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Cashew apples, a byproduct of the cashew nut industry with an estimated global production of 38 million tonnes, are rich in several essential nutrients and are widely processed into juice, syrup, wine, pickles, and other value-added products. However, their morphological and physicochemical properties vary significantly across varieties, complicating in-field characterization, maturity assessment, and biochemical analysis. These challenges originate from the reliance on costly chemicals, skilled manpower, limited time, and sophisticated equipment. This study employed a user-developed computer vision-based ImageJ batch processing plugin to assess 15 physicochemical properties across six diverse cashew apple varieties from the images of slices and whole samples. Five methodologies—color-grid, surface morphology, gray level co-occurrence matrix, local binary pattern, and color-indices—generated image-based metrics rapidly (2.87±0.79 s/image). Correlation of wet chemistry with image-based parameters, linear modeling, and wet chemistry parameters prediction with an independent dataset were successfully performed, and successfully modeled properties include acidity, antioxidant, carbohydrates, carotenoids, crude fat, flavonoids, pH, phenolics, proteins, tannins, vitamin C, and total soluble solids. The results demonstrated the feasibility of predicting 80 physicochemical properties of cashew apples (R2>0.5), and that can be successfully modeled include acidity, antioxidant, carbohydrates, carotenoids, crude fat, flavonoids, pH, phenolics, proteins, tannins, vitamin C, and total soluble solids. This methodology offers a faster, safer, and cost-effective alternative to wet chemistry and can be extended to other horticultural crops.