Cluster-based photography and modeling integrated method for an efficient measurement of cassava leaf area

Jittrawan Thaiprasit, Porntip Chiewchankaset, Saowalak Kalapanulak and Treenut Saithong

PLOS ONE, 2023, vol. 18, issue 10, 1-15

Abstract: Leaf area (LA) and biomass are important agronomic indicators of the growth and health of plants. Conventional methods for measuring the LA can be challenging, time-consuming, costly, and laborious, especially for a large-scale study. A hybrid approach of cluster-based photography and modeling was, thus, developed herein to improve practicality. To this end, data on cassava palmate leaves were collected under various conditions to cover a spectrum of viable leaf shapes and sizes. A total of 1,899 leaves from 3 cassava genotypes and 5 cultivation conditions were first assigned into clusters by size, based on their length (L) and width (W). Next, 111 representative leaves from all clusters were photographed, and data from image-processing were subsequently used for model development. The model based on the product of L and W outperformed the rest (R2 = 0.9566, RMSE = 20.00). The hybrid model was successfully used to estimate the LA of greenhouse-grown cassava as validation. This represents a breakthrough in the search for efficient, practical phenotyping tools for LA estimation, especially for large-scale experiments or remote fields with limited machinery.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0287293

DOI: 10.1371/journal.pone.0287293

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