Field phenotyping system for the assessment of potato late blight resistance using RGB imagery from an unmanned aerial vehicle

By R. Sugiura, S. Tsuda, S. Tamiya, Atsushi Itoh, Kentaro Nishiwaki, Noriyuki Murakami, Y. Shibuya, M. Hirafuji, S. Nuske. 2016

142 citations recorded by the source corpus.

In tests for field resistance of potato (Solanum tuberosum L.) to late blight, crop scientists rate the disease severity exclusively using visual examinations of infections on the leaves. However, this visual assessment is generally time-consuming and quite subjective. The objective of this study was to develop a new estimation technique for disease severity in a field using RGB imagery from an unmanned aerial vehicle (UAV). For the assessment of disease resistance of potatoes a test field was designed that consisted of 262 experimental plots on which various cultivars and lines were planted. From mid-July to mid-August in 2012, conventional visual assessment of disease severity was conducted while 11 aerial images of the field were obtained. The disease severity was estimated using an image processing protocol developed in this study. This estimation method was established so that the error of the severity estimated by image processing was minimal when compared with the visual assessment. Comparing the area under the disease progress curves (AUDPCs) calculated from the visual assessment and time series of images, the coefficient of determination was 0.77. A further experiment was conducted to validate the developed method. Eleven images of a field planted the following year were taken, and the resulting coefficient of determination was 0.73. The breeders concluded that these correlations were acceptable and that the UAV image acquisition and the disease severity estimation from the image were more efficient than the conventional visual assessments. Therefore, the developed technique based on aerial imagery allows high throughput, objective, and precise phenotyping with regard to field resistance to potato late blight.

Subfields: Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/784c08015bf716834a5d212a8b46b0dd3e79d763

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. Yang Bai
2. K. Narisawa
3. K. Peay
4. Klaus Schlaeppi

In Plant Science

1. Rodomiro Ortiz
2. Yang Bai
3. K. Narisawa
4. K. Peay
5. E. Kiers
6. Y. Onoda
7. H. D. Upadhyaya
8. C. Gowda

Citations in the corpus

• High-Throughput Field-Phenotyping Tools for Plant Breeding and Precision Agriculture (2019, methodology): “[79] developed a processing protocol based on the identification of the change of pixel color between observation times to evaluate late blight severity.”
• Core microbiomes for sustainable agroecosystems (2018, background): “Imagery from unmanned aerial vehicles (UAVs), for example, now allows physiological phenotyping and disease monitoring at the individual-plant level, revolutionizing the spatial and temporal resolution of field data 112,113 .”