Agricultural Treatment Cartridges for Plant-Level Precision Spraying
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Solution Overview
Problem
Conventional agricultural treatment methods are inefficient and wasteful, as they rely on coarse resolution applications of chemicals, often targeting entire fields or rows of crops rather than individual plants, leading to unnecessary chemical use and reduced precision in crop management, especially in challenging conditions like changing weather and limited arable land.
Innovation Solution
An agricultural treatment delivery system that uses autonomous vehicles equipped with sensors and precision technology to identify and treat specific agricultural objects, such as individual plants, with micro-precision, allowing for targeted application of fertilizers, herbicides, or other treatments based on real-time data and machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If conventional boom sprayers with flat fan or cone-shaped spray patterns are used to disperse chemicals, then coverage area is increased, but spray precision deteriorates causing chemicals to fall on non-intended targets like soil
Solution Approach 1:
The spray system is segmented into multiple individually controllable nozzle groups or single nozzles that can be independently activated. This allows selective application of chemicals to specific plants or rows rather than blanket coverage, improving precision while maintaining adequate coverage area through strategic positioning of active nozzles.
Solution Approach 2:
The system implements local quality by varying spray characteristics (activation, droplet size, application rate) at different locations along the boom. Sensors identify specific plants or areas requiring treatment, and the control system adjusts spray parameters locally to match the actual treatment needs, preventing waste on non-intended targets while ensuring adequate coverage where needed.
2Device complexity
If coarse resolution chemical application targeting entire fields or rows is used, then equipment complexity is reduced, but manufacturing precision of treatment application deteriorates
Solution Approach 1:
The system transitions from static, fixed-pattern spray application to dynamic, real-time controlled spray delivery. Sensors continuously monitor plant conditions and locations, and the control system dynamically adjusts which nozzles are active, at what rates, and with what droplet sizes based on current field conditions, achieving high precision without requiring complex custom equipment design.
Solution Approach 2:
The system incorporates feedback loops where sensors detect plant characteristics, location, and treatment needs, and this information feeds back to the control system which adjusts spray parameters in real-time. This closed-loop control achieves precision treatment application using relatively simple equipment by leveraging intelligent feedback-based decision-making.
3Ease of operation
If conventional spray nozzles with apertures facing the ground are used, then ease of operation is improved, but loss of substance increases due to spray falling on non-intended targets
Solution Approach 1:
The system performs preliminary identification and classification of plants requiring treatment before chemical application. Sensors scan and map the field, identifying target plants and their precise locations in advance, allowing the control system to pre-position active nozzles and prepare appropriate spray parameters, thereby avoiding chemical waste on non-target areas while maintaining simple operational procedures.
Solution Approach 2:
The system changes spray parameters (droplet size, application rate, nozzle activation) based on real-time plant identification and treatment requirements. By adjusting these parameters dynamically, the system minimizes chemical waste while maintaining ease of operation, as the control system automatically manages parameter changes without requiring complex manual intervention.
Data Source
AI summary
Systems and methods for computer vision and automation to autonomously identify and deliver for application a treatment to an object among other objects, data science and data analysis, including machine learning, deep learning, and other disciplines of computer-based artificial intelligence to facilitate identification and treatment of objects, and robotics and mobility technologies to navigate a delivery system, more specifically, to an agricultural delivery system configured to identify and apply, for example, an agricultural treatment to an identified agricultural object. In some examples, a method may include identifying a subset of payloads to provide one or more actions based on data representing a policy for one or more subsets of agricultural objects, causing one or more cartridges to be charged based on the subset of payloads, and, and implementing one or more cartridges at an agricultural projectile delivery system.