Cloud-Based Machine Learning for Adaptive Horticulture Growing Regimens
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Solution Overview
Problem
Existing horticulture systems face challenges in optimizing plant growth due to inefficiencies in data collection, self-learning mechanisms, and the time-consuming process of model training and retraining, leading to divergent grow results from desired objectives.
Innovation Solution
A horticulture growing system utilizing cloud-based machine learning and deep learning to optimize growing regimens through a dynamic and local system model, incorporating self-cleaning data techniques, gamification for user engagement, and real-time re-optimization loops to improve data accuracy and model adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data collection and model training methods are used in horticulture systems, then manual data cleaning and model retraining are required, but this process is time-consuming and leads to divergent grow results from desired objectives
Solution Approach 1:
The system implements self-learning mechanisms where the machine learning model automatically refines growing regimens by continuously processing sensor data and outcome information without requiring manual intervention for data cleaning or model retraining, enabling the system to serve itself in optimizing plant growth parameters
Solution Approach 2:
The system incorporates feedback loops where grow results are continuously monitored and fed back into the machine learning model to adjust and optimize growing regimens in real-time, ensuring that the model adapts to actual plant responses and maintains alignment with desired grow objectives
2Productivity
If cloud-based machine learning is implemented to optimize growing regimens, then model adaptation speed improves, but system complexity increases
Solution Approach 1:
The system uses a cloud-based machine learning platform as an intermediary that handles complex model training and optimization computations remotely, allowing the local horticulture system to benefit from advanced AI capabilities without bearing the full complexity burden locally
Solution Approach 2:
The system architecture is segmented into modular components including sensor data collection modules, cloud-based machine learning processing modules, and actuation modules for implementing growing regimens, allowing each component to be developed and maintained independently while working together to optimize plant growth
Data Source
AI summary
A horticulture growing system where a growing regimen is prescribed to achieve desired growing results. The system has self-learning mechanisms where the prescribed growing regimens are continually optimized to achieve the desired results through machine learning and deep learning. The system uses both a cloud based dynamic system model for growing and a local grow model. Various techniques are utilized to improve data collection and labeling. The results (the system's ability to accurately create growing regimens which produce the desired grow objectives) are improved using the dynamic system model and the local grow model. The models are trained and adjusted using datasets from multiple growing operations to increase the efficacy of the self-learning mechanisms. This system may also include a mechanism for in-harvest re-optimization to improve grow results in real-time.