Enhancing pineapple cultivar classification: a framework for image quality, feature extraction, and algorithmic refinement

Accurate classification of pineapple cultivars is hindered by limitations in image acquisition, feature extraction, and classification algorithms. This study identifies these technical challenges and proposes methodological improvements to support reliable autonomous recognition systems. Key findings reveal deficiencies in current image datasets, leading to a proposed standardised acquisition protocol involving consistent lighting, optimal camera positioning, and suitable file formats. The HSV colour space is validated as more effective for extracting skin features, with its threshold values and post-processing steps significantly reducing classification errors. The proposed algorithmic refinements integrate chromatic and morphological attributes, particularly surface area and optimise logical operators to enhance accuracy. The study addresses two main research gaps: precise quantification of skin colour and the development of robust classification frameworks. Future work will emphasise empirical validation and the deployment of the YOLOv7 model for real-time, on-site assessment of fruit maturity in field conditions. These contributions hold strong implications for advancing precision agriculture and improving post-harvest processing.