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Robots in tomato greenhouses need to perceive the plant and plant parts accurately to automate monitoring, harvesting, and de-leafing tasks. Existing perception systems struggle with the high levels of occlusion in plants and often result in poor perception accuracy. One reason for this is because they use fixed cameras or predefined camera movements. Next-best-view (NBV) planning presents an alternate approach, in which the camera viewpoints are reasoned and strategically planned such that the perception accuracy is improved. However, existing NBV-planning algorithms are agnostic to the task-at-hand and give equal importance to all the plant parts. This strategy is inefficient for greenhouse tasks that require targeted perception of specific plant parts, such as the perception of leaf nodes for de-leafing. To improve targeted perception in complex greenhouse environments, NBV planning algorithms need an attention mechanism to focus on the task-relevant plant parts. In this paper, the role of attention in improving targeted perception using an attention-driven NBV planning strategy was investigated. Through simulation experiments using plants with high levels of occlusion and structural complexity, it was shown that focusing attention on task-relevant plant parts can significantly improve the speed and accuracy of 3D reconstruction. Further, with real-world experiments, it was shown that these benefits extend to complex greenhouse conditions with natural variation and occlusion, natural illumination, sensor noise, and uncertainty in camera poses. The results clearly indicate that using attention-driven NBV planning in greenhouses can significantly improve the efficiency of perception and enhance the performance of robotic systems in greenhouse crop production. • Method to plan the next-best viewpoint to reconstruct the nodes of tomato plants. • Adding attention mechanism improves the efficiency of next-best-view planning. • Performed systematic evaluations with both simulation and real-world tomato plants. • Effectively handles occlusion in complex greenhouse environments. [ABSTRACT FROM AUTHOR]

The agro-food industry is turning to robots to address the challenge of labour shortage. However, agro-food environments pose difficulties for robots due to high variation and occlusions. In the presence of these challenges, accurate world models, with information about object location, shape, and properties, are crucial for robots to perform tasks accurately. Building such models is challenging due to the complex and unique nature of agro-food environments, and errors in the model can lead to task execution issues. In this paper, MinkSORT, a novel method for generating tracking features using a 3D sparse convolutional network in a deepSORT-like approach, is proposed to improve the accuracy of world models in agro-food environments. MinkSORT was evaluated using real-world data collected in a tomato greenhouse, where it significantly improved the performance of a baseline model that tracks tomato positions in 3D using a Kalman filter and Mahalanobis distance. MinkSORT improved the HOTA from 42.8% to 44.77%, the association accuracy from 32.55% to 35.55%, and the MOTA from 57.63% to 58.81%. Different contrastive loss functions for training MinkSORT were also evaluated, and it was demonstrated that it leads to improved performance in terms of three separate precision and recall detection outcomes. The proposed method improves world model accuracy, enabling robots to perform tasks such as harvesting and plant maintenance with greater efficiency and accuracy, which is essential for meeting the growing demand for food in a sustainable manner. • MinkSORT: A novel method of using 3D sparse convolutions for multi-object tracking. • A novel implementation of the concept of DeepSORT for 3D data like point clouds. • Real world experiments in a challenging tomato greenhouse environment. • An evaluation of MinkSORT showing a tracking accuracy increase up to 7.72 points. [ABSTRACT FROM AUTHOR]

High-tech greenhouses are characterised by high yields and high energy consumption. Current trends towards expanded use of supplemental lighting further increase the intensity of crop production and energy use, and it is expected that the availability of light-emitting diodes (LEDs) will accelerate this trend. At the same time, an increase in greenhouse lighting reduces the heating energy needed from the heating system. This study presents a novel concept for greenhouses, where both lighting and heating are derived exclusively from lamps. Such greenhouses can be highly efficient, as light is used both for crop growth and for heating. If the electric grid is based on renewable sources, such greenhouses can also be carbon-neutral and fossil-free. By using model simulations for a tomato greenhouse in the Netherlands, it was found that such greenhouses could be realised by employing a heat storage system with a heat storage capacity of 2 MJ m−2 and LEDs with a power capacity of 150 W m−2 and a photosynthetic photon flux density (PPFD) of 450 μmol m−2 s−1. The greenhouse heated by light was predicted to have 44% higher yields and 60% higher energy inputs than a reference greenhouse, equipped with a boiler and LEDs with a PPFD of 200 μmol m−2 s−1. This result was part of a general trade-off that was found between yield and energy efficiency. This exploration helped identify avenues for further improvement of the energy efficiency of greenhouses heated by lamps, highlighting their promise as a potential new direction in greenhouse intensification. • A novel approach for greenhouse climate control, using only lamps and no boiler. • Model simulations uncover the attributes required to make such systems feasible. • Increasing lamp intensity alone increases yields but reduces energy efficiency. • Avenues for improving the energy efficiency of heating by light are proposed. [ABSTRACT FROM AUTHOR]

Currently the Dutch greenhouse horticultural sector has a high energy demand. The present use of weather forecasts can exacerbate this high energy consumption by contributing to suboptimal prediction and trading of the greenhouse's power demand. This study investigates the role of weather forecast errors on energy prediction power and trading uncertainty in greenhouse horticulture. This was done using an uncertainty analysis and computer model of a tomato producing Venlo style greenhouse in Bleiswijk, The Netherlands. This greenhouse model was used to predict the greenhouse's gas and electrical power demand. The study concluded that errors in the weather forecast of outdoor radiation, temperature and wind speed caused an overestimation of greenhouse energy demand. A sensitivity analysis showed that the radiation forecast error had the greatest impact on predicted greenhouse power demand errors with a mean relative error of 6.1 %. Predicted gas demand errors were most dependent on the outside wind speed forecast mean relative error (18.0 %) and temperature forecast error (17.2 %). A power trading uncertainty analysis was done to investigate the impact of predicted energy demand errors on the cost of buying power on the Dutch imbalance and Amsterdam Power Exchange day-ahead market. This cost analysis found that the volume of initial power trading was greater than corrective trading. Additionally, the higher volatility in short term power prices resulted in higher corrective power costs per unit of power than if the power demand had been initially predicted with more accuracy. • The study shows that weather forecasts create energy demand uncertainty. • A data-based sensitivity analysis was done for input data weather variables. • The greenhouse power costs are calculated using imbalance and day-ahead markets. • As the weather forecasts lengthen, the energy prediction error and power costs increase. • The radiation forecast has the greatest impact on the predicted power error. [ABSTRACT FROM AUTHOR]

Measurement of volatile organic compounds (VOCs) emitted by plants allows us to monitor plant health status without touching the plant. To bring this technique a step further towards a practical plant diagnosis technique for greenhouse crop production, we have defined a numerical index named “Emission index” to evaluate VOC emissions from tomato plants isolated from ambient greenhouse air using an open-bottom chamber. The emission index of a VOC is a ratio of the concentration of the VOC in the air inside the chamber to that in the ambient greenhouse air and the index should be larger than one if the VOC is emitted by the plants in the chamber. Measurement of the emission indices of representative tomato VOCs proved that non-stressed tomato plants in a greenhouse emit n-hexanal, 2-carene, β-phellandrene, α-copaene, β-caryophyllene and (3E,7E)-4,8,12-trimethyl-1,3,7,11-tridecatetraene (TMTT), do not emit (Z)-3-hexenol, and might absorb and/or adsorb n-hexanol. Another experiment proved that a routine crop maintenance operation, i.e. removal of old leaves from the lower part of plants and removal of side shoots, enhances the emissions of n-hexanal, (E)-2-hexenal, (Z)-3-hexenol, but does not enhance the emissions of α-copaene, methyl salicylate (MeSA) and TMTT. The results suggest that the measurement of emission index with the open-bottom chamber is a useful technique for monitoring VOC emissions by plants in a greenhouse under practical conditions. [ABSTRACT FROM AUTHOR]

Mobile rose systems are designed to increase labour efficiency. However, many questions remain on best design and settings of operational parameters for best performance. The ultimate goal of this research is an assessment of re-designed horticultural crop production systems and work scenarios on labour and machine performance before implementation. To attain this goal, a queueing network model, GWorkS 1 [1] GWorkS is an acronym for Greenhouse Work Simulation, and is a discrete event simulation model on the crop handling processes and logistics inside a greenhouse production site. -rose, is presented for simulation of labour processes in a greenhouse with a mobile rose cultivation system. The objective for modelling is to quantify effects of production system changes by means of a flexible and generic model approach. A state-of-the-art mobile rose production system was used to validate and test the GWorkS-rose model. Data from the labour registration system and from video recordings were used for validation. System performance was simulated and compared to the performance measured in the real situation. Results of a single day validation show that the model estimates harvest labour time with an accuracy of 94%. For a one month validation an accuracy of 92% and RRMSE of 18% resulted. The value of RRMSE was caused by missing data on the number of workers at the loop and on the actual gutter speed level. The model can determine best system settings as is illustrated for operator and mobile gutter speed settings at given rose yield levels. It is concluded that the model can be used for studies on design and management of this kind of production systems. [Copyright &y& Elsevier]

Greenhouse models are important tools for the analysis and design of greenhouse systems and for offering decision support to growers. While many models are available, relatively few include the influence of supplementary lighting on the greenhouse climate and crop. This study presents GreenLight, a model for greenhouses with supplemental lighting. GreenLight extends state of the art models by describing the qualitative difference between the common lighting system of high-pressure sodium (HPS) lamps, and the newest technology for horticultural lighting - the light-emitting diodes (LEDs). LEDs differ from HPS lamps in that they operate at lower temperatures, emit mostly convective heat and relatively little radiative heat, and can be more efficient in converting electricity to photosynthetically active radiation (PAR). These differences can have major implications on the greenhouse climate and operation, and on the amount of heat that must be supplied from the greenhouse heating system. Model predictions have been evaluated against data collected in greenhouse compartments equipped with HPS and LED lamps. The model predicted the greenhouse's heating needs with an error of 8–51 W m−2, representing 1–12% of the measured values; the RMSE for indoor temperature was 1.74–2.04 °C; and the RMSE for relative humidity was 5.52–8.5%. The model is freely available as open source MATLAB software at https://github.com/davkat1/GreenLight. It is hoped that it may be further evaluated and used by researchers worldwide to analyse the influence of the most recent lighting technologies on greenhouse climate control. Image 1 • Model for lighting in greenhouses, including HPS and LED lamps, was developed. • Model was evaluated using data from a greenhouse with HPS and LED top-lights. • Model evaluates energy requirements of the greenhouse with an accuracy of 1–12%. • Model predicts indoor temperature and humidity with an RRMSE of 6–11%. • Model is publicly available in free open source format. [ABSTRACT FROM AUTHOR]

To reach a fruit in an obstacle-dense crop environment, robotic fruit harvesting requires a collision-free motion of the manipulator and end-effector. A novel two-part analysis was conducted of a sweet-pepper harvesting robot based on data of fruit (N = 158) and stem locations collected from a greenhouse. The first part of the analysis compared two methods of selecting the azimuth angle of the end-effector. The new ‘constrained-azimuth’ method avoided risky paths and achieved a motion planning success similar to the ‘full-azimuth’ method. In the second part, a sensitivity analysis was conducted for five parameters specifying the crop (stem spacing and fruit location), the robot (end-effector dimensions and robot position) and the planning algorithm, to evaluate their effect on successfully finding a collision-free goal configuration and path. Reducing end-effector dimensions and widening stem spacing are promising research directions because they significantly improved goal configuration success, from 63% to 84%. However, the fruit location at the stem is the strongest influencing parameter and therefore provides an incentive to train or breed plants that develop more fruit at the front side of the plant stem. The two analyses may serve as useful tools to study motion planning problems in a dense obstacle environment. [ABSTRACT FROM AUTHOR]