Evaluation of apple inner quality based on improved deep belief network

Objective: In order to resolve a lot of redundant information and low precision of apple internal quality evaluation existed in apple near infrared spectroscopy, improving the precision of apple internal quality evaluation. Methods: A new apple inner quality evaluation model based on deep belief network （DBN） and grey wolf optimization algorithm was proposed. According to the characteristic of high dimension and complexity of apple spectral data, the method of selecting characteristic wavelengths of apple spectral data was determined by comparing the results of selecting characteristic wavelengths of full-band, principal component analysis and continuous projection. The parameters of DBN model were optimized by GWO Algorithm, and a continuous projection method for feature wavelength selection and GWO-DBN model for apple inner quality evaluation were proposed. Results: Compared with PSO-DBN, GA-DBN and DBN, the accuracy of apple inner quality evaluation based on GWO-DBN was the highest. Conclusion: This algorithm can effectively improve the accuracy of apple inner quality evaluation and provide a new method for apple inner quality evaluation.