The temperature of the plant canopy is closely related with its transpirative status, and therefore, its stomatal conductance and cooling capacity. Ear and leaf temperature can provide useful information for monitoring crop water status, irrigation management and yield assessment. Previous studies have shown differences in temperature between ears and leaves, with higher ear temperatures than leaf temperatures observed. By employing a high resolution thermal radiometric camera for proximal imaging, temperature differences can be used for segmentation as well as for temperature estimation. This work uses thermal images taken from above the canopy at between 0.8 and 1m distance. Measurements were acquired after solar noon. The field trials were carried out in three experimental sites and two crop seasons in Spain: Aranjuez (2016/2017), Sevilla (2015/2016) and Valladolid (2016/2017). A set of 24 varieties of durum wheat in two growing conditions, irrigated and rainfed, were used to build the thermal imagery database. The algorithm uses a pipeline system to filter the low temperatures and enhance the local contrast in order to segment the ear regions in each thermal image. Finally, using the full thermal radiometric information, the algorithms provide the temperature for each ear automatically detected. The results show high correlation values between the ear temperatures manually measured (using the thermal camera software) and the ear temperatures automatically measured using an automatic image processing pipeline.
The number of ears per unit ground area, or ear density, is in most cases the main agronomic yield component of wheat. A fast evaluation of this attribute may contribute to crop monitoring and improve the efficiency of crop management practices as well as breeding programs. Currently, the number of ears is counted manually, which is time consuming. This work uses zenithal RGB images taken from above the crop canopy in natural light and field conditions. Wheat trials were carried out in two sites (Aranjuez and Valladolid, Spain) during the 2014/2015 crop season. A set of 24 varieties of durum wheat in two growing conditions with three dates of measurement were used to create the image database. The algorithm for ear counting uses three steps: (i) Laplacian frequency filter (ii) median filter (iii) Find Maxima. Although the image database was collected at the ground level, we have simulated images at lower resolutions in order to test potential application from cameras with lower resolution, such mobiles phones, action cameras (5 – 12 megapixels), or even aerial platforms (e.g. UAV from 25-50 meters). Images were resized to five different resolutions with no interpolation techniques applied. The results demonstrate high accuracy between the algorithm counts and the manual (image-based) ear counts, higher than 90% in success rate, with a decrease of <1% when images were reduced to a half of its original size, and success rates decreasing by 2.29%, 7.32%, 17.32% and 38.82% for images resized by four, eight, 16 and 32 values, respectively.
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