A framework for high-resolution remote sensing in tomato crop upon minicomputer and cloud computing

• Grant number: 22/16084-8
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: April 01, 2023
• Status: Discontinued
• Field of knowledge: Physical Sciences and Mathematics - Geosciences
• Principal Investigator: Rouverson Pereira da Silva
• Grantee: Vinicius dos Santos Carreira
• Host Institution: Faculdade de Ciências Agrárias e Veterinárias (FCAV). Universidade Estadual Paulista (UNESP). Campus de Jaboticabal. Jaboticabal , SP, Brazil
• Associated research grant: 21/06029-7 - High resolution remote sensing for digital agriculture, AP.TEM
• Associated scholarship(s): 24/23051-4 - Impact of viewing angles in high-resolution imagery of tomato canopy on yield and chlorophyll estimation, BE.EP.DR

Abstract

The digital revolution is driving agricultural systems and directly helping in decision-making. However, the unfriendly use and the lack of techonology for tomato crop monitoring dramatically marginalize the farmer. Remote sensing techniques with high-efficiency systems can overcome this challenge. Therefore, here the objective is to develop a open-source framework to monitor tomato cro growth dynamics (e.g., maturity and yield) upon remote sensing. To improve the system, multispectral images will be transmitted by a minicomputer and processed using cloud computing. Hence, the project will be conducted in two parts: (i) data-transmission dispositive development and validation (ii) develoment and validation of methods to monitor tomato crop using agronomic, climatic and spectral data and cloud computing. The framework will outperformed nowadays systems and it will be possible to define strategies towards less unpredictable crops. The results will be disruptive for remote sensing systems by improving the applicability and for the production chain by introducing tomato in the concept of digital agriculture.

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)

SOUZA, JARLYSON BRUNNO COSTA; DE ALMEIDA, SAMIRA LUNS HATUM; DE OLIVEIRA, MAILSON FREIRE; CARREIRA, VINICIUS DOS SANTOS; DE FILHO, ARMANDO LOPES BRITO; DOS SANTOS, ADAO FELIPE; DA SILVA, ROUVERSON PEREIRA. Generalization of peanut yield prediction models using artificial neural networks and vegetation indices. SMART AGRICULTURAL TECHNOLOGY, v. 11, p. 14-pg., 2025-03-11. (22/16084-8, 21/06029-7, 23/14041-2)

BARBOSA JUNIOR, MARCELO RODRIGUES; MOREIRA, BRUNO RAFAEL DE ALMEIDA; CARREIRA, VINICIUS DOS SANTOS; DE BRITO FILHO, ARMANDO LOPES; TRENTIN, CAROLINA; DE SOUZA, FAVIA LUIZE PEREIRA; TEDESCO, DANILO; SETIYONO, TRI; FLORES, JOAO PAULO; AMPATZIDIS, YIANNIS; et al. Precision agriculture in the United States: A comprehensive meta-review inspiring further research, innovation, and adoption. COMPUTERS AND ELECTRONICS IN AGRICULTURE, v. 221, p. 22-pg., 2024-05-09. (22/16084-8, 22/13992-0)

FILHO, ARMANDO LOPES DE BRITO; CARNEIRO, FRANCIELE MORLIN; CARREIRA, VINICIUS DOS SANTOS; TEDESCO, DANILO; SOUZA, JARLYSON BRUNNO COSTA; JUNIOR, MARCELO RODRIGUES BARBOSA; DA SILVA, ROUVERSON PEREIRA. Deep convolutional networks based on lightweight YOLOv8 to detect and estimate peanut losses from images in post-harvesting environments. COMPUTERS AND ELECTRONICS IN AGRICULTURE, v. 234, p. 12-pg., 2025-07-01. (22/16084-8)