Predicting yield attributes of maize through image processing
Sandhya P, Patil SG, Radha M, Djanaguiraman M, Dheebakaran GA and Gunasekaran
Currently, the yield attributes of maize ears (such as ear length, kernel count, kernel weight, and so on) are often assessed by hand throughout the breeding process, which necessitates a large number of workers. Furthermore, subjective mistakes are difficult to prevent, and manual measuring efficiency is quite poor. The method described in this work can quickly assess yield attributes linked to breeding traits of numerous maize ears, significantly improving maize variety evaluation efficiency. From photographs of ears taken from field trial plots, a low-cost ear digital imaging system was developed that offers estimations of ear and kernel characteristics such as ear number and size, kernel number and size, and kernel weight. Image J, an open-source program, is used here to process the images using a script that runs in batch mode. The total kernel number was determined from the number of visible kernels on the picture and the average kernel size was used to calculate kernel weight. Ground truth measurements and data obtained by image processing have an excellent agreement in terms of accuracy and precision. The procedure also entails utilizing a mobile camera to picture scattered kernel samples and counting them using the software. Results demonstrate that this is a fast (less than a minute per sample) and reliable approach that may be extensively used for estimating yield attribute and kernel counting.
How to cite this article:
Sandhya P, Patil SG, Radha M, Djanaguiraman M, Dheebakaran GA and Gunasekaran. Predicting yield attributes of maize through image processing. The Pharma Innovation Journal. 2021; 10(10S): 761-767.