Rouverson Pereira da Silva

Seasonal Topoclimatic zoning for mechanized peanut expansion in Brazil

Igor Cristian de Oliveira Vieira, Samira Luns Hatum de Almeida, Leonardo M. Bastos, Kamila Cunha de Meneses et autres

Agroclimatic and topographic zoning can support climate-informed agricultural decision-making by translating climate variability into spatially explicit suitability patterns. This study developed a national-scale zoning framework for peanut ( Arachis hypogaea L.) cultivation in Brazil by integrating temperature, climatic water deficit, and terrain …

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Statistical Process Control for Monitoring Soybean Seed Quality During Mechanical Harvest

Rafael Marani Barbosa, Rayane Aguiar Alves, Caíque Carvalho Medauar, Rouverson Pereira da Silva et autres

Seed moisture content (SMC) fluctuations and machine adjustments during mechanical harvesting are associatedwith soybean seed quality and process variability. This study aimed to monitor the physical and physiological quality of soybean seeds and evaluate process variability during mechanical harvesting using Statistical Process …

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Sugarcane yield and performance as a function of harvesting speed in dry and rainy periods

José Eldo Costa, Fábio Mielezrski, Valéria Fernandes de Oliveira Sousa, João Henrique Barbosa da Silva et autres

ABSTRACT Mechanized sugarcane harvesting can have negative implications, depending on soil moisture and climate conditions. Among the associated challenges is soil compaction, caused by heavy machinery traffic under inadequate moisture and speed conditions, which limits sugarcane yield and longevity. Therefore, the objective …

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Combining Artificial Intelligence and Remote Sensing to Enhance the Estimation of Peanut Pod Maturity

Thiago Caio Moura Oliveira, Jarlyson Brunno Costa Souza, Samira Luns Hatum de Almeida, Armando Lopes de Brito Filho et autres

The mechanized harvesting of peanut crops results in both visible and invisible losses. Therefore, monitoring and accurately determining pod maturation are essential to minimizing such losses. The objectives of this study were to (i) identify the most relevant variables for estimating peanut …

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Mass Impact of a Mounted Sprayer on the Operational Balance of an Agricultural Tractor

Bruno Passador Lombardi, Alex Portelinha, Igor Cristian de Oliveira Vieira, Breno Santos-Silva et autres

The operational stability of agricultural tractors is directly influenced by the mass distribution between axles, particularly when using mounted implements with variable loads. This study aimed to evaluate how different masses of a mounted sprayer (550 kg, 850 kg, and 1150 kg) …

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Spatial and temporal variability of coffee fruit detachment force in relation to maturity aiming at smart harvesting

Jarlyson Brunno Costa Souza, Samira Luns Hatum de Almeida, Igor Cristian de Oliveira Vieira, Tiago de Oliveira Tavares et autres

The implementation of smart harvesting, guided by variability in fruit detachment force, has the potential to facilitate and expand selective coffee harvesting, thereby strengthening the specialty coffee market. This study aimed to evaluate the variation in coffee fruit detachment force throughout the …

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Impact of rural credit on sustainable transformation of Brazilian agriculture: The case of the Low Carbon Agriculture (LCA) Program in Minas Gerais, Brazil

Marcelo Odorizzi de Campos, Regina Aparecida Leite de Camargo, Camila Fernanda Bassetto, Teresa Cristina Tarlé Pissarra et autres

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Application of artificial intelligence for identification of peanut maturity using climatic variables and vegetation indices

Thiago Orlando Costa Barboza, Jarlyson Brunno Costa Souza, Marcelo Araújo Junqueira Ferraz, Samira Luns Hatum de Almeida et autres

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Deep convolutional networks based on lightweight YOLOv8 to detect and estimate peanut losses from images in post-harvesting environments

Armando Lopes de Brito Filho, Franciele Morlin Carneiro, Vinicius dos Santos Carreira, Danilo Tedesco et autres

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Generalization of peanut yield prediction models using artificial neural networks and vegetation indices

Jarlyson Brunno Costa Souza, Samira Luns Hatum de Almeida, Maílson Freire de Oliveira, Vinicius dos Santos Carreira et autres

The prediction of crop yield is vital for the management and decision-making processes in agriculture. Techniques such as Remote Sensing (RS) and Artificial Neural Networks (ANN) emerge as potential tools for predicting these agronomic parameters. Therefore, the objective of this study was …

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Integrated Model to Reduce the Maneuver Time of the Harvester and Infield Wagon in Sugarcane Harvest

Lígia Negri Corrêa, Adão Felipe dos Santos, Carlos Eduardo Angeli Furlani, Glauco de Souza Rolim et autres

This study aims to enhance the efficiency of mechanized sugarcane (Saccharum spp.) harvesting by optimizing the maneuvers of the auxiliary vehicle (infield wagon) operating in tandem with the harvester. The optimization focuses on increasing productivity and reducing soil compaction in high-traffic maneuver …

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Soil and crop response to varying planter's downforce in corn and cotton fields

Brenda V. Ortiz, Gregory Lee Pate, Thomas R. Way, Rouverson Pereira da Silva

Effective planter downforce is crucial for optimizing seed placement and improving crop emergence and growth. This study explores the impact of different downforce settings on soil compaction, seeding depth, and crop performance for corn and cotton. Two field experiments were implemented using …