Deep Learning Applied to Phenotyping of Biomass in Forages with UAV-Based RGB Imagery

Sensors

Mateus Santos¹  Camilo Carromeu¹  Sanzio Barrios¹  Rosangela Simeão¹  Liana Jank¹  Cacilda Valle¹  LúcioAndré de Castro Jorge²  Wellington Castro³  Lucas Rodrigues³  Edson Matsubara³  Caio Polidoro³  José Marcato Junior⁴  Eloise Silveira⁴  Wesley Gonçalves⁴  LucasPrado Osco⁵ 

[1] Embrapa Beef Cattle, Brazilian Agricultural Research Corporation, Campo Grande 79106550, MS, Brazil;Embrapa Instrumentation, São Carlos 13560970, SP, Brazil;Faculty of Computer Science, Federal University of Mato Grosso do Sul, Campo Grande 79070900, MS, Brazil;Faculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do Sul, Campo Grande 79070900, MS, Brazil;Faculty of Engineering, Architecture and Urbanism, University of Western São Paulo, Presidente Prudente 19067175, SP, Brazil;

关键词: Convolutional Neural Network;    biomass yield;    data augmentation;    phenotyping;   

DOI  :  10.3390/s20174802

来源: DOAJ

【 摘 要 】

Monitoring biomass of forages in experimental plots and livestock farms is a time-consuming, expensive, and biased task. Thus, non-destructive, accurate, precise, and quick phenotyping strategies for biomass yield are needed. To promote high-throughput phenotyping in forages, we propose and evaluate the use of deep learning-based methods and UAV (Unmanned Aerial Vehicle)-based RGB images to estimate the value of biomass yield by different genotypes of the forage grass species Panicum maximum Jacq. Experiments were conducted in the Brazilian Cerrado with 110 genotypes with three replications, totaling 330 plots. Two regression models based on Convolutional Neural Networks (CNNs) named AlexNet and ResNet18 were evaluated, and compared to VGGNet—adopted in previous work in the same thematic for other grass species. The predictions returned by the models reached a correlation of 0.88 and a mean absolute error of 12.98% using AlexNet considering pre-training and data augmentation. This proposal may contribute to forage biomass estimation in breeding populations and livestock areas, as well as to reduce the labor in the field.

【 授权许可】

Unknown