Autonomous Agricultural Spray Evaluation via Image Comparison
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Solution Overview
Problem
Current agricultural technologies face challenges in efficiently managing land, chemicals, time, labor, and costs for crop production and harvesting, with existing methods being incremental improvements rather than significant advancements.
Innovation Solution
An agricultural treatment system equipped with a moveable treatment head and image sensors that uses computer vision to identify and track agricultural objects, adjust the spraying head's position, and emit fluid projectiles based on image comparison and calibration tables, optimizing fluid application and reducing waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional spraying methods are used to treat agricultural objects, then treatment coverage is achieved, but precision and resource efficiency deteriorate due to blanket application over entire areas
Solution Approach 1:
The system transitions from uniform blanket spraying to localized precision spraying by identifying specific agricultural objects through image sensors and applying treatment only to detected targets. The treatment head assembly with multiple spraying tips delivers fluid projectiles selectively to individual agricultural objects based on their detected positions, achieving local quality treatment rather than area-wide application.
Solution Approach 2:
The system uses image sensors to automatically detect, identify, and track agricultural objects, then autonomously controls the treatment head assembly to spray only where needed. This self-service capability eliminates the need for manual area-based spraying decisions, enabling the system to serve itself by making real-time targeting decisions based on sensor data.
2Measurement precision
If image sensors and computer vision are used to identify and track agricultural objects, then spray targeting precision is improved, but device complexity increases due to additional sensors and processing requirements
Solution Approach 1:
The image sensors serve multiple functions: detecting agricultural objects, tracking their positions, identifying treatment targets, and providing feedback for spray evaluation. This multi-functionality reduces the need for separate specialized sensors for each task, thereby managing system complexity while achieving high detection accuracy through a versatile sensing platform.
Solution Approach 2:
The system implements feedback loops where image sensors continuously monitor agricultural objects, the control system processes this visual data to determine precise spray targets, executes spraying actions, and then uses image sensors again to evaluate spray outcomes. This feedback mechanism enables continuous optimization of spray precision without requiring overly complex standalone systems for each function.
3Manufacturing precision
If a moveable treatment head with multiple spraying tips is used, then treatment precision is improved, but device complexity and manufacturing difficulty increase
Solution Approach 1:
The treatment head assembly is segmented into multiple independent spraying tips, each capable of being controlled separately. This segmentation allows for modular manufacturing where individual tips can be produced and tested independently, then assembled into the complete treatment head. The segmented design simplifies manufacturing complexity compared to a single complex spray mechanism while maintaining high precision through multiple targeted spray points.
Data Source
AI summary
Various embodiments of an apparatus, methods, systems and computer program products described herein are directed to an agricultural treatment system and method of operation. The agricultural treatment system may obtain with one or more image sensors at a first time period, a first set of images each comprising a plurality of pixels depicting a ground area and a first target agricultural object positioned in the ground area. The system may emit a first fluid projectile of a first fluid at the first target agricultural object. The system may obtain with the one or more image sensors at a second time period, a second set of images each comprising a plurality of pixels depicting the ground area and the agricultural object. The system may compare the first image with the second image to determine a change in pixels between at least a first image of the first set of images and at least a second image of the second set of images. And the system may, based on the determined change in pixels as between the first and second images, identify a first group of pixels that represent a first spray object.