Estimation of Botanical Composition in Mixed Clover-Grass Fields Using Machine Learning-Based Image Analysis.

ABSTRACT: This study aims to provide an effective image analysis method for clover detection and botanical composition (BC) estimation in clover-grass mixture fields. Three transfer learning methods, namely, fine-tuned DeepLab V3+, SegNet, and fully convolutional network-8s (FCN-8s), were utilized to detect clover fractions (on an area basis). The detected clover fraction (CF _detected ), together with auxiliary variables, viz., measured clover height (H _clover ) and grass height (H _grass ), were used to build multiple linear regression (MLR) and back propagation neural network (BPNN) models for BC estimation. A total of 347 clover-grass images were used to build the estimation model on clover fraction and BC. Of the 347 sampl

SUBMITTER: Sun S 

PROVIDER: S-EPMC7905353 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature