Convolutional Neural Network Models Help Effectively Estimate Legume Coverage in Grass-Legume Mixed Swards - Fujiwara, Ryo; Nashida, Hiroyuki; Fukushima, Midori; Suzuki, Naoya; Sato, Hiroko; Sanada, Yasuharu; Akiyama, Yukio (Frontiers in Plant Science, 2022)
Evaluation of legume proportion in grass-legume mixed swards is necessary for breeding and cultivation research of forage. For objective and time-efficient estimation of legume proportion, convolutional neural network (CNN) models were trained by fine-tuning GoogLeNet to estimate the coverage of timothy (TY), white clover (WC), and background (Bg) on unmanned aerial vehicle-based aerial images. The accuracies of the CNN models trained on different datasets were compared using the mean bias error and the mean average error. The models predicted the coverage with small errors when the plots in the training datasets were similar to the target plots in terms of coverage rate. The models trained on datasets of multiple plots had smaller errors than those trained on datasets of a single plot. The CNN models estimated the WC coverage more precisely than they did the TY and Bg coverages. The correlation coefficients (r) of measured coverage for aerial images vs. estimated coverage were 0.92–0.96, whereas those of scored coverage by a breeder vs. estimated coverage were 0.76–0.93. These results indicate that CNN models are helpful in estimating the legume coverage effectively.