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Processing tomato (Lycopersicon esculentum Mill.) is rich in vitamins and lycopene, which is favored by consumers. In this study, near-infrared hyperspectral imaging (HSI) technology (980–1660 nm) was used to detect the firmness, soluble solids, lycopene, and titratable acid content of processing tomatoes and to classify fruits at three maturity stages. Savitzky-Golay (SG) smoothing was used to reduce the noise of hyperspectral images. The average spectrum of the tomato fruit was extracted for model development. Random forest (RF), partial least squares (PLS), and recurrent neural network (RNN) were used to develop models for predicting the four quality attributes and identifying the maturity level. Results showed that the RNN model had a classification accuracy of 40% higher than RF and 17% higher than PLS. In the prediction of quality parameters, RNN models had the highest R2 value (>0.87), followed by PLS and RF models. Important wavelengths were identified by calculating its contribution values and were used to interpret the model. The results illustrated that near-infrared hyperspectral imaging technology combined with deep learning could effectively predict the quality and maturity of processing tomatoes. The work can provide a perspective on the application of HSI as a nondestructive testing approach for other agricultural products. • Use of hyperspectral imaging to predict multi-quality of processing tomatoes. • Use of hyperspectral imaging to classify maturity of processing tomatoes. • Comparison of recurrent neural networks with traditional machine learning methods. • Identification of important wavelengths in contributing to model performance. [ABSTRACT FROM AUTHOR]

• Near-infrared hyperspectral imaging was used to identify coated maize varieties. • Conventional and deep learning methods were used to build classification models. • Feature extraction and fusion were evaluated and compared with good performances. • Deep learning based data analysis was efficient to identify coated maize varieties. Tracing the varieties of seeds is of great importance for the seed industry. Maize kernels for planting are generally coated to protect kernels from fungi and insects. In this study, near-infrared hyperspectral imaging ranging from 874 nm to 1734 nm was used to identify the varieties of coated maize kernels. Spectral data were extracted and preprocessed. Logistic regression (LR) and support vector machine (SVM), convolutional neural network (CNN), recurrent neural network (RNN) and Long Short-Term Memory (LSTM) were used to build classification models. Furthermore, principal component analysis (PCA), CNN, RNN and LSTM were adopted to extract features. The extracted features were fused as the inputs of the classification models. Classification models using full spectra, extracted features and fused features obtained performances with the classification accuracy over 90% in the calibration, validation and prediction sets of most models. Models using extracted features obtained equivalently or slightly worse results than those using full spectra. The models using fused features all obtained good performances, with the classification accuracy over 90% in all sets. The overall results illustrated that near-infrared hyperspectral imaging with deep learning methods was a useful alternative for identifying coated maize varieties. [ABSTRACT FROM AUTHOR]