Detection of jelly orange granulation disease using a dual-input Resnet-Transformer model (DresT) based on acoustic vibration images and a novel acoustic vibration device

Abstract

Granulation is a common internal disease in citrus fruits, and it is difficult to distinguish fruits with granulation disease from their appearance. In this study, a novel acoustic vibration device based on a micro-LDV, a microphone and a resonance speaker was employed to collect acoustic vibration response signals of "Aiyuan 38" jelly orange. The one-dimensional acoustic vibration response signal was converted into acoustic vibration images, and a double-input Resnet-Transformer network (DresT) was constructed for extracting deep features in acoustic vibration images for identifying jelly-orange granulation disease. Firstly, train Drest and Resnet50 models using acoustic vibration images and compare the performance of Drest with that of Resnet50 (based on CNN). Then PLS-DA and SVM models are trained using acoustic vibration image texture features or acoustic vibration spectral features, and the performance is compared with the DresT model. The results showed that the DresT model trained using acoustic vibration images can accurately identify jelly orange granulation disease with a detection accuracy of 99.31 %. The F₁ of the model is 99.5 %, the accuracy is 99.01 %, and the recall is 100 %.

Plant phenotyping relevance

柑橘果実の内部病害を音響振動画像と専用デバイス、深層学習モデルで推定する手法が研究の中心であり、植物の病害状態を直接評価している。

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