Advanced Remote Sensing Image Processing Techniques for Plant Disease Detection
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".
Deadline for manuscript submissions: 15 May 2026 | Viewed by 46
Special Issue Editor
Interests: plant disease diagnosis; animal remote sensing; cloud mask
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In agricultural production, the occurrence of pests and diseases leads to reduced yields and quality deterioration, and it can itself become a source of infection that further exacerbates these problems. Such outbreaks cause serious economic losses at production sites and may hinder sustainable, stable food production. To minimize these damages, early detection of crop diseases and pests and rapid control measures are indispensable. Detecting an outbreak at an initial stage allows targeted interventions while the damage is still minor, preventing the situation from escalating beyond control. Early detection also improves the efficiency of pest and disease management, enabling the use of only the minimum necessary pesticides and thereby reducing environmental impact.
In recent years, significant progress has been made in leveraging remote sensing technologies to efficiently monitor crop conditions over large areas. These technologies help swiftly detect early signs of anomalies in crop health and facilitate precise responses, for example, by spraying pesticides only on affected spots. However, challenges remain. Early symptoms of plant diseases and pest infestations are often very subtle, and the targets to be detected (such as small lesions or tiny pests) are extremely small, requiring high spatial resolution for reliable detection. Moreover, applying supervised machine learning in this context depends heavily on training data. It is crucial to accumulate sufficient datasets for each crop and each type of pest or disease, and to continually refine the models. Establishing a framework where experts regularly label field-collected images to keep models up to date is an important part of this process. In addition, designing user-friendly systems that elderly farmers can easily operate is essential for wider adoption of these technologies.
Although the focus of this Special Issue is on advanced remote sensing image processing techniques for plant disease detection, contributions on detection using image classification models and image segmentation models are also welcome. Moreover, accompanying technologies and applications, such as sensors for plant disease detection, are also welcome. For this Special Issue, we welcome techniques and experimental research articles on the relevant research issues.
Dr. Yu Oishi
Guest Editor
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Keywords
- plant disease diagnosis
- early detection
- drone
- monitoring
- image recognition
- object detection
- image classification
- image segmentation
- new sensors
- applications
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