Citation: Santos, L.; Junior, J.M.;
Zamboni, P.; Santos, M.; Jank, L.;
Campos, E.; Matsubara, E.T. Deep
Learning Regression Approaches
Applied to Estimate Tillering in
Tropical Forages Using Mobile Phone
Images. Sensors 2022, 22, 4116.
https://doi.org/10.3390/s22114116
Academic Editor: M. Jamal Deen,
Subhas Mukhopadhyay, Yangquan
Chen, Simone Morais, Nunzio
Cennamo and Junseop Lee
Received: 29 March 2022
Accepted: 16 May 2022
Published: 28 May 2022
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Article
Deep Learning Regression Approaches Applied to Estimate
Tillering in Tropical Forages Using Mobile Phone Images
Luiz Santos
1
, José Marcato Junior
2
, Pedro Zamboni
2
, Mateus Santos
3
, Liana Jank
3
,
Edilene Campos
1
and Edson Takashi Matsubara
1,∗
1
Faculty of Computer Science, Federal University of Mato Grosso do Sul,
Campo Grande 79070-900, MS, Brazil; luiz.h.s.santos@ufms.br (L.S.); edilene.veneruchi@ufms.br (E.C.)
2
Faculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do
Sul, Campo Grande 79070-900, MS, Brazil; jose.marcato@ufms.br (J.M.J.); pedro.zamboni@ufms.br (P.Z.)
3
Embrapa Beef Cattle, Brazilian Agricultural Research Corporation, Campo Grande 79106-550, MS, Brazil;
mateus.santos@embrapa.br (M.S.); liana.jank@embrapa.br (L.J.)
* Correspondence: edson.matsubara@ufms.br
Abstract:
We assessed the performance of Convolutional Neural Network (CNN)-based approaches
using mobile phone images to estimate regrowth density in tropical forages. We generated a dataset
composed of 1124 labeled images with 2 mobile phones 7 days after the harvest of the forage plants.
Six architectures were evaluated, including AlexNet, ResNet (18, 34, and 50 layers), ResNeXt101, and
DarkNet. The best regression model showed a mean absolute error of 7.70 and a correlation of 0.89.
Our findings suggest that our proposal using deep learning on mobile phone images can successfully
be used to estimate regrowth density in forages.
Keywords: regrowth density; deep learning; forages
1. Introduction
Pasture areas cover 21% of the territory (170 million hectares) in Brazil; however,
a large part of these pastures are degraded [
1
], leading to lower livestock productivity.
The current average Brazilian productivity (73.5 kg of CWE. ha
−1
·
yr
−1
) is lower than the
potential productivity of 294 kg CWE.ha
−1
·
yr
−1
[
2
]. This production gap represents a great
challenge to be surpassed by the livestock producing countries. On one hand, the increase
in the world population leads to increased demand for protein. On the other hand, policies
to combat climate change require more natural environment conservation, thus demanding
less area for animal protein production. In this scenario, increasing the productivity of areas
already used for animal protein production is essential to meet the growing demand and to
attend to the policies for reducing greenhouse gas emissions, without increasing pasture
area. To achieve this goal, the development of more productive cultivars by efficient forage
breeding methodologies can help reduce the productivity gap [3].
Tillers are small units of forage grass plants responsible for pasture production. After
defoliation of the pasture (e.g., grazing by animals) the regrowth of tillers is crucial to
maintain pasture stability and productivity [
4
,
5
]. The tillers that effectively contribute to
productivity are those that regrow up to eight days after mechanical defoliation or grazing
by animals [
6
]. Thus, one way to measure productivity is to estimate regrowth seven days
after defoliation [
7
]. However, in situ measurements of this trait can be time-consuming,
labor-intensive, and is a subjective task. Thus, the development of low-cost technologies
for automated plant phenotyping could help scientists and professionals in forage breeding
programs. Machine and deep learning combined with mobile devices, such as smartphones,
are powerful and low-cost tools for this purpose. The development of such tools could
induce less labor and time and more accuracy in the phenotyping process in forage breeding
Sensors 2022, 22, 4116. https://doi.org/10.3390/s22114116 https://www.mdpi.com/journal/sensors
