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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.
Afiliação
  • Wang H; Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan.
  • Li T; Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan.
  • Nishida E; Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan.
  • Kato Y; Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan.
  • Fukano Y; Graduate School of Horticulture, Chiba University, Chiba, Japan.
  • Guo W; Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan.
Plant Phenomics ; 5: 0086, 2023.
Article em En | MEDLINE | ID: mdl-37692103
ABSTRACT
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.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article