Combination of Remote Sensing and Artificial Intelligence in Fruit Growing: Progress, Challenges, and Potential Applications

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 essenti...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2024-12, Vol.16 (24), p.4805
Main Authors: Furuya, Danielle Elis Garcia, Bolfe, Édson Luis, Parreiras, Taya Cristo, Barbedo, Jayme Garcia Arnal, Santos, Thiago Teixeira, Gebler, Luciano
Format: Article
Language:English
Subjects:
Accuracy
Agriculture
Algorithms
apple
Apples
Artificial intelligence
Citrus
Citrus fruits
Crop development
Crop production
Crops
Data analysis
Deep learning
Developmental stages
digital agriculture
Farmers
Food security
Food supply
Fruits
grape
Grapes
Keywords
Machine learning
orchard
Remote sensing
Rural development
Satellites
Sensors
Wineries & vineyards
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs16244805