Research Line - Smart Harvest
We develop research in Digital Agriculture using Artificial Intelligence, Data Modeling and Quality Control in mechanized agricultural operations.
We are currently developing some research into cotton, peanut and sweet potato crops.
Balance and operational performance
We developed research aiming at the correct configuration of the tractor for agricultural operations, considering the available engine power and the type of tires used, acting on four main topics: the real need for ballast (starting from the minimum ballast needed); the correct tire calibration (according to the support load of each tire); the use of specific tires (rolling surface and lateral flexion) and the tractor's forward index (by varying tire pressures).
Quality Control in Mechanized Agricultural Operations
We seek to apply the concepts of quality to mechanized agricultural operations, to verify the occurrence of special causes (6 M's - Machines, labor, measurement, method, raw material and environment) that may cause loss of efficiency and quality of the developed work, making the process unstable. Statistical Process Control (CEQ) is a managerial tool with a strong agricultural impact, which can be successful in reducing costs and improving the efficiency of agricultural management.
We carry out research to increase the efficiency and quality of mechanized coffee harvesting operations, which is one of the main crops grown in Brazil. Our focus is on selective harvesting to improve the quality of the harvested fruits.
For grain production, mechanization of the harvesting process is essential. Our research seeks to understand and improve this process through the use of quality tools. We work evaluating the harvesting process and developing methodologies for assessing losses.
The state of São Paulo is the largest peanut producer in Brazil and we seek to develop research to improve the production process of this crop, such as the use of remote sensing to predict maturity and yield.
In Brazil, Mato Grosso state stands out as the largest cotton producer, using high technology from sowing to harvest. We have students conducting research with intelligent systems on farms to optimize cotton yield prediction. The use of high resolution sattelite images of large lands cultivated with cotton is highly been used for decision making and tracking big data obtained from the harvester and we evaluate its relationship to fiber quality.
Sugarcane is one of the main crops in Brazil, with the state of São Paulo being the largest national producer.
The mechanization level of this culture is high and research must be carried out to improve agricultural operations, aiming to improve the efficiency and quality of the processes.