Modern Trends in Deep Learning for Fruit Quality Analysis of Hyperspectral Images: A Review

Non-destructive testing techniques for fruit quality analysis have gained importance over the years. Hyperspectral image processing is one of the important non-destructive, costeffective, rapid, and reliable quality testing techniques that provide both spatial as well as spectral information. Due to the automatic feature learning ability, deep learning is one of the most promising and powerful techniques with better classification accuracy for fruit applications. This paper starts by giving an overview of the quality concept of fruits and vegetables followed by fundamentals of hyperspectral imaging, and its potential applications. Then machine learning and deep learning techniques are highlighted followed by recent research work to illustrate the potential of hyperspectral imaging for fruit and vegetable quality analysis. Lastly, the research gap and future considerations are discussed to improve the analysis and applicability of hyperspectral imaging using deep learning for the quality assessment of fruits, and vegetables