https://pubag.nal.usda.gov/download/46603/PDF

Review: Sensing technologies for precision specialty crop production

A review on the main challenges in automatic plant disease identification based on visible range images

This paper presents a survey on methods that use digital image processing techniques to detect, quantify and classify plant diseases from digital images in the visible spectrum. Although disease symptoms can manifest in any part of the plant, only methods that explore visible symptoms in leaves and stems were considered. This was done for two main reasons: to limit the length of the paper and because methods dealing with roots, seeds and fruits have some peculiarities that would warrant a specific survey. The selected proposals are divided into three classes according to their objective: detection, severity quantification, and classification. Each of those classes, in turn, are subdivided according to the main technical solution used in the algorithm. This paper is expected to be useful to researchers working both on vegetable pathology and pattern recognition, providing a comprehensive and accessible overview of this important field of research.

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Digital image processing techniques for detecting, quantifying and classifying plant diseases.

Image pattern classification for the identification of disease causing agents in plants

Thermal and chlorophyll fluorescence imaging are powerful tools for the study of spatial and temporal heterogeneity of leaf transpiration and photosynthetic performance. The relative advantages and disadvantages of these techniques are discussed. When combined, they can highlight pre-symptomatic responses not yet apparent in visual spectrum images and provide specific signatures for diagnosis of distinct diseases and abiotic stresses. In addition, their use for diagnosis and for selection for stomatal or photosynthetic mutants, these techniques can be applied for stress tolerance screening. For example, rapid screening for stomatal responses can be achieved by thermal imaging, while, combined with fluorescence imaging to study photosynthesis, they can potentially be used to derive leaf water use efficiency as a screening parameter. A particular advantage of imaging is that it allows continuous automated monitoring of dynamic spatial variation. Examples of applications include the study of growth and development of plant lines differing in stress resistance, yield, circadian clock-controlled responses, and the possible interactions between these parameters. In the future, such dual-imaging systems could be extended with complementary techniques such as hyperspectral and blue-green fluorescence imaging. This would result in an increased number of quantified parameters which will increase the power of stress diagnosis and the potential for screening of stress-tolerant genotypes.

P.A. Viana

Papers

Fall Armyworm Damaged Maize Plant Identification using Digital Images

Identification of lesser cornstalk borer-attacked maize plants using infrared images