Linking Citrus Fruit Cracking Intensity to Plant Water Status: Insights from UAV-Derived Metrics Validated by Ground-Based Data

Abstract

Citrus fruit cracking, a physical failure of the peel, causes yield losses of 10% to 35%, peaking during October-November. Water status of the tree and water flow into the fruit influence this phenomenon. with excessive irrigation during critical fruit development stages exacerbates cracking. As part of the EU-Horizon CrackSense project, this study is aimed to link citrus tree plant water status (PWS) to fruit cracking, emphasizing how deficit irrigation can reduce yield loss due to cracking. Using UAV and eco-physiological measurements, we developed models to predict PWS and its relationship with cracking intensity early in the season. The study, conducted in 2023-2024 in a commercial orchard near Kfar Chabad, Israel, tested four irrigation treatments: control, defined as the standard irrigation, two deficits irrigations regimes (50% of control) early and late in the season, and excessive irrigation (150% of control) throughout the season. Ground-based measurements included fruit and trunk diameter, stem water potential (SWP), stomatal conductance, plant area index (PAI), and growth rate (TG). UAV flights integrated multispectral, thermal, and LiDAR sensors to capture spatial-temporal variability in PWS. Canopy metrics, such as height, volume, LiDAR-based PAI, and spectral and thermal indices, were incorporated into PWS models. Results revealed significant differences in TG, SWP, and stomatal conductance for 50% of early and late deficit irrigation treatments compared to other treatments. Random forest models demonstrated strong predictive performance for SWP (R² > 0.77) and TG (R² > 0.76). LiDAR-derived PA correlated highly with field optical measurements (R² = 0.92), yield (R² = 0.67), and cracked fruit percentages (R² > 0.50). This study underscores the importance of precise irrigation management in reducing fruit cracking. It highlights the potential of remote sensing systems for predicting cracking and managing water status at the tree level. The developed models equip farmers with tools to apply controlled water stress, minimizing cracking and improving yield.

Plant phenotyping relevance

UAVのマルチスペクトル・熱・LiDAR計測から樹体の水分状態、成長、樹冠形質、裂果状態を推定するモデルを開発し、地上計測で検証しており、表現型取得手法が研究の中心である。

Code and data availability

公開論文であることは確認できましたが、現在の公式API・許可済み取得経路では本文を自動取得できませんでした。

No evidence-backed public reproduction asset is currently recorded.