Automated Horticultural Scouting with Robotic UAVs
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Solution Overview
Problem
Large-scale horticultural operations face challenges in early detection of conditions such as diseases, pest infestations, and malnutrition due to resource constraints, making it difficult to implement effective remediation before conditions become catastrophic.
Innovation Solution
The implementation of a system that uses sensors distributed throughout the grow operation to collect data, generate attribute data values, and identify regions potentially affected by specific conditions, with robotic devices like UAVs assigned to confirm or disprove the existence of these conditions using machine-learning based pattern recognition techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a master grower manually collects and monitors information about plants throughout a large grow operation, then early detection of problematic conditions can be achieved, but sufficient resources cannot be dedicated to monitoring large-scale operations
Solution Approach 1:
The patent replaces manual mechanical inspection by master growers with automated electronic sensing systems. Sensors distributed throughout the grow operation continuously monitor plant conditions, environmental parameters, and detect problematic conditions automatically, eliminating the need for human physical inspection while enabling large-scale monitoring.
Solution Approach 2:
The patent introduces robotic devices as intermediaries between the sensors and the master grower. These robotic scouts autonomously navigate through the grow operation, collect data from sensors, analyze plant conditions using machine learning algorithms, and report findings, serving as an automated intermediary that bridges the gap between raw sensor data and human decision-making.
2Reliability
If more resources are allocated to manual monitoring and inspection of plants, then early detection of conditions improves, but resource constraints prevent adequate monitoring of large operations
Solution Approach 1:
The system enables self-service monitoring where the grow operation monitors itself through distributed sensors and autonomous robotic devices. The sensors automatically detect conditions, the robots autonomously navigate and collect data, and machine learning algorithms automatically analyze patterns to identify problematic conditions, eliminating the need for human resource allocation to monitoring tasks.
Solution Approach 2:
The patent changes the fundamental parameters of the monitoring system by transitioning from human-based inspection to automated electronic sensing and robotic analysis. This parameter change enables continuous, multi-parameter monitoring of plant health, environmental conditions, and potential problems without the resource constraints that limit manual monitoring capacity.
3Area of stationary object
If manual inspection methods are used to detect conditions in large grow operations, then comprehensive coverage can be achieved, but the complexity and time required for monitoring increases significantly
Solution Approach 1:
The patent introduces dynamic, mobile robotic devices that can autonomously navigate and move throughout the grow operation, rather than relying on static monitoring points. These robotic scouts dynamically adapt their paths based on sensor data and identified risk areas, enabling comprehensive coverage of large areas while minimizing the time required through intelligent, adaptive navigation.
Solution Approach 2:
The system performs preliminary action by using distributed sensors to continuously monitor and pre-identify potential problematic conditions before they become apparent through manual inspection. The machine learning algorithms analyze sensor data in real-time to detect early signs of issues, allowing the robotic devices to prioritize and focus their inspection on high-risk areas, thereby reducing overall monitoring time while maintaining comprehensive coverage.
Data Source
AI summary
Disclosed are techniques for providing automated scouting of a grow operation in order to facilitate early detection and treatment of various conditions. Such techniques may comprise receiving sensor data from a number of sensors within a grow operation, determining current data values for a number of attributes to be associated with locations within the grow operation, identifying one or more regions within the grow operation potentially associated with a condition, providing instructions to at least one robotic device to perform a scouting operation of the one or more regions, and determining, based on information collected during the scouting operation, whether the condition is present in the one or more regions.