DropLeaf: A precision farming smartphone tool for real-time quantification of pesticide application coverage

Author(s)

Brandoli, Bruno ^[1] ; Spadon, Gabriel ^[2] ; Esau, Travis ^[3] ; Hennessy, Patrick ^[3] ; Carvalho, Andre C. P. L. ^[2] ; Amer-Yahia, Sihem ^[4] ; Rodrigues, Jr., Jose F. ^{[2, 4]}

Affiliation

^[1] Dalhousie Univ, Dept Comp Sci, Halifax, NS - Canada

^[2] Univ Sao Paulo, Inst Math & Comp Sci, Sao Carlos, SP - Brazil

^[3] Dalhousie Univ, Fac Agr, Dept Engn, Truro, NS - Canada

^[4] Univ Grenoble, CNRS, Grenoble Alpes - France

• Document type: Journal article
• Source: COMPUTERS AND ELECTRONICS IN AGRICULTURE; v. 180, JAN 2021.
• Web of Science Citations: 0

Abstract

Pesticides have been heavily used in the cultivation of major crops, contributing to the increase of crop production over the past decades. However, in many cases their appropriate use and calibration of machines rely upon dated evaluation methodologies that cannot precisely estimate how well the pesticides' are being applied to the crop. A few strategies have been proposed in former works, yet their elevated costs and low portability do not permit their wide spread adoption. This work introduces and experimentally assesses a novel tool that functions as a smartphone-based mobile application, named DropLeaf - Spraying Meter. Tests performed using DropLeaf demonstrated that, notwithstanding its simplicity, it can estimate the pesticide coverage with high precision. Our methodology is based on the development of custom image analysis software for real-time assessment of spraying deposition of water-sensitive papers. The proposed tool can be extensively used by farmers and agronomists carrying regular smartphones, improving the utilization of pesticides with well-being, ecological, and monetary advantages. DropLeaf can be easily used for spray drift assessment of different methods, including emerging unmanned aerial vehicle and smart sprayers. (AU)

• FAPESP's process: 19/04461-9 - Advancing medical prognosis based on graph concepts and artificial neural networks
• Grantee: Gabriel Spadon de Souza
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• FAPESP's process: 17/08376-0 - Analysis and improvement of urban systems using digital maps in the form of complex networks
• Grantee: Gabriel Spadon de Souza
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 18/17620-5 - Preventive medicine by means of deep learning techniques applied in healthcare prognosis
• Grantee: José Fernando Rodrigues Júnior
• Support Opportunities: Scholarships abroad - Research