ArticleSensors (Basel, Switzerland)2020
Cherry Tomato Production in Intelligent Greenhouses-Sensors and AI for Control of Climate, Irrigation, Crop Yield, and Quality.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- Enhancing autonomous agriculture control systems in greenhouses for sustainable resource usage using deep learning techniques.PloS one · 2026Article
- Chlorophyll fluorescence-based control of greenhouse supplemental lighting improves energy use efficiency in lettuce.Frontiers in plant science · 2026Article
- Let's Get Real: Are Wearable Plant Sensors Ready for Crop Monitoring?ACS sensors · 2025Review
- Explainable AI-driven interpretation of environmental drivers of tomato fruit expansion in smart greenhouses using IoT sensing.Scientific reports · 2025Article
- Optimization of a multi-environmental detection model for tomato growth point buds based on multi-strategy improved YOLOv8.Scientific reports · 2025Article
- Autonomous Greenhouse Cultivation of Dwarf Tomato: Performance Evaluation of Intelligent Algorithms for Multiple-Sensor Feedback.Sensors (Basel, Switzerland) · 2025Article
- The influence of resource use on yield versus sale price trade-off in Australian vineyards.PloS one · 2025Article
- Estimation of the amount of pear pollen based on flowering stage detection using deep learning.Scientific reports · 2024Article
- Digital Twins in Agriculture: Orchestration and Applications.Journal of agricultural and food chemistry · 2024Review
- Advancing horizons in vegetable cultivation: a journey from ageold practices to high-tech greenhouse cultivation-a review.Frontiers in plant science · 2024Review
- Lettuce Production in Intelligent Greenhouses-3D Imaging and Computer Vision for Plant Spacing Decisions.Sensors (Basel, Switzerland) · 2023Article
- Shape classification technology of pollinated tomato flowers for robotic implementation.Scientific reports · 2023Article
- Plant science in the age of simulation intelligence.Frontiers in plant science · 2023Article
- Review
- Leveraging plant physiological dynamics using physical reservoir computing.Scientific reports · 2022Article
- Grid Search for Lowest Root Mean Squared Error in Predicting Optimal Sensor Location in Protected Cultivation Systems.Frontiers in plant science · 2022Article
- Article
- Response of Plant Rhizosphere Microenvironment to Water Management in Soil- and Substrate-Based Controlled Environment Agriculture (CEA) Systems: A Review.Frontiers in plant science · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Greenhouses and indoor farming systems play an important role in providing fresh and nutritious food for the growing global population. Farms are becoming larger and greenhouse growers need to make complex decisions to maximize production and minimize resource use while meeting market requirements. However, highly skilled labor is increasingly lacking in the greenhouse sector. Moreover, extreme events such as the COVID-19 pandemic, can make farms temporarily less accessible. This highlights the need for more autonomous and remote-control strategies for greenhouse production. This paper describes and analyzes the results of the second "Autonomous Greenhouse Challenge". In this challenge, an experiment was conducted in six high-tech greenhouse compartments during a period of six months of cherry tomato growing. The primary goal of the greenhouse operation was to maximize net profit, by controlling the greenhouse climate and crop with AI techniques. Five international teams with backgrounds in AI and horticulture were challenged in a competition to operate their own compartment remotely. They developed intelligent algorithms and use sensor data to determine climate setpoints and crop management strategy. All AI supported teams outperformed a human-operated greenhouse that served as reference. From the results obtained by the teams and from the analysis of the different climate-crop strategies, it was possible to detect challenges and opportunities for the future implementation of remote-control systems in greenhouse production.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.