Developing C-LSTM model for evaluating moisture content of carrot slices during drying

Abstract

Moisture content (MC) is an important indicator for evaluating the quality of agricultural products during drying. The rapid detection of MC helps to realize optimization of the configuration and online adjustment of drying process parameters, ensuring the quality of the final product and achieving green and efficient drying. As an effective tool for rapid and nondestructive detection of agricultural product quality, the use of hyperspectral/multispectral image (HSI/MSI) for MC evaluation of agricultural products/food during drying has been extensively studied. However, how to extract effective features from HSI/MSI and build a function relationship between these features and MC is still a challenging problem. The main work of this study is to develop a convolutional – long short term memory(C-LSTM) prediction model for MC evaluation of drying agricultural products based on HSI/MSI. The proposed C-LSTM model uses the convolution module to automatically extract the features of the HSI/MSI, and applies LSTM to explore the correlation information between the features extracted from each wavelength and the MC, thus finally realize the accurate prediction of MC. The carrot slices were dried with different drying time by using two different drying equipment (electric thermostatic drying oven drier and radio frequency- hot air drier (RF-HAD), and MSI of dried samples, covering the spectral range of 675–975 nm, were collected using multispectral imaging system based on a single shot. The best prediction results were achieved by C-LSTM models, with Rp of 0.966, RMSEP of 0.079(%) and RPD of 3.734 for oven drier, and Rp of 0.965, RMSEP of 0.085(%), and RPD of 3.604 for RF-HAD, compared to other four traditional models and LSTM model. The experimental results indicated that C-LSTM model is a potential modeling method for MC detection of agricultural products based on HSI/MSI.