Selective Weed Sprayer Loading Using Field Distribution Prediction
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Solution Overview
Problem
Existing agricultural sprayers face challenges in accurately predicting the amount of product needed for targeted weed treatment, particularly when completing a field spray with less than a full tank load, leading to economic losses from leftover product.
Innovation Solution
The use of a weed distribution model to estimate product requirements based on geographic location, combined with imaging and predictive modeling to identify and selectively apply herbicides to specific weed types, ensuring tanks are emptied to a threshold level at the end of the spraying task.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If full tank capacity is loaded for weed treatment spraying, then the sprayer can complete larger field areas, but leftover product remains in the tank causing economic loss
Solution Approach 1:
The system performs preliminary actions by creating a weed distribution model before spraying begins, predicting weed locations and densities ahead of time. This allows the sprayer to know exactly how much product will be needed for the remaining field area, enabling precise loading that eliminates leftover product while maintaining full productivity
Solution Approach 2:
The system uses feedback by continuously monitoring spray consumption rates and comparing them against the weed distribution model predictions. The control system adjusts product application in real-time based on actual weed density encountered, ensuring the tank is emptied precisely when the field treatment is complete, eliminating economic loss from leftover product
2Quantity of substance
If selective spraying is used to treat only detected weeds, then product usage is optimized, but accurate prediction of product quantity becomes more complex
Solution Approach 1:
The system introduces a weed distribution model as an intermediary between weed detection and product application. This model acts as a mediator that translates complex imaging and detection data into simplified predictions of product requirements, making the prediction system manageable while maintaining accurate selective spraying that optimizes product usage
Data Source
AI summary
A system for spraying an agricultural field provides a weed distribution model including information corresponding to a weed distribution as a function of geographic location within the field. An initial portion of the field is sprayed with a first product from a first tank leaving a remaining portion of the field to be sprayed. A quantity of the first product from the first tank required to complete the spraying of the remaining portion of the field is estimated based at least in part on the weed distribution model, the estimated quantity being less than the first tank capacity. The estimated quantity of the first product is loaded into the first tank so that the remaining portion of the field may be sprayed with the first tank being empty, at least to a threshold level, at the end of the spraying of the remaining portion of the field.