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Open AccessArticle

Automatic Wheat Ear Counting Using Thermal Imagery

1
Plant Physiology Section, Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology, University of Barcelona, Diagonal 643, 08028 Barcelona, Spain
2
AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198 Lleida, Spain
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Programa de Ingeniería Electrónica, Facultad de Ingeniería, Universidad de Ibagué, Carrera 22 Calle 67, Ibagué 730001, Colombia
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Instituto Tecnológico Agrario de Castilla y León (ITACyL), Ctra. Burgos Km. 119, 47071 Valladolid, Spain
5
Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Ctra. de la Coruña Km. 7.5, 28040 Madrid, Spain
*
Authors to whom correspondence should be addressed.
Remote Sens. 2019, 11(7), 751; https://doi.org/10.3390/rs11070751
Received: 18 January 2019 / Revised: 15 March 2019 / Accepted: 26 March 2019 / Published: 28 March 2019
(This article belongs to the Special Issue High-Resolution Thermal Imaging for Vegetation Monitoring)
Ear density is one of the most important agronomical yield components in wheat. Ear counting is time-consuming and tedious as it is most often conducted manually in field conditions. Moreover, different sampling techniques are often used resulting in a lack of standard protocol, which may eventually affect inter-comparability of results. Thermal sensors capture crop canopy features with more contrast than RGB sensors for image segmentation and classification tasks. An automatic thermal ear counting system is proposed to count the number of ears using zenithal/nadir thermal images acquired from a moderately high resolution handheld thermal camera. Three experimental sites under different growing conditions in Spain were used on a set of 24 varieties of durum wheat for this study. The automatic pipeline system developed uses contrast enhancement and filter techniques to segment image regions detected as ears. The approach is based on the temperature differential between the ears and the rest of the canopy, given that ears usually have higher temperatures due to their lower transpiration rates. Thermal images were acquired, together with RGB images and in situ (i.e., directly in the plot) visual ear counting from the same plot segment for validation purposes. The relationship between the thermal counting values and the in situ visual counting was fairly weak (R2 = 0.40), which highlights the difficulties in estimating ear density from one single image-perspective. However, the results show that the automatic thermal ear counting system performed quite well in counting the ears that do appear in the thermal images, exhibiting high correlations with the manual image-based counts from both thermal and RGB images in the sub-plot validation ring (R2 = 0.75–0.84). Automatic ear counting also exhibited high correlation with the manual counting from thermal images when considering the complete image (R2 = 0.80). The results also show a high correlation between the thermal and the RGB manual counting using the validation ring (R2 = 0.83). Methodological requirements and potential limitations of the technique are discussed. View Full-Text
Keywords: thermal images; ear counting; digital image processing; wheat thermal images; ear counting; digital image processing; wheat
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Fernandez-Gallego, J.A.; Buchaillot, M.L.; Aparicio Gutiérrez, N.; Nieto-Taladriz, M.T.; Araus, J.L.; Kefauver, S.C. Automatic Wheat Ear Counting Using Thermal Imagery. Remote Sens. 2019, 11, 751.

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