LiDAR-Guided Orchard Spraying for Tree-Specific Flow Control
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Solution Overview
Problem
Current agricultural spraying technologies face challenges in determining optimal spraying times, rates, and tree selection based on conditions and health, leading to inefficiencies in fruit production and resource usage in tree groves.
Innovation Solution
The integration of LiDAR sensors, cameras, and GPS modules with AI to collect and process data for precise control of sprayer operations, including classification of spray zones, tree health assessment, and yield prediction, enabling adaptive spraying based on real-time data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional spraying methods are used in tree groves, then spraying operations can be performed, but resource usage is inefficient and environmental impact is increased
Solution Approach 1:
The patent applies local quality by controlling spray application at different locations within the tree grove based on individual tree characteristics. The system identifies specific trees or zones that require spraying and applies liquid only to those locations, rather than uniformly spraying the entire grove. This is achieved through spatial mapping and zone-based control of spray valves, ensuring that spraying is localized to areas where it is actually needed, thereby improving efficiency and reducing resource waste.
Solution Approach 2:
The patent implements preliminary action by scanning and mapping the tree grove before spraying operations begin. The LiDAR system creates a three-dimensional map of the grove, identifying tree locations, heights, and densities in advance. This preliminary spatial information is used to pre-determine which zones require spraying and to plan the spray application strategy, allowing the system to optimize resource allocation and avoid unnecessary spraying before the actual spraying operation commences.
2Measurement precision
If traditional spraying methods are used in tree groves, then spraying operations can be performed, but determination of optimal spraying times, rates, and tree selection is difficult
Solution Approach 1:
The patent replaces mechanical spray control systems with an intelligent system based on LiDAR scanning, spatial mapping, and automated zone classification. Instead of relying on manual operation or simple mechanical timers, the system uses laser-based distance measurement and three-dimensional modeling to precisely determine which trees or zones require spraying. The LiDAR data is processed to create detailed spatial maps that automatically identify spray targets, eliminating the need for complex manual decision-making while achieving high measurement precision.
Solution Approach 2:
The patent introduces an intermediary spatial mapping system that bridges the gap between the physical tree grove and the spray application process. The LiDAR system acts as an intermediary by creating a digital three-dimensional representation of the grove, which serves as an intermediate layer for analysis and decision-making. This spatial map intermediary allows the system to precisely identify spray zones, calculate appropriate spray rates, and determine optimal timing without requiring direct complex interactions between the sprayer and individual trees.
3Measurement precision
If LiDAR scanning is performed to map the area of interest, then spatial data for precise spray control is obtained, but processing time and computational requirements increase
Solution Approach 1:
The patent applies the extraction principle by selectively processing only the relevant portions of LiDAR data that are necessary for spray control decisions. Instead of analyzing the entire point cloud dataset in full detail, the system extracts key spatial features such as tree locations, canopy heights, and zone boundaries that are critical for determining spray requirements. This selective extraction of essential spatial information reduces computational burden and processing time while maintaining the measurement precision needed for accurate spray application.
Solution Approach 2:
The patent implements segmentation by dividing the LiDAR point cloud data into manageable spatial zones or regions based on tree locations and grove layout. The large dataset is segmented into smaller, discrete areas that can be processed independently and in parallel. Each zone is analyzed separately to determine spray requirements, which reduces the overall computational complexity and processing time compared to analyzing the entire grove as a single unit, while still maintaining comprehensive spatial coverage and accuracy.
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
This approach enhances the efficiency and effectiveness of agricultural spraying by optimizing resource use, improving fruit production, and reducing environmental impact through data-driven decision-making.
Implementation Method 1
a Light Detection and Ranging (LiDAR) sensor to collect three dimensional spatial data
Data Source
AI summary
Embodiments provide methods, apparatus, systems, computing devices, computing entities, assemblies, and/or the like for providing smart agricultural spraying. Various embodiments of the disclosure involve the use of a LiDAR sensor to collect three-dimensional spatial data, one or more cameras to collect images, and a GPS module to collect position and speed measurements of a sprayer as the sprayer travels through an area of interest such as a tree grove. Accordingly, in particular embodiments, a map of the area of interest that may be acquired through UAV imagery, LiDAR measurements, camera images, GPS location and speed measurements, and Artificial Intelligence are used to control the flow of liquid being applied by the sprayer to objects of interest (e.g., trees) as the sprayer travels through the area of interest (e.g., the tree grove).