Title:	Combination of remote sensing and artificial intelligence in fruit growing: progress, challenges, and potential applications.
Authors:	FURUYA, D. E. G. BOLFE, E. L. PARREIRAS, T. C. BARBEDO, J. G. A. SANTOS, T. T. GEBLER, L.
Affiliation:	DANIELLE ELIS GARCIA FURUYA; EDSON LUIS BOLFE, CNPTIA, UNIVERSIDADE ESTADUAL DE CAMPINAS; TAYA CRISTO PARREIRAS, UNIVERSIDADE ESTADUAL DE CAMPINAS; JAYME GARCIA ARNAL BARBEDO, CNPTIA; THIAGO TEIXEIRA SANTOS, CNPTIA; LUCIANO GEBLER, CNPUV.
Date Issued:	2024
Citation:	Remote Sensing, v.1 6, n. 24, 4805, Dec. 2024.
Description:	Fruit growing is important in the global agricultural economy, contributing significantly to food security, job creation, and rural development. With the advancement of technologies, mapping fruits using remote sensing and machine learning (ML) and deep learning (DL) techniques has become an essential tool to optimize production, monitor crop health, and predict harvests with greater accuracy. This study was developed in four main stages. In the first stage, a comprehensive review of the existing literature was made from July 2018 (first article found) to June 2024, totaling 117 articles. In the second stage, a general analysis of the data obtained was made, such as the identification of the most studied fruits with the techniques of interest. In the third stage, a more in-depth analysis was made focusing on apples and grapes, with 27 and 30 articles, respectively. The analysis included the use of remote sensing (orbital and proximal) imagery and ML/DL algorithms to map crop areas, detect diseases, and monitor crop development, among other analyses. The fourth stage shows the data’s potential application in a Southern Brazilian region, known for apple and grape production. This study demonstrates how the integration of modern technologies can transform fruit farming, promoting more sustainable and efficient agriculture through remote sensing and artificial intelligence technologies.
Thesagro:	Maçã Uva Pomar Sensoriamento Remoto
Keywords:	Agricultura digital Aprendizado profundo Aprendizado de máquina Inteligência artificial Digital agriculture Deep learning Machine learning Orchard Apple Grape
ISSN:	2072-4292
DOI:	https://doi.org/10.3390/rs16244805
Type of Material:	Artigo de periódico
Access:	openAccess
Appears in Collections:	Artigo em periódico indexado (CNPTIA)