Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/176066
Title: Automatic Wheat Ear Counting Using Thermal Imagery
Author: Fernandez Gallego, José Armando
Buchaillot, María Luisa
Aparicio Gutiérrez, Nieves
Nieto Taladriz, María Teresa
Araus Ortega, José Luis
Kefauver, Shawn Carlisle
Keywords: Enginyeria agronòmica
Anàlisi tèrmica
Conreu
Processament d'imatges
Agricultural engineering
Thermal analysis
Crops
Image processing
Issue Date: 28-Mar-2019
Publisher: MDPI
Abstract: 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.
Note: Reproducció del document publicat a: https://doi.org/10.3390/rs11070751
It is part of: Remote Sensing, 2019, vol. 11, num. 7, p. 751
URI: http://hdl.handle.net/2445/176066
Related resource: https://doi.org/10.3390/rs11070751
ISSN: 2072-4292
Appears in Collections:Articles publicats en revistes (Biologia Evolutiva, Ecologia i Ciències Ambientals)

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