The deployment of intelligent surveillance systems to monitor tomato plant growth poses substantial challenges due to the dynamic nature of disease patterns and the complexity of environmental cond...

Greenhouse cultivation consumes large volumes of freshwater, and excessive irrigation induces environmental problems, such as nutrient leaching and secondary salinization. Pyrochar (biochar from high-temperature pyrolysis) is an effective soil amendment, and researches have shown that pyrochar application could maintain soil nutrient and enhance carbon sequestration. In addition to pyrochar from pyrolysis, hydrochar from hydrothermic carbonization is considered as a new type of biochar and has the advantages of low energy consumption and a high productive rate. However, the effect of these two biochars on water evaporation in clayey soils under a greenhouse system has seldom been studied. The relationship between water evaporation and biochar properties is still unknown. Thus, in the present study, water evaporation under pyrochar and hydrochar application were recorded. Results showed that both pyrochar and hydrochar application could inhibit water evaporation in clayey soil under greenhouse cultivation. Pyrochar showed a better inhibition effect compared with hydrochar. Correlation analysis indicated that the water evaporation rate was significantly positively correlated with bulk density of biochar (p < 0.05). Overall, application of pyrochar or hydrochar could both reduce soil bulk density and inhibit soil evaporation, and be available for greenhouse cultivation. However, the inhibition effect depends on the properties of the biochar.

Light-emitting diodes (LEDs) have been widely used as light sources for plant production in plant factories with artificial lighting (PFALs), and light spectrum and light amount have great impacts on plant growth and development. With the expansion of the product list of PFALs, tomato production in PFALs has received attention, but studies on fruit quality influenced by artificial light are lacking. In this study, precisely modulated LED light sources based on white light combined with additional red, blue, and green lights were used to investigate the effects of light spectrum and daily light integral (DLI) on the main quality indicators and flavor substances of “Micro-Tom” tomato fruits. The highest sugar–acid ratio was obtained under the white light with addition of red light with high DLI and blue light with low DLI. The contents of β-carotene, lycopene, and lutein were significantly increased by higher DLI conditions except for under the blue light treatment, and the cross-interactions between the light spectrum and DLI were observed. The accumulation of the main flavor substances in tomato fruits was decreased by addition of green light with a high DLI and red light with a low DLI; notably, the percentage of 2-isobutylthiazole, which is associated with fresh tomato aroma, was decreased by green light. This study provides insights for improving tomato fruit quality and flavor by regulating light conditions in PFALs.

Remote sensing technology can be used to monitor changes in crop planting areas to guide agricultural production management and help achieve regional carbon neutrality. Agricultural UAV remote sensing technology is efficient, accurate, and flexible, which can quickly collect and transmit high-resolution data in real time to help precision agriculture management. It is widely used in crop monitoring, yield prediction, and irrigation management. However, the application of remote sensing technology faces challenges such as a high imbalance of land cover types, scarcity of labeled samples, and complex and changeable coverage types of long-term remote sensing images, which have brought great limitations to the monitoring of cultivated land cover changes. In order to solve the abovementioned problems, this paper proposed a multi-scale fusion network (MSFNet) model based on multi-scale input and feature fusion based on cultivated land time series images, and further combined MSFNet and Model Diagnostic Meta Learning (MAML) methods, using particle swarm optimization (PSO) to optimize the parameters of the neural network. The proposed method is applied to remote sensing of crops and tomatoes. The experimental results showed that the average accuracy, F1-score, and average IoU of the MSFNet model optimized by PSO + MAML (PSML) were 94.902%, 91.901%, and 90.557%, respectively. Compared with other schemes such as U-Net, PSPNet, and DeepLabv3+, this method has a better effect in solving the problem of complex ground objects and the scarcity of remote sensing image samples and provides technical support for the application of subsequent agricultural UAV remote sensing technology. The study found that the change in different crop planting areas was closely related to different climatic conditions and regional policies, which helps to guide the management of cultivated land use and provides technical support for the realization of regional carbon neutrality.

With the advancement of computer vision technology, vision-based target perception has emerged as a predominant approach for harvesting robots to identify and locate fruits. However, little attention has been paid to the fact that fruits may be obscured by stems or other objects. In order to improve the vision detection ability of fruit harvesting robot, a fruit target selection and location approach considering obstacle perception was proposed. To enrich the dataset for tomato harvesting, synthetic data were generated by rendering a 3D simulated model of the tomato greenhouse environment, and automatically producing corresponding pixel-level semantic segmentation labels. An attention-based spatial-relationship feature extraction module (SFM) with lower computational complexity was designed to enhance the ability of semantic segmentation network DeepLab v3+ in accurately segmenting linear-structured obstructions such as stems and wires. An adaptive K-means clustering method was developed to distinguish individual instances of fruits. Furthermore, a barrier-free fruit selection algorithm that integrates information of obstacles and fruit instances was proposed to identify the closest and largest non-occluded fruit as the optimal picking target. The improved semantic segmentation network exhibited enhanced performance, achieving an accuracy of 96.75%. Notably, the Intersection-over-Union (IoU) of wire and stem classes was improved by 5.0% and 2.3%, respectively. Our target selection method demonstrated accurate identification of obstacle types (96.15%) and effectively excluding fruits obstructed by strongly resistant objects (86.67%). Compared to the fruit detection method without visual obstacle avoidance (Yolo v5), our approach exhibited an 18.9% increase in selection precision and a 1.3% reduction in location error. The improved semantic segmentation algorithm significantly increased the segmentation accuracy of linear-structured obstacles, and the obstacle perception algorithm effectively avoided occluded fruits. The proposed method demonstrated an appreciable ability in precisely selecting and locating barrier-free fruits within non-structural environments, especially avoiding fruits obscured by stems or wires. This approach provides a more reliable and practical solution for fruit selection and localization for harvesting robots, while also being applicable to other fruits and vegetables such as sweet peppers and kiwis.

Phenotypic traits, such as plant height, internode length, and node count, are essential indicators of the growth status of tomato plants, carrying significant implications for research on genetic breeding and cultivation management. Deep learning algorithms such as object detection and segmentation have been widely utilized to extract plant phenotypic parameters. However, segmentation-based methods are labor-intensive due to their requirement for extensive annotation during training, while object detection approaches exhibit limitations in capturing intricate structural features. To achieve real-time, efficient, and precise extraction of phenotypic traits of seedling tomatoes, a novel plant phenotyping approach based on 2D pose estimation was proposed. We enhanced a novel heatmap-free method, YOLOv8s-pose, by integrating the Convolutional Block Attention Module (CBAM) and Content-Aware ReAssembly of FEatures (CARAFE), to develop an improved YOLOv8s-pose (IYOLOv8s-pose) model, which efficiently focuses on salient image features with minimal parameter overhead while achieving a superior recognition performance in complex backgrounds. IYOLOv8s-pose manifested a considerable enhancement in detecting bending points and stem nodes. Particularly for internode detection, IYOLOv8s-pose attained a Precision of 99.8%, exhibiting a significant improvement over RTMPose-s, YOLOv5s6-pose, YOLOv7s-pose, and YOLOv8s-pose by 2.9%, 5.4%, 3.5%, and 5.4%, respectively. Regarding plant height estimation, IYOLOv8s-pose achieved an RMSE of 0.48 cm and an rRMSE of 2%, and manifested a 65.1%, 68.1%, 65.6%, and 51.1% reduction in the rRMSE compared to RTMPose-s, YOLOv5s6-pose, YOLOv7s-pose, and YOLOv8s-pose, respectively. When confronted with the more intricate extraction of internode length, IYOLOv8s-pose also exhibited a 15.5%, 23.9%, 27.2%, and 12.5% reduction in the rRMSE compared to RTMPose-s, YOLOv5s6-pose, YOLOv7s-pose, and YOLOv8s-pose. IYOLOv8s-pose achieves high precision while simultaneously enhancing efficiency and convenience, rendering it particularly well suited for extracting phenotypic parameters of tomato plants grown naturally within greenhouse environments. This innovative approach provides a new means for the rapid, intelligent, and real-time acquisition of plant phenotypic parameters in complex backgrounds.