Wang Haozhou, Li Tang, Nishida Erika, Kato Yoichiro, Fukano Yuya, Guo Wei
Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan.
Graduate School of Horticulture, Chiba University, Chiba, Japan.
Plant Phenomics. 2023 Sep 7;5:0086. doi: 10.34133/plantphenomics.0086. eCollection 2023.
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 ( > 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.
农场食物损失（即次品蔬菜）是可持续农业系统中一项艰巨的挑战。减少次品蔬菜数量的最简单方法是监测和预测菜地里所有蔬菜个体的大小，并确定次品数量最少且利润最高的最佳收获日期，但传统方法在成本效益方面并不理想。在此，我们开发了一套完整的流程，利用无人机遥感和图像分析自动且无损地准确估计和预测每一个西兰花头的大小（超过3000个）。将个体大小输入基于温度的生长模型，预测最佳收获日期。两年的田间试验表明，我们的流程成功地高精度估计和预测了所有西兰花的头大小。我们还发现，收获日期与最佳日期仅相差1至2天就会显著增加次品率并降低农民利润。这明确证明了这些方法在经济作物优化和减少食物损失方面的实用性。
