Estimating spinach fresh weight from UAV multispectral imagery: A multitask attention U-Net and regression approach

Abstract

Monitoring plant weight during its development cycle is crucial for effective growth monitoring; it provides valuable information on plant’s health and development. Weight data are essential to determine the optimal harvest time and ensure that plants are harvested when they are at their best. This work aimed to design and implement a workflow that allows the study of growth and development variables in a spinach crop cycle using high spatial resolution multispectral images acquired with an Unmanned Aerial Vehicle (UAV). We based this workflow on applying a multitask attention U-Net model for plant segmentation and advanced statistical analysis, including regression methods and hierarchical analysis, to build and evaluate a Random Forest (RF) model and a Generalized Linear Model (GLM) for spinach fresh weight estimation. The segmentation model achieved a mean Intersection over Union (mIoU) of 0.90 and an F-score of 0.93 against manually drawn labels. Experimental validation of the estimation of the fresh weight of spinach plants from geometric and spectral characteristics with an R2 of 0.90 and RMSE = 23.48 g for the RF model constructed from explanatory variables found with hierarchical analysis. Results demonstrate the utility of a novel hybrid approach for analysis of multispectral imagery from UAVs in crop monitoring.

Plant phenotyping relevance

UAVマルチスペクトル画像から植物セグメンテーションと幾何・スペクトル特徴を抽出し、ホウレンソウの生体重を推定するワークフローを開発・検証しており、表現型取得・推定手法が中心である。

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