This is like giving a farmer a super-smart camera and set of sensors that can look at cashew fruits and say: “these are ripe, these are not yet ready, and these are low quality” automatically, instead of relying on workers to visually inspect each fruit by hand.
Manual grading of cashew fruit maturity and quality is slow, inconsistent, and labor‑intensive. This work reviews how AI models and sensors can automate detection of ripeness and quality in cashew fruits, helping farmers and processors harvest at the right time, reduce waste, and improve product consistency.
Domain-specific datasets of cashew images and sensor readings, paired with expert labels of maturity and quality, can become a strong moat. Integrating these models into farm machinery, packhouse lines, or mobile apps creates sticky workflows and switching costs for growers and processors.
Hybrid
Unknown
High (Custom Models/Infra)
Need for large, well-labeled cashew-specific datasets across varieties, lighting, and field conditions; deploying models on edge devices with limited compute and power in farm environments.
Early Adopters
Focuses narrowly on cashew fruit maturity and quality, reviewing not just models but also sensor setups and concrete farming applications, which makes it more specialized than generic crop-vision or fruit-quality AI literature.