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Effective identification of tomato plant traits is crucial for timely monitoring and evaluating their growth and harvest. However, conducting stress experiments on multiple tomato genotypes introduces challenges due to the nature of the data. One of these challenges arises from an imbalanced sample distribution, potentially leading to misclassification between classes and disruptions in model recognition. This paper addresses the effect of these challenges by considering the imbalanced classes of flowers, fruits, and nodes and proposing an improved detection approach through data balancing. A novel data-balancing approach is introduced in this study to overcome the issue of imbalanced data. The proposed solution involves the implementation of a YOLOv8 deep learning model, which effectively detects flowers, fruits, and nodes in tomato plants. This model significantly enhances the ability of the algorithm to detect objects of varying sizes within complex environments. To further bolster the recognition capability of the targeted classes, the proposed model integrates a Squeeze-and-Excitation (SE) block attention module into its head architecture. This module strengthens the model recognition ability by giving increased attention to the studied classes, thereby enhancing overall detection performance. The results demonstrate that the data balancing approach successfully improves the model performance in response to the data challenges. When applying the technique of pre-training the optimal weights obtained from balanced data on imbalanced data, the SE-block module showed significant improvements in outcomes. • YOLOv8-based single-stage detector for tomato plant phenotyping. • Data balancing of nodes, fruit, and flowers on a challenging tomato images dataset. • Impact of attention mechanisms in the localization of small phenotypical traits. • Performance comparison of several optimization strategies, preprocessing methods, and stand-alone detectors. [ABSTRACT FROM AUTHOR]

Plant phenotyping is the study of complex plant traits to evaluate its status depending on the life-cycle conditions. Often, these evaluations are carried out by human operators, and the accuracy could be biased by their experience and skill, especially when dealing with huge amounts of data produced by high-throughput phenotyping (HTP) platforms. With the rapid development of key enabling technologies, HTP is only made possible by the vast amounts of data made available by computer vision systems. In this scenario, artificial intelligence algorithms play a key role in the automation, standardization, and quantitative analysis of large data. This paper focuses on detecting tomato plants phenotyping traits using single-stage detectors (either stand-alone or ensemble) based on YOLOv5, aiming to effectively identify nodes, fruit, and flowers on a challenging dataset acquired during a stress experiment conducted on multiple tomato genotypes. Results demonstrate that the models achieve relatively high scores, considering the particular challenges of the input images in terms of object size, similarity between objects, and their color. • YOLOv5-based deep learning models for tomato plant phenotyping. • Performance evaluation using stand alone detectors and ensemble strategies. • Identification of nodes, fruits and flowers on a challenging image dataset (different object size, similarity between objects and their color). [ABSTRACT FROM AUTHOR]