Evaluating the Single-Shot MultiBox Detector and YOLO Deep Learning Models for the Detection of Tomatoes in a Greenhouse

The development of robotic solutions for agriculture requires advanced perception capabilities that can work reliably in any crop stage. For example, to automatise the tomato harvesting process in greenhouses, the visual perception system needs to detect the tomato in any life cycle stage (flower to the ripe tomato). The state-of-the-art for visual tomato detection focuses mainly on ripe tomato, which has a distinctive colour from the background. This paper contributes with an annotated visual dataset of green and reddish tomatoes. This kind of dataset is uncommon and not available for research purposes. This will enable further developments in edge artificial intelligence for in situ and in real-time visual tomato detection required for the development of harvesting robots. Considering this dataset, five deep learning models were selected, trained and benchmarked to detect green and reddish tomatoes grown in greenhouses. Considering our robotic platform specifications, only the Single-Shot MultiBox Detector (SSD) and YOLO architectures were considered. The results proved that the system can detect green and reddish tomatoes, even those occluded by leaves. SSD MobileNet v2 had the best performance when compared against SSD Inception v2, SSD ResNet 50, SSD ResNet 101 and YOLOv4 Tiny, reaching an F1-score of 66.15, an mAP of 51.46 and an inference time of 16.44ms with the NVIDIA Turing Architecture platform, an NVIDIA Tesla T4, with 12 GB. YOLOv4 Tiny also had impressive results, mainly concerning inferring times of about 5ms.

Light-Controlled Fruit Pigmentation and Flavor Volatiles in Tomato and Bell Pepper

Light is a major environmental factor affecting the regulation of secondary metabolites, such as pigments and flavor. The Solanaceae plant family has diverse patterns of fruit metabolisms that serve as suitable models to understand the molecular basis of its regulation across species. To investigate light-dependent regulation for fruit pigmentation and volatile flavors, major fruit pigments, their biosynthetic gene expression, and volatiles were analyzed in covered fruits of tomato and bell pepper. Immature covered fruits were found to be ivory in color and no chlorophyll was detected in both plants. The total carotenoid content was found to be reduced in ripe tomato and bell pepper under cover. Naringenin chalcone decreased more than 7-fold in ripe tomato and total flavonoids decreased about 10-fold in immature and ripe pepper fruit under light deficiency. Light positively impacts fruit pigmentation in tomato and bell pepper by regulating gene expression in carotenoid and flavonoid biosynthesis, especially phytoene synthase and chalcone synthase, respectively. Nineteen volatile flavors were detected, and seven of these exhibited light-dependent regulations for both ripe tomato and pepper. This study will help in improving fruit quality and aid future research works to understand the molecular mechanisms regulating the influence of light-dependency on pigments and flavor volatiles.