Silverleaf whitefly (SLW), mites and aphids gathering under cotton leaves are not easily detected manually because of diurnal movement or patchy distribution. Left untreated, they can reduce the value of a cotton crop through feeding damage, or by depositing honeydew that reduces quality. Effective management relies on their accurate and timely detection and quantification to identify the need for treatment. This project has successfully developed a proof-of-concept machine-vision sensing approach able to discriminate pest infestations with a beta app now being tested in the field. The algorithm includes machine learning based on thousands of SLW nymph image samples. The sensor has the potential to further improve management of these key pests.