Machine Learning as a Strategic Tool for Helping Cocoa Farmers in Côte D’Ivoire
Machine learning can be used for social good. The employment of artificial intelligence in smart agriculture has many benefits for the environment: it helps small farmers (at a local scale) and policymakers and cooperatives (at regional scale) to take valid and coordinated countermeasures to combat...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490821/ https://www.ncbi.nlm.nih.gov/pubmed/37688090 http://dx.doi.org/10.3390/s23177632 |
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author | Ferraris, Stefano Meo, Rosa Pinardi, Stefano Salis, Matteo Sartor, Gabriele |
author_facet | Ferraris, Stefano Meo, Rosa Pinardi, Stefano Salis, Matteo Sartor, Gabriele |
author_sort | Ferraris, Stefano |
collection | PubMed |
description | Machine learning can be used for social good. The employment of artificial intelligence in smart agriculture has many benefits for the environment: it helps small farmers (at a local scale) and policymakers and cooperatives (at regional scale) to take valid and coordinated countermeasures to combat climate change. This article discusses how artificial intelligence in agriculture can help to reduce costs, especially in developing countries such as Côte d’Ivoire, employing only low-cost or open-source tools, from hardware to software and open data. We developed machine learning models for two tasks: the first is improving agricultural farming cultivation, and the second is water management. For the first task, we used deep neural networks (YOLOv5m) to detect healthy plants and pods of cocoa and damaged ones only using mobile phone images. The results confirm it is possible to distinguish well the healthy from damaged ones. For actions at a larger scale, the second task proposes the analysis of remote sensors, coming from the GRACE NASA Mission and ERA5, produced by the Copernicus climate change service. A new deep neural network architecture (CIWA-net) is proposed with a U-Net-like architecture, aiming to forecast the total water storage anomalies. The model quality is compared to a vanilla convolutional neural network. |
format | Online Article Text |
id | pubmed-10490821 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104908212023-09-09 Machine Learning as a Strategic Tool for Helping Cocoa Farmers in Côte D’Ivoire Ferraris, Stefano Meo, Rosa Pinardi, Stefano Salis, Matteo Sartor, Gabriele Sensors (Basel) Article Machine learning can be used for social good. The employment of artificial intelligence in smart agriculture has many benefits for the environment: it helps small farmers (at a local scale) and policymakers and cooperatives (at regional scale) to take valid and coordinated countermeasures to combat climate change. This article discusses how artificial intelligence in agriculture can help to reduce costs, especially in developing countries such as Côte d’Ivoire, employing only low-cost or open-source tools, from hardware to software and open data. We developed machine learning models for two tasks: the first is improving agricultural farming cultivation, and the second is water management. For the first task, we used deep neural networks (YOLOv5m) to detect healthy plants and pods of cocoa and damaged ones only using mobile phone images. The results confirm it is possible to distinguish well the healthy from damaged ones. For actions at a larger scale, the second task proposes the analysis of remote sensors, coming from the GRACE NASA Mission and ERA5, produced by the Copernicus climate change service. A new deep neural network architecture (CIWA-net) is proposed with a U-Net-like architecture, aiming to forecast the total water storage anomalies. The model quality is compared to a vanilla convolutional neural network. MDPI 2023-09-03 /pmc/articles/PMC10490821/ /pubmed/37688090 http://dx.doi.org/10.3390/s23177632 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ferraris, Stefano Meo, Rosa Pinardi, Stefano Salis, Matteo Sartor, Gabriele Machine Learning as a Strategic Tool for Helping Cocoa Farmers in Côte D’Ivoire |
title | Machine Learning as a Strategic Tool for Helping Cocoa Farmers in Côte D’Ivoire |
title_full | Machine Learning as a Strategic Tool for Helping Cocoa Farmers in Côte D’Ivoire |
title_fullStr | Machine Learning as a Strategic Tool for Helping Cocoa Farmers in Côte D’Ivoire |
title_full_unstemmed | Machine Learning as a Strategic Tool for Helping Cocoa Farmers in Côte D’Ivoire |
title_short | Machine Learning as a Strategic Tool for Helping Cocoa Farmers in Côte D’Ivoire |
title_sort | machine learning as a strategic tool for helping cocoa farmers in côte d’ivoire |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490821/ https://www.ncbi.nlm.nih.gov/pubmed/37688090 http://dx.doi.org/10.3390/s23177632 |
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