A review of imaging techniques for plant disease detection

Singh, V. K.; Sharma, Namita; Singh, Shikha

doi:10.1016/j.aiia.2020.10.002

Cited by 126 publications

(55 citation statements)

References 49 publications

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“…The main idea of the MRI technique is based on the magnetization of the atomic nuclei of the object using strong magnets and the nuclei rotate the magnetic field at variable speeds, which can be detected by the scanner and converted into usable data through Fourier transform. In the MRI technique, the hydrogen atom is used as a base atom because water is plentiful in all biological systems [3,191,204,205]. Noting the magnetic nature of this MRI [204,206] has mentioned that the low magnetic nature of the hydrogen protons which have different behaviors depending on the type of the tissues (e.g., lipids and water).…”

Section: Radio Waves Imagingmentioning

confidence: 99%

Thoughts for Foods: Imaging Technology Opportunities for Monitoring and Measuring Food Quality

Eissa¹,

Helyes²,

Romano³

et al. 2022

A Glance at Food Processing Applications

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In recent decades, the quality and safety of fruits, vegetables, cereals, meats, milk, and their derivatives from processed foods have become a serious issue for consumers in developed as well as developing countries. Undoubtedly, the traditional methods of inspecting and ensuring quality that depends on the human factor, some mechanical and chemical methods, have proven beyond any doubt their inability to achieve food quality and safety, and thus a failure to achieve food security. With growing attention on human health, the standards of food safety and quality are continuously being improved through advanced technology applications that depend on artificial intelligence tools to monitor the quality and safety of food. One of the most important of these applications is imaging technology. A brief discussion in this chapter on the utilize of multiple imaging systems based on all different bands of the electromagnetic spectrum as a principal source of various imaging systems. As well as methods of analyzing and reading images to build intelligence and non-destructive systems for monitoring and measuring the quality of foods.

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Section: Radio Waves Imagingmentioning

confidence: 99%

Thoughts for Foods: Imaging Technology Opportunities for Monitoring and Measuring Food Quality

Eissa¹,

Helyes²,

Romano³

et al. 2022

A Glance at Food Processing Applications

View full text Add to dashboard Cite

show abstract

“…[30] Plant diseases identification and early disease detection using machine learning techniques applied to crop images. [31] Hyperspectral imaging and 3D technologies for plant phenotyping from satellite to close-range sensing. [32] Deep learning for plant diseases.…”

Section: Topics Covered Year Reviewmentioning

confidence: 99%

Computer Vision, IoT and Data Fusion for Crop Disease Detection Using Machine Learning: A Survey and Ongoing Research

et al. 2021

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Crop diseases constitute a serious issue in agriculture, affecting both quality and quantity of agriculture production. Disease control has been a research object in many scientific and technologic domains. Technological advances in sensors, data storage, computing resources and artificial intelligence have shown enormous potential to control diseases effectively. A growing body of literature recognizes the importance of using data from different types of sensors and machine learning approaches to build models for detection, prediction, analysis, assessment, etc. However, the increasing number and diversity of research studies requires a literature review for further developments and contributions in this area. This paper reviews state-of-the-art machine learning methods that use different data sources, applied to plant disease detection. It lists traditional and deep learning methods associated with the main data acquisition modalities, namely IoT, ground imaging, unmanned aerial vehicle imaging and satellite imaging. In addition, this study examines the role of data fusion for ongoing research in the context of disease detection. It highlights the advantage of intelligent data fusion techniques, from heterogeneous data sources, to improve plant health status prediction and presents the main challenges facing this field. The study concludes with a discussion of several current issues and research trends.

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“…The article [15] presented many imaging techniques for plant disease detection, and the focus was on imaging techniques. The major techniques presented for plant diseases and classification are SVM, K-means, and KNN.…”

Section: Introductionmentioning

confidence: 99%

Plant Disease Detection and Classification by Deep Learning—A Review

Zhang

2021

IEEE Access

337

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Deep learning is a branch of artificial intelligence. In recent years, with the advantages of automatic learning and feature extraction, it has been widely concerned by academic and industrial circles. It has been widely used in image and video processing, voice processing, and natural language processing. At the same time, it has also become a research hotspot in the field of agricultural plant protection, such as plant disease recognition and pest range assessment, etc. The application of deep learning in plant disease recognition can avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature extraction more objective, and improve the research efficiency and technology transformation speed. This review provides the research progress of deep learning technology in the field of crop leaf disease identification in recent years. In this paper, we present the current trends and challenges for the detection of plant leaf disease using deep learning and advanced imaging techniques. We hope that this work will be a valuable resource for researchers who study the detection of plant diseases and insect pests. At the same time, we also discussed some of the current challenges and problems that need to be resolved. INDEX TERMSDeep learning, plant leaf disease detection, visualization, small sample, hyperspectral imaging.

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A review of imaging techniques for plant disease detection

Cited by 126 publications

References 49 publications

Thoughts for Foods: Imaging Technology Opportunities for Monitoring and Measuring Food Quality

Thoughts for Foods: Imaging Technology Opportunities for Monitoring and Measuring Food Quality

Computer Vision, IoT and Data Fusion for Crop Disease Detection Using Machine Learning: A Survey and Ongoing Research

Plant Disease Detection and Classification by Deep Learning—A Review

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