Abstract
Plant stress is one of major issues that cause significant economic loss for growers. The labor-intensive conventional methods for identifying the stressed plants constrain their applications. To address this issue, rapid methods are in urgent needs. Developments of advanced sensing and machine learning techniques trigger revolutions for precision agriculture based on deep learning and big data. In this paper, we reviewed the latest deep learning approaches pertinent to the image analysis of crop stress diagnosis. We compiled the current sensor tools and deep learning principles involved in plant stress phenotyping. In addition, we reviewed a variety of deep learning applications/functions with plant stress imaging, including classification, object detection, and segmentation, of which are closely intertwined. Furthermore, we summarized and discussed the current challenges and future development avenues in plant phenotyping.
Highlights
Plant stress is one of the major threats to crops causing significant reduction of crop yield and quality [1]
Multispectral imaging sensor combined with drones have been applied broadly in remote sensing for plant disease detection [15], while this type of sensors is limited to a few spectral bands and sometimes cannot quantify the diseased plants severity
Despite many successful studies having been applied to crop stress detection using cheap passive imagery sensors, i.e., digital and near infrared (NIR), most of the applications require fast image processing and computational algorithms for image analysis
Summary
Plant stress is one of the major threats to crops causing significant reduction of crop yield and quality [1]. Digital imaging sensors acquire the visible range of wavelengths, i.e., RGB colored images with red, blue, and green channels to detect plant diseases. Such images provide physical attributes of the plants, such as canopy vigor, leaf color, leaf texture, size, and shape information [8]. Despite many successful studies having been applied to crop stress detection using cheap passive imagery sensors, i.e., digital and near infrared (NIR), most of the applications require fast image processing and computational algorithms for image analysis. Among the image analysis techniques, supervised methods have been popular with training data being used to develop a system Such methods include shape segmentation, feature extraction, and classifiers for stress diagnosis. Highlight the future directions that could be helpful for circumventing the challenges in plant phenotyping tasks
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