Using computational intelligence and UAVs to reduce drift on the application of pesticides

• Grant number: 13/18859-8
• Support type: Scholarships in Brazil - Doctorate
• Effective date (Start): May 01, 2015
• Effective date (End): April 17, 2016
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Cooperation agreement: Coordination of Improvement of Higher Education Personnel (CAPES)
• Principal researcher: Jó Ueyama
• Grantee: Bruno Squizato Faiçal
• Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil

Abstract

The use of pesticides in agriculture is essential for production of quality and in high quantity. Spraying these products from aircraft provides greater speed to this process and prevents soil compaction. On the other hand, meteorological factors (e.g. wind speed and direction) may hinder the application of pesticides in the area of cultivation target, causing the drift of the product to neighboring areas. It is believed that most of the pesticides sprayed suffer drift to outside of target area and that only a small portion is actually effective for the control of pests. Therefore, if the agricultural spraying is performed with greater precision is possible to decrease the amount of pesticides used and increase the quality of the cultivation process, besides decrease environmental impact. With the proposed a set of concepts that can be used in distinct architectures, this Thesis Doctoral proposes a Framework for intelligent spraying of pesticides based on Unmanned Aerial Vehicle (UAV) in the areas of agricultural cultivation in order to optimize the application coverage. The concepts proposed in this thesis will be evaluated on a case study using an architecture composed by a rotary wing UAV and a Wireless Sensor Network (WSN) deployed around the field crop. Preliminary results demonstrate that the use of techniques to adjust policy, that corrects the route of the UAV to weather conditions, provides a spray more precise than using static parameters defined empirically. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

UEYAMA, JO; FAICAL, BRUNO S.; MANO, LEANDRO Y.; BAYER, GUILHERME; PESSIN, GUSTAVO; GOMES, PEDRO H. Enhancing reliability in Wireless Sensor Networks for adaptive river monitoring systems: Reflections on their long-term deployment in Brazil. COMPUTERS ENVIRONMENT AND URBAN SYSTEMS, v. 65, p. 41-52, SEP 2017. Web of Science Citations: 3.

FAICAL, BRUNO S.; FREITAS, HEITOR; GOMES, PEDRO H.; MANO, LEANDRO Y.; PESSIN, GUSTAVO; DE CARVALHO, ANDRE C. P. L. F.; KRISHNAMACHARI, BHASKAR; UEYAMA, JO. An adaptive approach for UAV-based pesticide spraying in dynamic environments. COMPUTERS AND ELECTRONICS IN AGRICULTURE, v. 138, p. 210-223, JUN 1 2017. Web of Science Citations: 18.

MANO, LEANDRO Y.; FAICAL, BRUNO S.; NAKAMURA, LUIS H. V.; GOMES, PEDRO H.; LIBRALON, GIAMPAOLO L.; MENEGUETE, RODOLFO I.; FILHO, GERALDO P. R.; GIANCRISTOFARO, GABRIEL T.; PESSIN, GUSTAVO; KRISHNAMACHARI, BHASKAR; UEYAMA, JO. Exploiting IoT technologies for enhancing Health Smart Homes through patient identification and emotion recognition. COMPUTER COMMUNICATIONS, v. 89-90, p. 178-190, SEP 1 2016. Web of Science Citations: 31.

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

FAIÇAL, Bruno Squizato. The Use of Computational Intelligence for Precision Spraying of Plant Protection Products. 2016. Doctoral Thesis - Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação São Carlos.