Early Detection of Jujube Shrinkage Disease by Multi-Source Data on Multi-Task Deep Network.

Abstract

In the arid cultivation region of Xinjiang, China, shrinkage disease severely compromises the quality, yield, and market value of jujube. Published research has achieved high accuracy in detecting larger lesions using RGB imaging and hyperspectral imaging (HSI). However, these methods lack sensitivity in detecting early and subtle symptoms of disease. In this study, a multi-source data fusion strategy combining RGB imaging and HSI was proposed for non-destructive and high-precision detection of early-stage jujube shrinkage disease. Firstly, a total of 317 fruits of the 'Junzao' cultivar were collected during multiple stages of natural infection, covering early-stage shrinkage disease detection across different growth stages, including both green and mature red fruits. Secondly, morphological features were extracted from RGB images in multiple dimensions, while a three-stage feature selection strategy combining Principal Component Analysis (PCA), the Successive Projections Algorithm (SPA), and the Genetic Algorithm (GA) was implemented to identify four key wavelengths from HSI. Thirdly, a hybrid convolutional neural network-multilayer perceptron (CNN-MLP) architecture was constructed, with dynamic feature weighting employed to achieve effective multimodal fusion and optimize detection performance. Experimental results demonstrated that compared to the MLP and CNN models, the proposed method achieved approximately 8.0% and 5.4% improvements in accuracy and 38.6% and 32.4% improvements in F1 scores, respectively. It offers a robust and scalable solution for early disease detection and postharvest quality assessment in jujube production.

Plant phenotyping relevance

RGB画像・HSIから果実の病斑形態と分光特徴を抽出し、マルチモーダル深層学習で植物病害状態を検出する手法の開発・性能評価が中心であるため。

Code and data availability

The paper's Data Availability Statement points to a public GitHub repository containing the study's dataset (RGB images and hyperspectral data of jujube fruits). No separate analysis code availability is stated, but the deposited dataset is a paper-specific, publicly actionable asset.