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

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.