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Drone-Based Harvest Data Prediction Can Reduce On-Farm Food Loss and Improve Farmer Income
On-farm food loss (i.e., grade-out vegetables) is a difficult challenge in sustainable agricultural systems. The simplest method to reduce the number of grade-out vegetables is to monitor and predict the size of all individuals in the vegetable field and determine the optimal harvest date with the s...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AAAS
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10484300/ https://www.ncbi.nlm.nih.gov/pubmed/37692103 http://dx.doi.org/10.34133/plantphenomics.0086 |
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author | Wang, Haozhou Li, Tang Nishida, Erika Kato, Yoichiro Fukano, Yuya Guo, Wei |
author_facet | Wang, Haozhou Li, Tang Nishida, Erika Kato, Yoichiro Fukano, Yuya Guo, Wei |
author_sort | Wang, Haozhou |
collection | PubMed |
description | On-farm food loss (i.e., grade-out vegetables) is a difficult challenge in sustainable agricultural systems. The simplest method to reduce the number of grade-out vegetables is to monitor and predict the size of all individuals in the vegetable field and determine the optimal harvest date with the smallest grade-out number and highest profit, which is not cost-effective by conventional methods. Here, we developed a full pipeline to accurately estimate and predict every broccoli head size (n > 3,000) automatically and nondestructively using drone remote sensing and image analysis. The individual sizes were fed to the temperature-based growth model and predicted the optimal harvesting date. Two years of field experiments revealed that our pipeline successfully estimated and predicted the head size of all broccolis with high accuracy. We also found that a deviation of only 1 to 2 days from the optimal date can considerably increase grade-out and reduce farmer's profits. This is an unequivocal demonstration of the utility of these approaches to economic crop optimization and minimization of food losses. |
format | Online Article Text |
id | pubmed-10484300 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | AAAS |
record_format | MEDLINE/PubMed |
spelling | pubmed-104843002023-09-08 Drone-Based Harvest Data Prediction Can Reduce On-Farm Food Loss and Improve Farmer Income Wang, Haozhou Li, Tang Nishida, Erika Kato, Yoichiro Fukano, Yuya Guo, Wei Plant Phenomics Research Article On-farm food loss (i.e., grade-out vegetables) is a difficult challenge in sustainable agricultural systems. The simplest method to reduce the number of grade-out vegetables is to monitor and predict the size of all individuals in the vegetable field and determine the optimal harvest date with the smallest grade-out number and highest profit, which is not cost-effective by conventional methods. Here, we developed a full pipeline to accurately estimate and predict every broccoli head size (n > 3,000) automatically and nondestructively using drone remote sensing and image analysis. The individual sizes were fed to the temperature-based growth model and predicted the optimal harvesting date. Two years of field experiments revealed that our pipeline successfully estimated and predicted the head size of all broccolis with high accuracy. We also found that a deviation of only 1 to 2 days from the optimal date can considerably increase grade-out and reduce farmer's profits. This is an unequivocal demonstration of the utility of these approaches to economic crop optimization and minimization of food losses. AAAS 2023-09-07 /pmc/articles/PMC10484300/ /pubmed/37692103 http://dx.doi.org/10.34133/plantphenomics.0086 Text en Copyright © 2023 Haozhou Wang et al. https://creativecommons.org/licenses/by/4.0/Exclusive licensee Nanjing Agricultural University. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Article Wang, Haozhou Li, Tang Nishida, Erika Kato, Yoichiro Fukano, Yuya Guo, Wei Drone-Based Harvest Data Prediction Can Reduce On-Farm Food Loss and Improve Farmer Income |
title | Drone-Based Harvest Data Prediction Can Reduce On-Farm Food Loss and Improve Farmer Income |
title_full | Drone-Based Harvest Data Prediction Can Reduce On-Farm Food Loss and Improve Farmer Income |
title_fullStr | Drone-Based Harvest Data Prediction Can Reduce On-Farm Food Loss and Improve Farmer Income |
title_full_unstemmed | Drone-Based Harvest Data Prediction Can Reduce On-Farm Food Loss and Improve Farmer Income |
title_short | Drone-Based Harvest Data Prediction Can Reduce On-Farm Food Loss and Improve Farmer Income |
title_sort | drone-based harvest data prediction can reduce on-farm food loss and improve farmer income |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10484300/ https://www.ncbi.nlm.nih.gov/pubmed/37692103 http://dx.doi.org/10.34133/plantphenomics.0086 |
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