Field Insect Detection Using Sticky Trap Imaging and ML
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Solution Overview
Problem
Insect populations in farmlands are challenging to accurately assess due to their small size, visual similarity, and hiding behavior, leading to inefficient and wasteful pesticide application, which degrades soil health and poses environmental risks.
Innovation Solution
A field analysis system with sensor stations equipped with imaging devices and agricultural sensors, using machine-learning based object detection to identify insect populations and prescribe targeted pesticide application based on real-time data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual scouting is used to estimate insect populations, then farmers can identify insect presence, but the process is tedious, slow, and expensive
Solution Approach 1:
The patent replaces manual mechanical scouting with an automated optical detection system. Imaging devices capture images of crops, and machine learning algorithms automatically analyze these images to detect and count insects, eliminating the need for manual inspection while improving both speed and accuracy of population estimation.
Solution Approach 2:
The system enables self-service through autonomous operation. The imaging devices and machine learning models work automatically to detect, classify, and count insects without human intervention. The system processes images and generates population estimates independently, freeing farmers from tedious manual scouting tasks.
2Reliability
If chemical pesticides are applied to entire fields, then insect populations are controlled, but soil health is degraded and non-pest organisms are eliminated
Solution Approach 1:
The patent implements localized pesticide application by first using imaging devices and machine learning to identify specific areas with insect infestations. Pesticides are then applied only to these targeted locations rather than entire fields, maintaining insect control effectiveness while minimizing harm to soil health and non-pest organisms in unaffected areas.
Solution Approach 2:
The system applies pesticides partially rather than excessively. By detecting actual insect presence and distribution through image analysis, the system applies pesticides only where needed and in appropriate quantities, avoiding the excessive application to entire fields that causes environmental harm.
3Reliability
If excess chemical pesticides are applied, then insect populations are suppressed, but pesticide runoff contaminates water sources
Solution Approach 1:
The patent uses localized detection and application. Imaging devices identify specific locations with insect infestations, and pesticides are applied only to these targeted spots. This prevents excess pesticide from reaching water sources through runoff, as application is restricted to areas where insects are actually present.
Solution Approach 2:
The system incorporates feedback through continuous monitoring. Imaging devices regularly capture images to detect insect presence, and this information feeds into the decision-making process for pesticide application. The system adjusts application based on actual detected insect populations, preventing unnecessary application that would lead to water contamination.
4Measurement precision
If imaging devices and machine learning are used to detect insects, then insect populations are accurately identified, but device complexity increases
Solution Approach 1:
The patent employs multi-functional sensor stations that perform multiple tasks. The imaging devices not only detect insects but also capture crop imagery for analysis. The machine learning models handle both insect detection and classification. This universal approach consolidates multiple functions into integrated sensor stations, managing complexity through functional integration rather than separate specialized devices.
Data Source
AI summary
A field analysis system includes a plurality of sensor stations, a field data processor, a gateway server, and an access device. Each sensor station includes an imaging device, at least one agricultural sensor, and a sticky trap. The field data processor includes a field data processor configured to receive data collected by the plurality of sensor stations. Each of the sensor stations intermittently collects data in the form of image data comprising images taken of the sticky trap using the imaging device, and sensor data taken from the at least one agricultural sensor. A sensor station processor is configured to extract insect population data from the image data. The insect population data and sensor data are transmitted to the field data processor where it is processed to generate a prescribed field action. This is transmitted to a cloud server via the gateway server. The cloud server is accessible by the access device.