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Systematic Review

Artificial Neural Networks for Image Processing in Precision Agriculture: A Systematic Literature Review on Mango, Apple, Lemon, and Coffee Crops

by
Christian Unigarro
,
Jorge Hernandez
and
Hector Florez
*
ITI Research Group, Universidad Distrital Francisco Jose de Caldas, Bogota 110231, Colombia
*
Author to whom correspondence should be addressed.
Informatics 2025, 12(2), 46; https://doi.org/10.3390/informatics12020046
Submission received: 8 January 2025 / Revised: 31 March 2025 / Accepted: 30 April 2025 / Published: 6 May 2025

Abstract

Precision agriculture is an approach that uses information technologies to improve and optimize agricultural production. It is based on the collection and analysis of agricultural data to support decision making in agricultural processes. In recent years, Artificial Neural Networks (ANNs) have demonstrated significant benefits in addressing precision agriculture needs, such as pest detection, disease classification, crop state assessment, and soil quality evaluation. This article aims to perform a systematic literature review on how ANNs with an emphasis on image processing can assess if fruits such as mango, apple, lemon, and coffee are ready for harvest. These specific crops were selected due to their diversity in color and size, providing a representative sample for analyzing the most commonly employed ANN methods in agriculture, especially for fruit ripening, damage, pest detection, and harvest prediction. This review identifies Convolutional Neural Networks (CNNs), including commonly employed architectures such as VGG16 and ResNet50, as highly effective, achieving accuracies ranging between 83% and 99%. Additionally, it discusses the integration of hardware and software, image preprocessing methods, and evaluation metrics commonly employed. The results reveal the notable underuse of vegetation indices and infrared imaging techniques for detailed fruit quality assessment, indicating valuable opportunities for future research.
Keywords: artificial neural networks; image processing; harvest prediction; systematic literature review artificial neural networks; image processing; harvest prediction; systematic literature review

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MDPI and ACS Style

Unigarro, C.; Hernandez, J.; Florez, H. Artificial Neural Networks for Image Processing in Precision Agriculture: A Systematic Literature Review on Mango, Apple, Lemon, and Coffee Crops. Informatics 2025, 12, 46. https://doi.org/10.3390/informatics12020046

AMA Style

Unigarro C, Hernandez J, Florez H. Artificial Neural Networks for Image Processing in Precision Agriculture: A Systematic Literature Review on Mango, Apple, Lemon, and Coffee Crops. Informatics. 2025; 12(2):46. https://doi.org/10.3390/informatics12020046

Chicago/Turabian Style

Unigarro, Christian, Jorge Hernandez, and Hector Florez. 2025. "Artificial Neural Networks for Image Processing in Precision Agriculture: A Systematic Literature Review on Mango, Apple, Lemon, and Coffee Crops" Informatics 12, no. 2: 46. https://doi.org/10.3390/informatics12020046

APA Style

Unigarro, C., Hernandez, J., & Florez, H. (2025). Artificial Neural Networks for Image Processing in Precision Agriculture: A Systematic Literature Review on Mango, Apple, Lemon, and Coffee Crops. Informatics, 12(2), 46. https://doi.org/10.3390/informatics12020046

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