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Plantation monitoring using heterogeneous robots

Grant number: 17/17444-0
Support type:Scholarships in Brazil - Doctorate (Direct)
Effective date (Start): September 01, 2017
Effective date (End): August 31, 2022
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computer Systems
Cooperation agreement: CNPq - INCTs
Principal researcher:Roseli Aparecida Francelin Romero
Grantee:Guilherme Vicentim Nardari
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Associated research grant:14/50851-0 - INCT 2014: National Institute of Science and Technology for Cooperative Autonomous Systems Applied in Security and Environment, AP.TEM
Associated scholarship(s):18/24526-5 - Estimation of crop structure and health using heterogeneous robots, BE.EP.DD


The development of the National Agriculture accelerated by the government's investments made the insertion of new technologies in this field possible, increasing productivity, fighting pests, and defining suitable ground occupation. Brazil is a reference in exportation for some products, such as orange, coffee and cocoa. As it is in the farmer's interest to increase productivity, in order to reduce risks and losses, it is believed that the use of an autonomous monitoring system with the use of robots to collect images of the plantation for later analysis may assist the farmer in his decisions. The data collection aims to estimate several factors, such as the quantity of products/fruits produced by region, detection of diseases in the leaves of the plants, rate of development and degree of maturity of the plants. These data are important, because they reflect on the flavor, color, size and quality of the product/fruit. The proposed system uses a heterogeneous robots architecture in a way that they have simpler tasks combined to the execution of a final purpose. The two classes of autonomous robots applied will be the car and the quadrotor (UAV). The first one will be responsible for walking a block of the plantation to be monitored in order to identify it, and then shoot the quadrotor. The UAV must go through each row collecting images of the front of the plantation, and then return to the base (car) to charge its own batteries. The quadrotor also receives the coordinates for each row from the system inside the car. It is expected, with this research, in the medium term, to produce an economically viable product, guided by an iterative development and a close relationship to users, that brings relevant information to farmers' decision-making process. (AU)

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