Master-Slave Agricultural System with LoRa Geolocation Correction
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Solution Overview
Problem
Existing agricultural machinery is complex, incompatible across different manufacturers, and prone to errors due to over-engineering, leading to high power consumption, inaccurate row identification, and misidentification of crops and weeds in varying environmental conditions, resulting in inefficient chemical application and wastage.
Innovation Solution
A master-slave system with a central image-capture device and slave image-capture devices, utilizing GPS data and location correction from external devices via LoRa for precise geolocation, enabling centimeter-level accuracy and adaptive operation without prior row demarcation, and employing AI for distinguishing between crops and weeds despite environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional agricultural machinery systems are used, then basic agricultural operations can be performed, but the systems are complex, incompatible across manufacturers, and prone to errors due to over-engineering
Solution Approach 1:
The patent implements a universal master-slave architecture where the master control unit can communicate with and control slave units from different manufacturers through standardized protocols. This allows a single master system to operate with multiple types of agricultural implements, eliminating manufacturer-specific incompatibilities while maintaining manageable system complexity through modular design.
2Measurement precision
If existing camera-based systems are used for chemical application, then automated spraying can be performed, but accuracy is severely impacted in varying environmental conditions leading to misidentification of crops and weeds
Solution Approach 1:
The system dynamically adjusts imaging parameters such as exposure time, gain, and filtering based on real-time environmental conditions including lighting, weather, and crop stage. This adaptive parameter adjustment maintains high identification accuracy across varying environmental conditions by optimizing the imaging settings for current conditions.
Solution Approach 2:
The patent introduces an intermediate processing layer between image capture and chemical application that uses multiple imaging modalities and environmental sensors to verify crop-weed differentiation. This intermediary verification step reduces misidentification errors caused by environmental factors before triggering chemical application.
3Measurement precision
If GPS-based location systems are used, then basic positioning can be achieved, but the error range is 1-10 meters which is insufficient for precise agricultural operations
Solution Approach 1:
The positioning system is segmented into multiple levels: coarse GPS positioning for general location, followed by incremental refinement using wheel encoders and inertial sensors for higher precision. This segmented approach achieves centimeter-level accuracy without continuously operating high-power correction systems, optimizing the balance between precision and power consumption.
4Adaptability or versatility
If row-based processing systems are used, then structured field operations can be performed, but the systems fail when proper rows are not demarcated in the agricultural field
Solution Approach 1:
The system dynamically adapts its processing mode based on field conditions. When rows are present, it uses row-based processing for efficiency; when rows are absent or indistinct, it automatically transitions to feature-based or grid-based processing. This dynamic adaptability ensures reliable operation across all field types without system failure.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system achieves precise and efficient chemical application with reduced power consumption, accurate identification of crops and weeds, and adaptive operation in real-world conditions, minimizing wastage and ensuring correct targeting of agricultural inputs.
Implementation Method 1
obtain location correction data from an external device installed at a fixed location within a communication range of the master control device
Data Source
AI summary
A system mounted in a vehicle for agricultural applications includes a master apparatus and one or more slave apparatus. The master apparatus includes a central image-capture device and a master control device that is configured to acquire first geospatial location data including a first precision value and obtain location correction data from an external device. The master control device further generates a second geospatial location data including a second precision value by applying the location correction data to the first geospatial location data. The master control device further communicates the generated second geospatial location data to the slave control device. Thereafter, each slave control device is configured to determine one or more time slots in advance to automatically perform a determined action when the vehicle is in motion.