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Multi-modal approach for cherry tomato (Solanum lycopersicum) maturity assessment through deep learning and unmanned vehicles (UV) integration
Thesis Abstract:
In modern agriculture, cherry tomato (Solanum lycopersicum) is an economically significant commodity. However, their small size and unique characteristics, including their brief lifespan and vulnerability to damage, pose challenges in crop monitoring and harvesting. Up to date, most previous studies focused on developing and optimizing deep learning models for detecting tomatoes at different maturity levels. However, the impact of factors like distance and confidence threshold on the detection performance of object detectors is often ignored and left uninvestigated. Therefore, this study proposed an autonomous method of detecting maturity levels of greenhouse-grown cherry tomatoes using UAV and YOLOv8. Subsequently, the impact of the aforementioned factors was investigated. DJI Tello drone was utilized to setup a UDP-based communication, enabling efficient data transmission and UAV control. For the model training, a cherry tomato dataset comprising images from different modalities were used. The results of fine-tuning and ablation studies demonstrated the effectiveness of incorporating coordinate attention blocks and a bounding box regression loss with dynamic focusing mechanism (WIoU) loss function, achieving high precision (90.2%), recall (88.5%), F1-score (89.34%), and mAP (93.7%) for the ripe detection model. The developed model, YOLOv8n +CA+ WIoU, was used for detection and tracking together with a fine-tuned BoT-SORT tracking algorithm. The results revealed the efficacy of BoT-SORT for tracking the cherry tomatoes by achieving Multi-Object Tracking Accuracy (MOTA) ranging from 74 ~ 87% and Counting Accuracy ranging from 60% ~ 85%. Moreover, the results of the study determined the implications that for cherry tomatoes, low threshold (45% ~ 50%) leads to higher sensitivity, while higher threshold (75% ~ 80%) leads to higher specificity and increased tracking performance. However, it was observed that the YOLOv8n + CA+ WIoU algorithm started to display decrease in detection certainty at a range of 0.4 m to 1.0 m object-to-camera distance, particularly in the presence of occlusion and close proximity between tomatoes with the same class. The overall findings highlight the potential of using UAV-based systems and advanced deep learning models for efficient and accurate monitoring of the maturity level of cherry tomatoes in greenhouse settings. Furthermore, subsequent findings also demonstrate the critical impact of object-tocamera distance, confidence threshold, and occlusion on detection performance. Addressing these factors are essential for maximizing the accuracy and reliability of UAV-based agricultural monitoring systems, reinforcing the feasibility and effectiveness of these technologies in real-world and industrial applications.