Ripening Stage Detection of Mangoes by Size and Maturity Using Hybrid (1D-2D) Convolution-Based Adaptive Densenet with Attention Mechanism

The classification of mangoes' ripening stage is the major aspect of supplying better fruit grade to buyers, which is a standard necessity of the fruit processing industry. The optical examination, which is manually done, takes to instability, and it involves a lot of work by human workers. To harvest better-quality mangoes, the measurement of maturity is supremely important. During the development and storing at the context temperature, the differentiation in the surface color, size, Total Soluble Solids (TSS) content, firmness and sphericity are analyzed. To tackle the challenges that formed in the classical mango ripening stage, detection approaches are solved by using the newly proposed deep learning approach to identify the maturity state of mangoes. The required mango pictures are taken from the standard databases, and these pictures are given to the image preprocessing to improve image quality and contrast. The improved quality images are applied to the feature extraction section, where the size, shape and color feature are obtained. After that, the ripening of mango is performed through the "Hybrid (1D-2D) Convolution-based Adaptive DenseNet with Attention Mechanism (HCADNet-AM)" to get efficient classification results. The extracted characteristic is applied as the input to the 1D convolution, and the mango images are given as the input for 2D convolution for classifying the maturity stages. The parameter optimization takes place via the Fitness-aided Random Function in Red Panda Optimization (FRF-RPO) during the ripening stage to improve the performance. The research output is validated with conventional ripening techniques to ensure effectiveness.