Optimizing Agricultural Product Quality With Machine Learning-Enabled Grading Systems
Abstract
Coherence Shock Filtering (CSF), which is used to improve the sharpness and decrease blurriness of fruit photos, is the first step in the suggested approach. The pictures are then subjected to a Discrete Wavelet Transform (DWT) in order to identify key characteristics for additional categorization. The Marine Predators Algorithm (MPA) is used for optimal feature selection in order to guarantee that only the most pertinent features are taken into account. The Water Wheel Plant Algorithm (WWPA) handles the classification stage, classifying the fruit photos as either healthy or unhealthy. The model outperformed previous approaches with remarkable accuracy rates of 99.9% and 99.98% on two well-known datasets: Fruits 360 and Fresh and Rotten Fruits.
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