Regression study on fruit-setting days of purple eggplant fruit based on in situ VIS-NIRS and attention cycle neural network.

Abstract

In the intelligent harvesting of eggplant, the lack of in situ identification technology makes it challenging to determine the maturity of purple eggplant fruit. The length of the fruit-setting date can determine when the eggplant is ready to be harvested. This study uses deep learning techniques to predict the date of fruit maturity. First, we proposed a fruit-setting days prediction method based on fruit spectroscopy and neural networks. Second, we collected the field in situ spectral data of purple eggplant fruit during 15-33 days of fruit setting using a portable spectrometer, covering 500-1000 nm. A fruit-setting time regression network combining multi-scale convolution, multi-head attention mechanism, and long short-term memory recurrent neural network was constructed using the collected in situ spectral data. The model demonstrated better fitting performance than traditional machine learning models such as backpropagation neural network, random forest, support vector machine, and partial least squares regression in the regression task of fruit-setting days. After testing various spectral preprocessing methods, the best fitting effect was found on the standard normal variate-processed dataset, with R 2 of 0.876 and RMSE(root mean square error) of 2.148 days. Furthermore, the feasibility of each model module was analyzed in depth through ablation experiments, confirming each component's role in improving the model's performance. The network attention weight was also analyzed, and the model has strong detail mining ability in a specific spectral interval. In summary, the combination of visible and near-infrared spectroscopy and attention cycle neural network is an effective method to predict the fruit-setting days of purple eggplant fruit. PRACTICAL APPLICATION: A prediction method of fruit-setting days based on fruit spectral characteristics and recurrent neural network regression was proposed. A novel approach to detecting and disclosing in situ surface VIS-NIRS reflectance data of eggplant fruit during ripening is presented for the first time. A set of long-term and short-term memory networks based on multi-scale convolution and multi-head attention mechanisms was constructed for spectral data fitting. Through the ablation test method and attention weight analysis, the function of each module in the network and the interpretability of feature extraction are explored.

Plant phenotyping relevance

紫ナス果実の成熟時期（果実設定日数）をVIS-NIRSスペクトルと深層学習で推定する手法を開発・検証しており、植物形質の取得・推定方法が研究の中心である。

Code and data availability

The supplied article blocks describe an in situ VIS-NIRS eggplant fruit-setting-days regression study with a custom attention-LSTM network, but contain no data availability statement, no public spectral dataset, no deposited code or trained model, and no supplement with phenotyping assets. All URLs present are the payw

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