AI HD Color Sorter Empowers Green Pea Processing with Flexible Response to Diverse Production Requirements As consumer demand for exports and frozen fruits and vegetables continues to diversify, the challenges facing green pea processors are shifting. In the past, the focus was on “how to increase sorting throughput.” Today, the greater challenge is: while maintaining throughput, how to achieve high-precision, low-loss, customized sorting according to the quality requirements of different customers and different orders. From “Batch Sorting” to “Customized Sorting” Customized sorting is not simply “one-size-fits-all” sorting — it translates customer requirements into executable standards on the sorting end. The equipment matches different recognition strategies according to different customer standards, achieving more precise and stable sorting within the AI intelligent sorting solution. Simply put: whatever grade of green peas the customer wants, the machine sorts to that standard. Why Do Green Pea Processors Urgently Need Customized Sorting Solutions? Many processing companies in the frozen vegetable and grain export sectors simultaneously serve domestic supermarkets and export customers from different countries, with vastly different order standards: some orders only require removing seriously defective peas, while export orders strictly control defects such as wormholes, breakage, subtle yellow spots, and localized browning. As product categories and customer orders continue to diversify, companies face numerous production challenges: How to quickly switch sorting standards for different grade orders? How to balance removing wormholes, half kernels, and soybean impurities while controlling false rejection of good peas and ensuring yield? How to stably control the defect rate of finished products when incoming material quality fluctuates across batches? All of these require sorting equipment with stronger precision response and fine defect recognition capabilities. Sorting Test: Beyond Order Standards for Green Pea Processing In this green pea sample test: total raw material weight was 925g, with an overall impurity rate of 25%, including 20% wormhole-damaged peas and 5% half soybean kernels. The green pea raw material mixed with wormholes, broken half kernels, and soybeans was fed into the HAWIT AI HD Deep Learning Color Sorter. The resulting finished green peas were plump and uniform in color, with wormholes, broken half kernels, off-color soybeans, and other impurities effectively removed, achieving a finished product sorting purity of 99.6%.     Whatever grade of green peas the customer wants, the machine sorts to that standard. This is HAWIT’s customized sorting solution for green pea processing — AI-driven recognition, flexible model switching, balancing sorting precision and raw material yield, adapting to the ever-changing demands of export and frozen fruit and vegetable orders. HAWIT specializes in AI visual intelligent sorting equipment, developing customized deep learning sorting models for grains, nuts, and frozen fruits and vegetables. If you also face challenges with difficult-to-identify material defects or hard-to-control yield rates, we welcome you to send samples for free testing and verify the sorting results on site.