Drone-Based Harvest Data Prediction Can Reduce On-Farm Food Loss and Improve Farmer Income.

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.

Plant phenotyping relevance

ドローンリモートセンシングと画像解析により、個々のブロッコリー頭部サイズを自動・非破壊推定するパイプラインを開発・検証しており、植物形質取得が中心的です。

Code and data availability

The authors' full phenotyping/analysis pipeline source code is publicly available on GitHub (UAVbroccoli). Original drone image data (224 GB for 2020, 72 GB for 2021) exist but are only available upon request via Google Drive. Generic tools (YOLOv5, BiSeNet, labelme, EasyIDP, scikit-image) are third-party libraries, so