
RESSALVA 

Atendendo solicitação do(a) 
autor(a), o texto completo desta tese 
será disponibilizado somente a partir 

de 16/12/2023. 


UNIVERSIDADE ESTADUAL PAULISTA – UNESP
CÂMPUS DE JABOTICABAL

DON’T FORGET THE BELOW-GROUND CROPS:
INTRODUCING SWEET POTATOES INTO THE CONCEPT OF

DIGITAL AGRICULTURE

Danilo Tedesco de Oliveira
Mestre em Agronomia

2021


UNIVERSIDADE ESTADUAL PAULISTA – UNESP
CÂMPUS DE JABOTICABAL

DON’T FORGET THE BELOW-GROUND CROPS:
INTRODUCING SWEET POTATOES INTO THE CONCEPT OF

DIGITAL AGRICULTURE

Danilo Tedesco de Oliveira
Prof. Dr. Rouverson Pereira da Silva

Tese apresentada à Faculdade de Ciências
Agrárias e Veterinárias - Unesp, Câmpus de
Jaboticabal, como parte das exigências para
a obtenção do tı́tulo de Doutor em Agronomia
(Produção Vegetal)

2021


O48d
Tedesco de Oliveira, Danilo

Don’t forget the below-ground crops: introducing sweet pota-
toes into the concept of digital agriculture. / Danilo Tedesco de
Oliveira. – – Jaboticabal, 2021

x, 94 p. : il., tabs.

Tese (doutorado) – Universidade Estadual Paulista (Unesp),
Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal

Orientador: Prof. Dr. Rouverson Pereira da Silva

1. Agricultura de precisão. 2. Agricultura inteligente. 3. Colheita
inteligente. 4. Ipomoea batatas. 5. Predição de produtividade.
6. Sensoriamento remoto. I. Tı́tulo.

Sistema de geração automática de fichas catalográficas da Unesp. Biblioteca da Faculdade de
Ciências Agrárias e Veterinárias, Jaboticabal. Dados fornecidos pelo autor.
E-mail: danilo.tedesco@unesp.br


DADOS CURRICULARES DO AUTOR

Danilo Tedesco de Oliveira, filho de Lourival Bruno de Oliveira e Roseli Martins
Tedesco de Oliveira, nasceu em Tupã no dia 11 de junho de 1995, municı́pio localizado
no interior do Estado de São Paulo. Cursou ensino fundamental e médio no municı́pio
de Quintana, São Paulo, no perı́odo entre 2002 a 2012. Durante o ensino médio
cursou Técnico em Mecânica no SENAI “Shunji Nishimura” concluindo no ano de
2013. Em 2014 iniciou o curso de graduação em Tecnologia em Mecanização em
Agricultura de Precisão na FATEC “Shunji Nishimura”. Durante o curso foi membro
do Grupo de Estudos de Colheita Mecanizada (GECOM), onde realizou trabalhos e
pesquisas em campo, relacionadas a qualidade de operações agrı́colas de semeadura
e colheita, sob supervisão do Prof. Me. Edson Massão Tanaka. Obteve o tı́tulo de
Tecnólogo em Mecanização em Agricultura de Precisão no ano de 2016. Em 2017
inicio o mestrado no Programa de Pós-graduação em Agronomia (Ciência do Solo)
na Universidade Estadual Paulista “Júlio de Mesquita Filho” Faculdade de Ciências
Agrarias e Veterinárias Campus Jaboticabal (Unesp/FCAV), sobe a orientação do Prof.
Dr. Rouverson Pereira da Silva, onde desenvolveu projetos de pesquisas relacionados
a agricultura de precisão, sensoriamento remoto e uso de técnicas de inteligência
artificial na agricultura, obtendo o tı́tulo de Mestre em Agronomia (Ciência do Solo)
em fevereiro de 2019. No mesmo ano ingressou o curso de Doutorado em Agronomia
(Produção Vegetal) na mesma universidade, sob a mesma orientação. Onde dedicou
seu tempo para realizar pesquisas relacionadas ao desenvolvimento de ferramentas
para avaliar remotamente o crescimento, desenvolvimento, produtividade e qualidade
de culturas com a implementação de técnicas de sensoriamento remoto e inteligência
artificial. Durante o tempo de pós-graduação realizou dezenas de cursos, treinamentos
e palestras relacionadas ao uso de imagens na agricultura de precisão e sistemas de
informações geográficas (SIG). No dia de 16 de dezembro de 2021, submeteu esta
Tese à banca examinadora para obtenção do tı́tulo de Doutor em Agronomia (Produção
Vegetal).


”Eis que estou à porta e bato. Se alguém ouvir a minha voz, e abrir a porta, entrarei em
sua casa, e com ele cearei, e ele comigo.”

Apocalipse 3:20

”A verdadeira ciência descobre Deus esperando atrás de cada porta.”

Papa Pio XII

”Fizeste-nos, Senhor, para ti, e o nosso coração anda inquieto enquanto não descansar
em ti...

Santo Agostinho

Que alegria ter buscado a ciência em Jaboticabal
e encontrado Deus esperando “atrás da porta”.


Aos meus Pais, meus Avós e meus Irmãos.

Dedico!


AGRADECIMENTOS

A Deus, pelo incomparável amor com que guiou cada um dos meus passos
nesse caminho singular do aperfeiçoamento acadêmico e humano. A Jesus, pela
amizade fiel de todos os dias. Ao Espı́rito Santo pelos dons derramados. À Santı́ssima
Virgem Maria, minha mãe, pela fortaleza e paciência que me ensinou para vivência
desses anos do doutorado. Ao meu anjo da guarda, por toda proteção.

Aos meus Pais Lourival Bruno de Oliveira e Roseli Martins Tedesco de Oliveira.
Aos meus Avós João Tedesco e Isabel Martins Tedesco. Aos meus Irmãos Luana
Tedesco de Oliveira e Vinı́cius Martins Tedesco.

Aos Pe. Paulo Ricardo, Pe. Egg Cabral, Pe. Ermı́nio Ignácio, Pe. Leonardo
Wagner e ao Dr. Italo Marsili, pelas orientações e ensinamentos.

Ao meu orientador Dr. Rouverson Pereira da Silva pela orientação e tempo
dedicado na orientação e elaboração desta tese. Aos professores Dr. Gilson Luiz
Volpato e Dr. Abmael da Silva Cardoso. Aos integrantes do Laboratório de Máquinas e
Mecanização Agrı́cola (LAMMA). Aos professores e funcionários do Departamento de
Engenharia e Ciências Matemáticas.

À Universidade Estadual Paulista, Campus de Jaboticabal, em especial ao
Programa de Pós-Graduação em Agronomia (Produção Vegetal).

O presente trabalho foi realizado com apoio da Coordenação de Aperfeiçoamento
de Pessoal de Nı́vel Superior - Brasil (CAPES) - Código de Financiamento 001.

A todos que que estiveram presentes nesta inesquecı́vel fase de minha vida,
compartilhando todos os momentos, bons ou ruins, mas que foram essenciais para
minha formação acadêmica e pessoal.

A vocês, meu muito obrigado.


NÃO ESQUEÇAM DAS CULTURAS SUBTERRÂNEAS: INTRODUZINDO A
BATATA-DOCE NO CONTEXTO DA AGRICULTURA DIGITAL

RESUMO – Produzir alimentos de qualidade para atender a demanda global, em meio
às incertezas climáticas com práticas de manejo sustentáveis é um dos maiores de-
safios das próximas décadas. Se realmente queremos atender a essa demanda, as
ferramentas digitais são a chave para tornar a produção de alimentos mais segura.
No entanto, os esforços para desenvolvimento dessas ferramentas estão totalmente
focados para as culturas de maior interesse comercial, deixando de lado as de menor
interesse, como as culturas subterrâneas. Grande parte das culturas subterrâneas
são cultivadas por pequenos produtores rurais. Eles não possuem recursos ou con-
hecimento necessários para desenvolver ou adaptar essas ferramentas para suas
necessidades. Devido a importância do seu valor nutricional na alimentação diária,
nesta tese mostramos que não podemos esquecer das culturas subterrâneas. Para
contribuir com a solução desse problema, nós desenvolvemos métodos para avaliar o
padrão de crescimento, produção e qualidade da batata-doce. Mostramos que dados
de clima e de satélites podem ser usados para detectar mudanças em estágios de
crescimento e predizer caracterı́sticas da cultura. Nosso trabalho introduz a cultura da
batata-doce no contexto atual da agricultura conectada.

Palavras-chave: agricultura de precisão, agricultura inteligente, colheita inteligente,
Ipomoea batatas, predição de produtividade, sensoriamento remoto

iii


DON’T FORGET THE BELOW-GROUND CROPS: INTRODUCING SWEET
POTATOES INTO THE CONCEPT OF DIGITAL AGRICULTURE

ABSTRACT – To produce quality food to meet global demand amidst climate uncertainty
with sustainable management practices is one of the major challenges of the coming
decades. If we truly want to meet this demand, digital tools are the key to making food
production safer. However, efforts to develop such tools are entirely focused on the
crops of greatest commercial interest, while leaving aside crops of minor interest, such
as below-ground crops. Most below-ground crops are grown by smallholder’s farmers.
These farmers do usually do not have the necessary resources or knowledge to develop
or adapt these tools to their needs. Due to the importance of these crops’ nutritional
value in day-to-day diets, in this dissertation we show that we cannot forget below-ground
crops. To contribute to the solution of this problem, we developed methods to assess the
pattern of growth, yield, and quality of sweet potatoes. We show that weather and satel-
lite data can be used to detect some changes in growth stages and predict crop traits.
Our efforts introduce the sweet potato crop in the current context of e-farming agriculture.

Keywords: precision agriculture, smart farming, smart harvesting, yield prediction,
remote sensing, Ipomoea batatas

v


CHAPTER 1 – General considerations

1.1 Introduction

Digital technologies are transforming the way society, farmers, and decision-
makers look at the agricultural sector and food production. Increasingly accessible
climate data and satellite images have enabled the creation of tools to assess the
characteristics of the development of many crops. Large-scale crop assessment ensures
food security for many families because it makes it possible to determine the impacts
of field interventions and direct efforts to inspect and solve problems on-site. Such a
comprehensive approach makes production more sustainable and boosts productivity
gains.

Scientists and companies are focusing on the development of digital tools for the
assessment of major crops such as cotton, soybeans, corn, and sugarcane. While this
development is promising and the benefits are immeasurable, they forget about minor
crops such as fruits, legumes, and vegetables, which are often grown by smallholders
farmers. Traditionally, smallholders farmers contribute a significant amount of food to
supply the city and regional economy. However, they do not have sufficient resources or
training to adapt the use of digital tools to their realities. Moreover, to develop such tools,
it is necessary to know how to program, working using databases, and with complex
statistical analysis. The challenge for developing these tools is doubled when the crop
grown has the development of its raw material of main economic interest undergrounds,
such as roots, rhizomes, and tubers. Therefore, when developing digital tools, we cannot
forget about the below-ground crops.

Sweet potato is an example of such a crop, because its roots grow below ground.
Its management, therefore, requires farmers or decision-makers to look closely at the
characteristics that determine its productive performance. But to look closely at these
characteristics requires intensive fieldwork, due to the need to dig up the soil to visualize
the production pattern of the roots. Such practice, increases production costs and
does not allow for a detailed understanding of the spatio-temporal dynamics of root
development. Against this background, we believe, therefore, that establishing potential
relationships between vegetation growth characteristics (above ground) from remotely
sensed and weather data with the pattern of root production (below ground) would allow
site-specific management strategies to be set. Thus, we propose the elaboration of this
dissertation. Our objective is to generate knowledge to assist smallholders farmers and
decision-makers in the assessment of sweet potato development characteristics during
the crop cycle. Such an approach will allow the introduction of this crop in the current
context of e-farming agriculture.

1


87

4.7 Concluding remarks and outlooks

Our study clearly demonstrates the possibility of advantageously integrating
high-resolution remote sensing and multi-output machine learning into an immersive
single framework to accurately predict for the yield of sweet potato by the market class
of its tuberous roots upon imagery data on full-scale fields. Straightforward preliminary
evidence exists for the exceptional ability of both random forest and k-nearest neighbors
to learn from the on-field reflectance at GNDVI and SAVI then output multiple numeric
values to the independent variable, without any bias or computational unfeasibility. Our
insights are timely and absolutely will open up the horizons for more precisely harvesting
high-quality material to fulfill the rigors of commercialization and industrialization. Also,
they will do support selecting healthily seedlings to vegetative propagation into new
fields in croplands, where heterogeneity makes it difficult for planning and leveling up
cost-effectiveness. Thereby, the grower who wishes to use our approach can expect, for
the ideal scenario, suitable on-field management to produce sweet potato with more
profitability and societal and environmental responsibility. These are key-aspects to
food safety, especially in regions where this starch-rich crop structures up the basis of
scoping agriculture and human nutrition. Finally, our approach will assist with scaling up
this rural food towards an essentially provocative yet emerging transformative agriculture
for its supply chain. Future directions are basically to (i) predict daily tuberization for
progressively harvesting; and (ii) test if it could be possible for an integrative UAV-SAR
imaging system to improve acquiring data with higher spatial-temporal resolution and
without any damaging factor.


88

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	General considerations
	Introduction
	An overview of thesis structure

	Sustainable management of sweet potato: resilience-building practices for nutrition-sensitive agriculture, energy security and quality of life
	Abstract
	Background
	Food security and human health: “Empowering” sweetness
	Energy security: Do not go hungry to power the world
	On-field management
	Pre-harvest, harvest, and post-harvest: A holistic look at processes
	Planting systems
	Fertilization and irrigation: Use locally available resources and put water in the heart of the field
	Pest Control: Propagate healthy material, make natural barriers and “be biological”

	Reduce Risk: Equitable and fair technocentric arrangement
	Future directions
	References

	Use of remote sensing to characterize the phenological development and to predict sweet potato yield in two growing seasons
	Abstract
	Introduction
	Methodology
	Study area
	Data
	Sweet potato yield
	Satellite imagery
	Pixel extraction and database building

	Definition of phenological stages using VIs
	Yield estimation
	Yield maps


	Results
	Sweet potato phenological stages
	Best growing season and vegetation index to estimate crop yield
	Sweet potato yield maps

	Discussion
	Vegetation indices and sweet potato phenological stages
	Sweet potato yield estimates and maps

	Future perspectives and applications
	Conclusions
	References

	Predicting on multi-target regression for the yield of sweet potato by the market class of its roots upon vegetation indices
	Abstract
	Abbreviations and nomenclature
	Introduction
	Methodology
	Site description
	In-situ measurements
	Satellite data collection
	Prediction on MTR
	Data curation
	Multi-target regression

	Data analysis

	Results
	Crop’s yield by the season
	Prediction on MTR

	Discussion
	Concluding remarks and outlooks
	References

	Final considerations