Journal
SENSORS
Volume 20, Issue 17, Pages -Publisher
MDPI
DOI: 10.3390/s20174802
Keywords
Convolutional Neural Network; biomass yield; data augmentation; phenotyping
Funding
- Fundacao de Apoio ao Desenvolvimento do Ensino, Ciencia e Tecnologia do Estado do Mato Grasso do Sul (FUNDECT-MS) [59/300.075/2015]
- Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA)
- CNPq [433783/2018-4, 303559/2019-5]
- Associacao para o Fomento a Pesquisa de Melhormento de Forrageiras (UNIPASTO)
- Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior-Brasil (CAPES) [001]
- Universidade Federal de Mato Grosso do Sul (Federal University of Mato Grosso do Sul)-UFMS/MEC-Brasil
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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 speciesPanicum maximumJacq. 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.
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