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Plant phenotyping studies the complex characteristics of plants, with the aim of evaluating and assessing their condition and finding better exemplars. Recently, a new branch emerged in the phenotyping field, namely, high-throughput phenotyping (HTP). Specifically, HTP exploits modern data sampling techniques to gather a high amount of data that can be used to improve the effectiveness of phenotyping. Hence, HTP combines the knowledge derived from the phenotyping domain with computer science, engineering, and data analysis techniques. In this scenario, machine learning (ML) and deep learning (DL) algorithms have been successfully integrated with noninvasive imaging techniques, playing a key role in automation, standardization, and quantitative data analysis. This study aims to systematically review two main areas of interest for HTP: hardware and software. For each of these areas, two influential factors were identified: for hardware, platforms and sensing equipment were analyzed; for software, the focus was on algorithms and new trends. The study was conducted following the PRISMA protocol, which allowed the refinement of the research on a wide selection of papers by extracting a meaningful dataset of 32 articles of interest. The analysis highlighted the diffusion of ground platforms, which were used in about 47% of reviewed methods, and RGB sensors, mainly due to their competitive costs, high compatibility, and versatility. Furthermore, DL-based algorithms accounted for the larger share (about 69%) of reviewed approaches, mainly due to their effectiveness and the focus posed by the scientific community over the last few years. Future research will focus on improving DL models to better handle hardware-generated data. The final aim is to create integrated, user-friendly, and scalable tools that can be directly deployed and used on the field to improve the overall crop yield. [ABSTRACT FROM AUTHOR]

Reduction in nozzle drift can be considered as one of the main factors in preventing the risk of environmental pollution because of using pesticides. For this aim, three types of air-assisted nozzles, namely, air-liquid-air(ALA), liquidair(LA), liquid-air-liquid(LAL), and at four levels of air-assisted velocity (0, 2, 4, and 7.5 m/s-1)were used; They were also examined at four levels of wind velocity (0, 2, 3, and 4 m/s-1). A spectrophotometry device, MATLAB, SAS 9.1, and IBM SPSS statistical software were used for measurement. The results showed that the effects of the nozzle type, air-assisted velocity, and wind velocity on the drift, deposition, unified spraying, volume median diameters of 50% and 90%, and spraying quality indicators were significant (a=0.01). Also in this study, the third nozzle (LAL) with wind and air-assisted velocity of 4 m/s-1 as the best nozzle was obtained in order to the maximum of the deposition, the minimum of the drift, and the highest spraying uniformity. Therefore, it is recommended to use the third nozzle (air-liquid-air) for better deposition and less drift to protect the solution against different environmental conditions. [ABSTRACT FROM AUTHOR]

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]