Agricultural Sensor Image Processing for Real-Time Weed Treatment
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Solution Overview
Problem
Current agricultural technologies face challenges in efficiently identifying and managing undesirable vegetation in fields, as existing methods are labor-intensive and require large amounts of land, chemicals, and time, with existing machine learning models struggling to accurately distinguish between crop and weeds in real-time due to the vast and dynamic nature of agricultural environments.
Innovation Solution
A computer-implemented method using machine learning processing on sensor inputs from agricultural vehicles to identify and control undesirable vegetation, involving real-time image processing, multi-model training, and user feedback to improve the accuracy and efficiency of object detection and treatment, reducing the data required for training and enabling selective data culling for subsequent use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to identify and manage undesirable vegetation, then coverage is achieved, but labor intensity and time consumption increase significantly
Solution Approach 1:
The patent replaces manual mechanical identification and treatment of undesirable vegetation with an automated optical system. Sensors capture images of vegetation, machine learning algorithms automatically classify plants as crop or weed, and the system controls treatment mechanisms to apply herbicides only to identified weeds, eliminating labor-intensive manual inspection and treatment
Solution Approach 2:
The system enables self-service by allowing the agricultural vehicle to autonomously identify, classify, and treat undesirable vegetation without human intervention. The machine learning model continuously processes sensor data in real-time, automatically making decisions about which plants require treatment and controlling the treatment mechanism accordingly
2Reliability
If conventional vegetation management is used, then all vegetation is treated, but chemical usage and environmental impact increase
Solution Approach 1:
The patent applies local quality by treating only the specific locations where undesirable vegetation is identified rather than treating all vegetation uniformly. The system uses precision targeting to apply herbicides only to weed locations detected by the sensor and classification system, leaving crop plants untreated and significantly reducing overall chemical usage
Solution Approach 2:
The system implements feedback by using sensors to continuously monitor vegetation, machine learning algorithms to classify plants in real-time, and using this information to control treatment mechanisms. The closed-loop system verifies treatment effectiveness and adjusts operations based on actual field conditions, ensuring chemicals are applied only where needed
3Measurement precision
If machine learning models are trained with large datasets, then accuracy improves, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent extracts only the essential features needed for accurate plant classification rather than processing complete images or using complex models. The system identifies key distinguishing characteristics of crop versus weed plants and focuses computational resources on these critical features, reducing overall computational complexity while maintaining high identification accuracy
Solution Approach 2:
The system segments the image processing task into distinct stages: initial image capture, preprocessing to extract relevant features, machine learning classification based on those features, and treatment decision-making. This segmentation allows each stage to be optimized independently, reducing the computational burden of any single processing step
Data Source
AI summary
A computer-implemented method of sensor input processing, implemented by an agricultural platform comprising a processor and a sensor, includes capturing, using the sensor, sensor images of a vicinity of a target object of a time interval during which a treatment is applied to the target object; processing the sensor images using one or more machine learning (ML) algorithms wherein at least one ML algorithm uses an ML model trained to detect a presence of a treatment action in the vicinity of the target object; and providing, selectively based on a result of detecting the presence of the treatment action in the vicinity of the target object, an outcome of the processing for further processing.