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2024
UNIVERSIDADE ESTADUAL PAULISTA – UNESP
CAMPUS JABOTICABAL
PREVISÃO DE PRODUTIVIDADE E QUALIDADE DE
CENOURA UTILIZANDO APRENDIZADO DE MÁQUINA
Yara Karine de Lima Silva
Engenheira agrônoma
2024
UNIVERSIDADE ESTADUAL PAULISTA – UNESP
CAMPUS JABOTICABAL
PREVISÃO DE PRODUTIVIDADE E QUALIDADE DE
CENOURA UTILIZANDO APRENDIZADO DE MÁQUINA
Yara Karine de Lima Silva
Dr. Carlos Eduardo Angeli Furlani
Dr. Alberto Carvalho Filho
Dr. Renato Adriane Alves Ruas
Tese de doutorado apresentada à Faculdade
de Ciências Agrárias e Veterinárias – Unesp,
Câmpus de Jaboticabal, como parte das
exigências para obtenção do título de
Doutora em Agronomia (Ciencia do Solo)
S586p
Silva, Yara Karine de Lima
Previsão de produtividade e qualidade de cenoura utilizando
aprendizado de máquina / Yara Karine de Lima Silva. -- Jaboticabal,
2024
109 p.
Tese (doutorado) - Universidade Estadual Paulista (UNESP),
Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal
Orientador: Carlos Eduardo Angeli Furlani
Coorientador: Alberto Carvalho Filho
1. Solo. 2. Mapeamento. 3. Qualidade. 4. Índice de vegetação. I.
Título.
Sistema de geração automática de fichas catalográficas da Unesp. Biblioteca da Universidade
Estadual Paulista (UNESP), Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal. Dados
fornecidos pelo autor(a).
Essa ficha não pode ser modificada.
DADOS CURRICULARES DO AUTOR
YARA KARINE DE LIMA SILVA – nascida em Cedro do Abaeté, Minas Gerais,
no dia 06 de dezembro de filho de Divino Eustáquio de Lima e Mary Aparecida Pessoa.
Cursou os ensinos fundamental e médio na Escola Frederico Campos em sua cidade
de origem, finalizando-os no ano de 2013. Ingressou no ensino superior no ano de
2014 no curso de Engenharia Agronômica pela Universidade Federal de Viçosa (UFV),
Câmpus de Rio Paranaíba - MG, obtendo o título de Engenheira Agrônoma em
fevereiro de 2019. Durante a graduação foi bolsista de Iniciação Cientifica pelo
Conselho Nacional de Desenvolvimento Científico e Tecnológico durante os anos de
2016 e 2017 no Grupo de Estudos em Solos e Suas Interfaces com a Agroecologia
(GESSIA) sob orientação do Dr. André Mundstock Xavier de Carvalho, gerando
trabalhos científicos e participação em eventos acadêmicos-científicos. Durante a
graduação ministrou monitorias remunerada e voluntária das disciplinas de Gêneses
do solo, Pedologia, Geologia básica, Fertilidade do solo e tutoria especial de
Bioquímica Fundamental. Em março de 2019 iniciou o Mestrado em Produção Vegetal
na mesma Universidade de sua graduação, participando ativamente no laboratório de
Solos e Mecanização do Grupo de Mecanização Agrícola e Solos (GESOL) sob
orientação do Dr. Alberto Carvalho Filho. No final de 2020, ainda no mestrado, atuou
profissionalmente como Assistente Técnica em culturas anuais (soja e milho) e na
cultura perene do café no Triângulo Mineiro e Alto Paranaíba. Em janeiro de 2021
obteve o título de Mestre em Produção Vegetal. Após o término do Mestrado, atuou
como Analista de Mercado e Produto na região de Patrocínio - MG antes de ingressar
no Doutorado em junho de 2021. O Doutorado em Ciência do Solo foi cursado na
Universidade Estadual Paulista “Júlio de Mesquita Filho” em Jaboticabal, São Paulo,
sob orientação do Dr. Carlos Eduardo Angeli Furlani. Ao final do Doutorado tem atuado
como Analista Técnica Comercial Sênior pela multinacional Yara Fertilizantes nas
regiões do Noroeste Mineiro e Leste Goiano. Em janeiro de 2024 submeteu-se à banca
examinadora para obtenção do título de Doutora em Agronomia (Ciência do Solo).
“Pensei mais um milhão de vezes em parar,
em desistir de mim por não acreditar.
E hoje eu sou o meu melhor motivo pra
comemorar. O ontem passou e o amanhã
ainda não é eu. Tudo o que mudou me
transformou no que hoje sou eu.”
(Kell Smith)
Dedico aos meus pais e à toda a minha família.
Aos meus amigos e profissionais que sempre
me deram o suporte necessário para que eu
não desistisse! E principalmente aos que
estiveram comigo desde o mestrado me
mantendo forte, me apoiando e me motivando
para que eu me tornasse cada dia melhor. Meu
muito obrigada. Sem vocês nada disso se
concretizaria.
AGRADECIMENTOS
O presente trabalho foi realizado com apoio da Coordenação de Aperfeiçoamento de Pessoal de
Nível Superior – Brasil (CAPES) – Código de Financiamento 001.
SUMÁRIO
RESUMO ....................................................................................................................... 11
ABSTRACT ................................................................................................................... 12
1 INTRODUÇÃO ............................................................................................................ 13
2 REVISÃO DE LITERATURA ...................................................................................... 14
2.1 A cultura da cenoura.............................................................................................. 14
2.2 Aspectos de cultivo ................................................................................................ 21
I. Semeadura e solo ................................................................................................. 21
II. Condições de temperatura e germinação ............................................................. 24
III. Manejo da irrigação ............................................................................................... 28
IV. Adubação .............................................................................................................. 29
V. Plantas daninhas................................................................................................... 33
VI. Doenças e pragas ................................................................................................. 35
2.3 Cenoura e seus componentes para a nutrição humana ........................................ 38
2.4 Cenoura além da alimentação humana ................................................................ 42
I. Protetor solar ............................................................................................................ 42
II. Ração bovina ........................................................................................................... 43
III. Biodiesel ................................................................................................................. 43
IV. Corante .................................................................................................................. 43
V. Revestimento .......................................................................................................... 43
VI. Cosméticos ............................................................................................................. 44
2.5 Qualidade da cenoura ........................................................................................... 44
2.6 Segurança alimentar e meio ambiente .................................................................. 47
2.7 Aspectos da Colheita ............................................................................................ 51
2.7.1 Padronização das raízes ................................................................................ 51
2.7.2 Colheita manual ............................................................................................. 52
2.7.3 Colheita mecanizada ...................................................................................... 54
i. Regulagens da colhedora de cenoura ............................................................ 58
ii. Danos nas raízes provocados pela colheita mecanizada mal regulada ......... 59
iii. Perda de solo na colheita ............................................................................... 60
iv. Colheita e inteligência artificial ....................................................................... 60
v. Operação de colheita e a qualidade final do produto cenoura........................ 62
2.8 Monitoranento das características de produtividade e qualidade da cenoura via
sensoriamento e inteligencia artificial ................................................................... 63
2.9 Considerações finais da revisão ........................................................................... 65
3 MATERIAL E MÉTODOS...................................................................................... 66
3.1 Área de estudo e sistema de produção ................................................................. 66
3.2 Amostragem das raízes e avaliação biométrica da produtividade......................... 68
3.3 Análise qualitativa das raízes ................................................................................ 70
3.4 Aquisição e processamento das imagens de satélite ............................................ 70
3.5 Análise de componentes principais (PCA) ............................................................ 72
3.6 Modelagem da cenoura ........................................................................................ 72
4 RESULTADOS ...................................................................................................... 74
4.1 Normalidade dos dados de produtividade da cultura ............................................ 74
4.2 Qualidade das raízes ............................................................................................ 74
4.3 Correlação entre as variáveis................................................................................ 76
4.4 ANN – Multilayer perceptron regressor .......................................................... 77
4.6 RLM – Regressão linear múltipla .......................................................................... 78
4.7 Comparação do desempenho dos modelos .......................................................... 79
5 DISCUSSÕES....................................................................................................... 80
5.1 Correlação das variáveis ....................................................................................... 80
5.2 Modelagem da produtividade ................................................................................ 81
5.3 Modelagem da produtividade ................................................................................ 82
6 CONSIDERAÇÕES FINAIS .................................................................................. 83
7 REFERÊNCIAS ..................................................................................................... 84
Apêndice .................................................................................................................... 109
PREVISÃO DE PRODUTIVIDADE E QUALIDADE DE CENOURA UTILIZANDO
APRENDIZADO DE MÁQUINA
RESUMO
A cenoura (Daucus carota L.) destaca-se entre os principais vegetais cultivados
globalmente. A implementação de sistemas agrícolas baseados em inteligência artificial
podem os tornar mais eficientes e sustentáveis nas diferentes esferas da produção. No
contexto dessa abordagem, o híbrido EX 4098 de cenoura foi testado em dois
experimentos durante a safra de verão em Rio Paranaíba /MG, visando otimizar a
produção e impulsionar a agricultura sustentável.Com o objetivo de prever a produtividade
e a qualidade da cultura, foram realizadas amostragens das raízes em 200 pontos de 0,25
m² com grade amostral de 30 m x 30 m, em duas épocas de coleta (82 e 116 dias após
semeadura) em ambos os experimentos. Para a produtividade quantificou-se a biomassa
fresca total, parte aérea e raiz e biometria das raízes (comprimento e diâmetro). A
qualidade das raízes foi avaliada na subamostragem de três cenouras pela concentração
de sólidos solúveis totais (°Brix) e firmeza. Os índices de vegetação NDVI, RDVI, EVI e
SAVI foram extraídos da PlanetScope CubeSat. Os parâmetros mais importantes
verificados na análise dos componentes principais foram submetidos aos algoritmos
artificial neural network (ANN), random forest (RF) e regressão linear múltipla (RLM) para
a modelagem da cultura. Para a estimativa de produtividade, o modelo ANN foi superior
à RF e ao RLM, respectivamente. Os índices SAVI e NDVI destacaram-se como
indicadores significativos para predizer a produtividade da cultura. No entanto, é
importante esses algoritmos não foram capazes de modelar a qualidade da cenoura.
Sugere-se que estudos futuros explorem o potencial preditivo dos parâmetros °Brix e
Firmeza para avaliar e aprimorar a qualidade da cenoura.
Palavras-chaves: Solo, Mapeamento, Qualidade, Índice de vegetação.
PREDICTING CARROT YIELD AND QUALITY USING MACHINE
LEARNING
ABSTRACT
Carrot (Daucus carota L.) stands out among the main globally cultivated
vegetables. The implementation of artificial intelligence-based agricultural systems
can make them more efficient and sustainable across different spheres of
production. In the context of this approach, the carrot hybrid EX 4098 was tested
in two experiments during the summer crop in Rio Paranaíba/MG, aiming to
optimize production and boost sustainable agriculture.In order to predict crop
productivity and quality, root samples were taken at 200 points of 0.25 m² with a
sampling grid of 30 m x 30 m, at two collection times (82 and 116 days after sowing)
in both experiments. For productivity, total fresh biomass, aboveground and root
biomass, and root biometrics (length and diameter) were quantified. Root quality
was assessed by sub-sampling three carrots for total soluble solids concentration
(°Brix) and firmness. NDVI, RDVI, EVI, and SAVI vegetation indices were extracted
from the PlanetScope CubeSat. The most important parameters verified in the
principal component analysis were subjected to artificial neural network (ANN),
random forest (RF), and multiple linear regression (MLR) algorithms for crop
modeling.For productivity estimation, the ANN model outperformed RF and MLR,
respectively. SAVI and NDVI indices stood out as significant indicators for
predicting crop productivity. However, it is important to note that these algorithms
were unable to model carrot quality. It is suggested that future studies explore the
predictive potential of °Brix and firmness parameters to assess and improve carrot
quality.
Keywords: Soil, Mapping, Quality, Vegetation index.
13
1 INTRODUÇÃO
A produção de cenouras apresenta desafios complexos, que vão desde a
otimização da qualidade das raízes até a previsão da produtividade. A cenoura é uma
hortaliça geocárpica, crescendo subterraneamente, o que dificulta a avaliação direta de
sua parte desejada, as raízes. Fatores como variação genética, condições ambientais e
práticas de cultivo influenciam a qualidade das cenouras, tornando essencial a busca por
abordagens inovadoras na tomada de decisões.
A qualidade da cenoura de mesa é fortemente influenciada por características
visuais, como uniformidade de tamanho e formato, além de sabor, aspectos fundamentais
para sua valorização no mercado e preferência dos consumidores. Nesse contexto,
variáveis como °Brix (sólidos solúveis totais) e firmeza tornam-se críticas, mas a
modelagem preditiva desses parâmetros ainda é uma lacuna a ser preenchida. A
integração de tecnologias como sensoriamento remoto (SR) como a coleta de dados via
imagens de satelite e inteligência artificial (IA) emerge como uma solução promissora para
superar os desafios específicos da cenoura, possibilitando uma avaliação mais completa.
A utilização de ferramentas de monitoramento para acompanhar as características
de produtividade e qualidade da cenoura é essencial para embasar decisões no campo.
Ao investigar a variação dessas características, é possível delimitar unidades de gestão
diferenciadas (UGDs) e identificar locais com maior potencial produtivo da cultura,
adaptando-se aos diferentes usos. Esses locais de maior potencial produtivo podem
otimizar o uso de recursos, como adubação, defensivos agrícolas, densidade de plantio e
época de semeadura. Áreas identificadas com produção de cenouras com teores mais
elevados de açúcares podem ser destinadas à produção de energia ou receber maior
valorização no mercado consumidor. Além disso, a capacidade de prever a produtividade
e qualidade da cenoura antes da colheita abre oportunidades para negociações futuras.
Neste contexto, o presente estudo visa estabelecer relações funcionais entre
diversas variáveis, como a massa fresca total, a massa da parte aérea e da raiz, o
comprimento, diâmetro, °Brix e firmeza das raízes, integrando-os com índices de
vegetação e inteligência artificial. A ausência de pesquisas que explorem de maneira
simultânea a produtividade e a qualidade das raízes de cenoura destaca a importância
desta abordagem, proporcionando insights valiosos aos produtores e contribuindo para
avanços significativos na produção sustentável da cultura. A tese inclui uma revisão de
14
literatura aprofundada sobre o tema, fornecendo uma base sólida para a análise dos
resultados obtidos no experimento.
83
6 CONSIDERAÇÕES FINAIS
Os índices SAVI e NDVI mostraram-se promissores em comparação aos índices RDVI
e EVI na previsão da produtividade de cenoura. Os modelos ANN, RF e RLM
demonstraram ser eficazes na modelagem da produtividade da cultura. A análise
de componentes principais revelou a influência temporal nas variáveis espectrais, o
que pode ser útil para otimizar o monitoramento da cultura ao longo do tempo.
Apesar da qualidade não ter sido modelada, os resultados aqui obtidos trazem
contextualização metodológica que podem incentivar pesquisas futuras.
Aqui, propomos encontrar respostas no campo que refletissem em ganhos
múltiplos no âmbito econômico, social e ambiental, contemplando sobretudo os
objetivos de desenvolvimento sustentável da ONU: ODS #02, ODS #10, ODS #12 e
ODS #17. O desenvolvimento de novas tecnologias que proporcionam melhorias na
qualidade dos alimentos vai ao encontro do fomento da segurança alimentar e
saúde humana. Isso permite alcançar a melhoria da gestão e governança de forma
responsável, reduzindo os impactos ambientais e otimizando a produção de
alimentos. Pelos objetivos de desenvolvimento sustentável, zerar pobreza (ODS
2) e introduzir tecnologias de construção de resiliência em agroecossistemas são
indiscutíveis à segurança alimentar e qualidade de vida.
84
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