detection of stored-grain insects using deep learning
Comput. Electron. Agric.
Forecasting of nonlinear time series using ANN
Futur. Comput. Informatics J.
Downy mildews of India
Crop Prot.
Computational intelligence in optical remote sensing image processing
Appl. Soft Comput. J.
Image captioning with triple-attention and stack parallel LSTM
Neurocomputing
Plant Pathology
Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging
CRC. Crit. Rev. Plant Sci.
Effect of environmental conditions and inocolum concentration on sporulation of Peronospora destructor
Agron. Res.
Cited by (50)
Label-efficient learning in agriculture: A comprehensive review
2023, Computers and Electronics in AgricultureClassification and recycling of recyclable garbage based on deep learning
2023, Journal of Cleaner ProductionLightweight tomato real-time detection method based on improved YOLO and mobile deployment
2023, Computers and Electronics in AgricultureA survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools
2023, Smart Agricultural TechnologyDetection of powdery mildew on strawberry leaves based on DAC-YOLOv4 model
2022, Computers and Electronics in AgricultureCitation Excerpt :In the later stage, PM can infect the fruit, reduce the quality of strawberries, and cause serious economic loss of farmers (Jacob et al., 2008). Thus, controlling disease progression in the early stages of PM is essential (Carisse et al., 2013; Kim et al., 2020; Bischoff et al., 2021). Early-onset indications of PM are typically described as when white spots are observed on the leaf.
An improved YOLOv5-based vegetable disease detection method
2022, Computers and Electronics in AgricultureCitation Excerpt :In the past five years, weed identification (Su et al., 2021; Jiang et al., 2020) and disease detection of plants (Bi et al., 2020; Jiang et al., 2019) using deep learning techniques by convolutional neural networks (CNNs) have been widely used. Unlike traditional machine learning-based models that manually select features, CNNs automatically extract advanced and stable features through an end-to-end pipeline, thus significantly improving the utility of plant leaf detection (Fu et al., 2021; Kim et al., 2020). However, most of the CNN models for vegetable disease detection are implemented on images with simple backgrounds, which limits their application in practical production.