Phenological modeling for ecological double-cropping of grapes with rain and pest resilience strategies based on MobileNet developed by Augmented Dream Optimizer.

Abstract

Double-cropping grape systems offer enhanced land productivity but face significant challenges from climate variability, particularly rain stress and pest outbreaks during critical phenological stages. Accurate phenological prediction is essential to synchronize management practices with crop development and improve ecological resilience. This study presents a novel deep learning framework that integrates MobileNet with an augmented version of Dream Optimizer [Augmented Dream Optimizer (ADO)] to model grape phenology using satellite-derived time series of Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and rainfall from the CropHarvest dataset. The model transforms temporal data into pseudo-images for efficient spatiotemporal feature extraction, achieving 93% classification accuracy and a 6.1-day mean absolute error in stage prediction. ADO enhances convergence and generalization by optimizing key hyperparameters through a hybrid metaheuristic search. The system further identifies high-risk periods for rain damage and pest infestation, enabling proactive interventions. The model statistically significantly outperforms machine learning and deep learning baselines (p< 0.01) across three different agroecological zones [Mediterranean (California, USA, and southern Europe), subtropical (South Australia), and temperate (central Europe)] through spatially stratified fivefold cross-validation on 3,000 held-out test samples of the CropHarvest dataset. This work demonstrates the potential of optimized lightweight neural networks in sustainable viticulture, providing a scalable tool for precision management in climate-resilient double-cropping systems.

Plant phenotyping relevance

衛星時系列からブドウの生育ステージを推定する深層学習手法を開発し、交差検証とベースライン比較で性能を検証しており、植物フェノタイピング手法が中心である。

Code and data availability

The paper uses the CropHarvest dataset, but no authors' public URL, code deposit, or dataset access link is provided in the supplied blocks. The data availability statement only directs inquiries to the corresponding author. No qualifying paper-specific public asset is present.

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