Prediction of plant-level tomato biomass and yield using machine learning with unmanned aerial vehicle imagery.

ABSTRACT:

Background

The objective of this study is twofold. First, ascertain the important variables that predict tomato yields from plant height (PH) and vegetation index (VI) maps. The maps were derived from images taken by unmanned aerial vehicles (UAVs). Second, examine the accuracy of predictions of tomato fresh shoot masses (SM), fruit weights (FW), and the number of fruits (FN) from multiple machine learning algorithms using selected variable sets. To realize our objective, ultra-high-resolution RGB and multispectral images were collected by a UAV on ten days in 2020's tomato growing season. From these images, 756 total variables, including first- (e.g., average, standard deviation, skewness, range, and maximum) and second-order (e.g., gray-level co-occurrence matrix features and gro

SUBMITTER: Tatsumi K 

PROVIDER: S-EPMC8281694 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature