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In this study, moisture contents and product quality of Pu‐erh tea were predicted with deep learning‐based methods. Images were captured continuously in the sun‐drying process. Environmental parameters (EP) of air humidity, air temperature, global radiation, wind speed, and ultraviolet radiation were collected with a portable meteorological station. Sensory scores of aroma, flavor, liquor color, residue, and total scores were given by a trained panel. Convolutional neural network (CNN) and gated recurrent unit (GRU) models were constructed based on image information and EP, which were selected in advance using the neighborhood component analysis (NCA) algorithm. The evolved models based on deep‐learning methods achieved satisfactory results, with RMSE of 0.4332, 0.2669, 0.7508 (also with R2 of.9997,.9882,.9986, with RPD of 53.5894, 13.1646, 26.3513) for moisture contents prediction in each batch of tea, tea at different sampling periods, the overall samples, respectively; and with RMSE of 0.291, 0.2815, 0.162, 0.1574, 0.3931 (also with R2 of.9688,.9772,.9752,.9741,.8906, with RPD of 5.6073, 6.5912, 6.352, 6.1428, 4.0045) for final quality prediction of aroma, flavor, liquor color, residue, total score, respectively. By analyzing and comparing the RMSE values, the most significant environmental parameters (EP) were selected. The proposed combinations of different EP can also provide a valuable reference in the development of a new sun‐drying system. [ABSTRACT FROM AUTHOR]

The ageing rate of cellulose insulation materials (paper/pressboard) in oil‐impregnated transformers is largely determined by moisture. In this paper, several experiments were conducted under controlled laboratory conditions to quantitatively diagnose the amount of moisture in cellulose insulation using frequency domain spectroscopy (FDS) technique, which has been proven to be an effective tool to predict the insulation condition in oil‐impregnated power equipment. The frequency dielectric response in oil‐impregnated pressboards, moisture content as well as test temperature, was monitored carefully, and a new dielectric characteristic parameter for the diagnosis of moisture content was proposed. In addition, the master curve technique was introduced to calibrate the temperature effect on FDS measurement results. These experiments revealed that a strong correlation exists between frequency dielectric parameter and moisture content in cellulose insulation. Such correlation can be utilised for better prediction of moisture content in paper–oil insulation. A modified assessment method for the diagnosis of moisture content in paper–oil insulation was investigated and its effectiveness was also demonstrated using a case. [ABSTRACT FROM AUTHOR]