Summary
This paper presents an Internet of Things-enabled hydroponic strawberry production system that automates environmental control and crop monitoring whilst enabling remote live analysis. The system uses computer vision and machine learning to classify plant growth stages, addressing Sri Lanka's geographical constraints on strawberry cultivation by enabling production in non-traditional climates. The authors validated their growth-stage classification algorithm on data from an operational strawberry farm and propose the system as a pathway to expand strawberry farming beyond currently limited growing regions.
Regional applicability
Whilst this system was developed for Sri Lankan conditions, the autonomous hydroponic and remote-monitoring approach has potential application in UK horticulture, particularly for extending the season for heat-demanding crops or enabling production in controlled environments where outdoor conditions are marginal. Transferability would depend on adaptation to UK energy costs, refrigeration availability, and market economics for strawberry production.
Key measures
Growth stage classification accuracy via machine learning image analysis; environmental factor monitoring and control parameters; system scalability assessment
Outcomes reported
The study designed and tested an automated hydroponic strawberry cultivation system with remote monitoring capabilities using IoT, image processing, and machine learning to control environmental factors and track plant growth stages. The system's ability to identify strawberry growth stages through image analysis was verified using farm data.
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