Browsing by Author "Deacon, Quintus"
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- ItemDevelopment of a smart trap for the surveillance of invasive fruit flies using internet of things and artificial intelligence(Stellenbosch : Stellenbosch University, 2022-04) Deacon, Quintus; Louw, Louis; Palm, DanielENGLISH SUMMARY: Invasive fruit flies are of major concern to the agricultural industry, causing millions of rands lost due to harvest damage, trade bans, and surveillance cost. Current surveillance methods of invasive fruit flies consist of entomologists manually inspecting fruit fly traps to determine the species of fruit flies captured. This process is time intensive, expensive, and inaccurate. This study proposes a smart trap approach based on vision system technology to automate the fruit fly species classification aspect of the surveillance process. The goal of the smart trap is to serve as an early warning system of invasive fruit fly outbreaks in pest free areas. A design science methodology was followed to design a smart trap that uses a camera imbedded in traditional fruit fly bucket traps to take images of new fruit fly captures and send them to a central server. Otsu's thresholding image segmentation was compared to the EfficienDet DO object detector for segmenting fruit fly instances from the image provided by the smart trap camera. EfficeintDet DO had the highest precision, recall, and Intersection over Union of 92%, 96.88% and 90.5% respectively. Thereafter pretrained models of EfficientNet BO, MobileNet V2, and MobileNet V3 Large were trained to differentiate between the Ceratitis capitata and -quilici fruit fly species segments provided by EfficientDet DO. MobileNet V3 Large had the highest accuracy and Fl-Score of 96.55% and 96.57% respectively. The object detection and image classification algorithms were trained on Google Colab using transfer learning and image augmentation. These were then executed on a Raspberry Pi 4 Model B microcomputer. The smart trap system was accurate in distinguishing between two fruit fly species, and capable of execution on a resource constrained device. The smart trap system shows promise for low cost, easy deployment smart traps but has some issues regarding connectivity in remote areas.