Deep learning-based postharvest strawberry bruise detection under UV and incandescent light

Abstract

Bruising is one of the major defects of strawberries. Strawberry bruised areas are often visible as flattened, sunken, and discolored. Postharvest strawberries can be bruised by compression, impact, or vibration forces during harvesting, transportation, and packinghouse operations. These bruises make strawberries more vulnerable to rot or disease, thus shortening their shelf life. Bruises can also cause consumers to abandon purchase decisions. Therefore, inspecting strawberry bruises is an important procedure for commercial strawberry farms to guarantee the high quality of strawberries sent to the market. A novel technique was developed in this study to detect bruises on strawberry images captured by a color camera under incandescent and ultraviolet (UV) light. Mask Region-Based Convolutional Neural Networks (Mask R-CNN), a deep learning method, was utilized for the strawberry bruise detection. Four bruise severities were classified according to the estimated bruise ratio of every single strawberry (Minor Bruise, Light Bruise, Moderate Bruise, and Severe Bruise). The results showed that Mask R-CNN could accurately detect whether a strawberry is bruised or not under both lighting conditions. However, the strawberry bruise severity classification accuracy of the proposed machine vision technique was improved significantly under UV light compared to incandescent light. For whole strawberry bruise detection under UV light, the F1 score for detecting strawberry bruises was 0.99, and the highest F1 score for whole strawberry bruise severity classification was 0.92 for Minor Bruise. This strawberry bruise detection system can assist farmers in detecting bruised strawberries on the processing line or an automated strawberry harvester, improve the quality of strawberries that are sent to the market, and avoid shortening the shelf life of fresh strawberries.