Inferring Agronomical Insights for Wheat Canopy Using Image-Based Curve Fit K-Means Segmentation Algorithm and Statistical Analysis.

ABSTRACT: Phenomics and chlorophyll fluorescence can help us to understand the various stresses a plant may undergo. In this research work, we observe the image-based morphological changes in the wheat canopy. These changes are monitored by capturing the maximum area of wheat canopy image that has maximum photosynthetic activity (chlorophyll fluorescence signals). The proposed algorithm presented here has three stages: (i) first, derivation of dynamic threshold value by curve fitting of data to eliminate the pixels of low-intensity value, (ii) second, extraction and segmentation of thresholded region by application of histogram-based K-means algorithm iteratively (this scheme of the algorithm is referred to as the curve fit K-means (CfitK-means) algorithm); and (iii) third, computation

SUBMITTER: Gupta A 

PROVIDER: S-EPMC8820929 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature