Ongoing research

Deep learning image processing to estimate peanut harvest losses

The management of the mechanized harvesting operation is essential for controlling production and providing economic returns for agricultural activities.

In this process, losses detection is the key to monitoring the operational quality of the mechanized systems. Thus, our research aims to develop new technologies using automated image processing to detect, count, and estimate losses for mechanized peanut harvesting.

Reducing losses, increasing food safety!

SDG/UN: 2 - 9 - 12

Armando Lopes Brito Filho

PhD Dissertation

Operational and energy performance of tractors as a function of the constructive types of agricultural tires

Tractors used in agricultural operations have undergone changes, which include increasing horsepower and using different traction devices such as diagonal and radial tires and rubber tracks.

Recently, an agricultural tire with rubber track characteristics was made available to the market for use in agricultural tractors, with the objective of providing less soil compaction, higher traction coefficient, operator comfort and lateral stability to the tractor for work on rough terrain.

Our research, pioneering in Latin America, aims to evaluate the operational and energy performance of agricultural tractors, when using diagonal tires and track tires in different operations

We respect and protect the soil!

SDG/UN: 9 - 13

Edward Victor Aleixo

PhD Dissertation

Peanut yield estimation using remote sensing and artificial intelligence tools

The sustainable development in agriculture involves the application of new technologies that aim to increase production to reduce environmental impacts. The use of remote sensing and artificial intelligence tools can increase productivity in agricultural fields, reducing losses and helping the producer in decision making. For peanuts, the estimation of agronomic parameters of the crop can bring great development to the sector. The determination of the maturation index and productivity remotely can improve the use of resources and bring better management for the producer, increasing profitability and quality of the harvested products.

The sky is not the limit; it is the solution!

SDG/UN: 2 - 9 - 12

Jarlyson B. Costa Souza

PhD Dissertation

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SDG/UN: 2 - 9 - 12

Igor Cristian de O. Vieira

PhD Dissertation

High-Throughput Analysis in Sugarcane Based on UAV imagery

Problems associated with the increased efficiency and improved quality and pricing of the harvested product call for the use of high-accuracy approaches and thus have received close attention from researchers and practitioners. Our research uses a high-resolution remote sensing platform applied in high-throughput analysis in sugarcane to show that:

i) estimating sugar content help to determine the best time to harvest;

ii) harvest management is a potential fit-to-purpose strategy can marginally help to avoid.

Toward harvesting high-quality material!

SDG/UN:  12 - 17

Marcelo R. Barbosa Júnior

PhD Dissertation

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SDG/UN: 2 - 9 - 12

Marcelo Odorizzi Campos

PhD Dissertation

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SDG/UN: 2 - 9 - 12

Vinícius dos S. Carreira

PhD Dissertation

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SDG/UN: 2 - 9 - 12

Breno dos Santos Silva

Master Dissertation

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SDG/UN: 2 - 9 - 12

Thiago Caio M. Oliveira

Master Dissertation