Image-Based Insect Attack Prediction with Environmental Data
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Solution Overview
Problem
Existing insect attack prediction methods are inefficient and require human intervention, lacking timely and precise monitoring capabilities, especially for agricultural crops, which leads to ineffective remedies and limited adoption of biological pesticides.
Innovation Solution
A system and method utilizing a processor apparatus with integrated sensing, insect identification, and risk prediction modules, employing digital cameras, sensors, and machine learning algorithms to automatically analyze environmental and meteorological data for precise insect attack probability prediction, adapting to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expert-based manual analysis is used to identify insects and estimate attacks, then human expertise can be applied, but the system requires continuous human intervention and shows limited prediction capability
Solution Approach 1:
The patent replaces the mechanical system of manual expert analysis with an automated image recognition system using deep learning algorithms. The system processes agricultural field images automatically to identify insect species, their locations, and infestation levels without requiring continuous human intervention, thereby substituting human expertise with automated computational analysis.
Solution Approach 2:
The system enables self-service by allowing the prediction system to automatically perform insect identification, risk assessment, and prediction without external human intervention. The deep learning model continuously learns from new data and automatically updates its predictions, making the system self-sufficient in terms of operation and decision-making.
2Loss of time
If traditional monitoring methods are used, then simple equipment can be deployed, but timely and precise monitoring capabilities are lacking
Solution Approach 1:
The system performs preliminary action by continuously monitoring agricultural fields and identifying insect infestations at early stages before significant damage occurs. The deep learning model analyzes images in real-time to detect the presence and development of insect populations, enabling early warning and timely intervention before the infestation reaches critical levels.
Solution Approach 2:
The system implements feedback mechanisms by continuously comparing predicted insect attack risks with actual monitoring data. The model receives feedback from ongoing surveillance, adjusts its predictions based on observed patterns, and refines its accuracy over time. This closed-loop feedback system ensures both timeliness and precision in monitoring and prediction.
3Object-affected harmful factors
If biological pesticides are applied without timely monitoring, then environmental-friendly control can be used, but effectiveness is limited due to delayed intervention
Solution Approach 1:
The system enables preliminary action by detecting insect infestations at early stages and predicting future attack risks before significant crop damage occurs. This early detection allows farmers to apply biological pesticides proactively rather than reactively, ensuring that control measures are implemented at the optimal time when they are most effective and when pest populations are still manageable.
Data Source
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AI summary
It is described an insect attack prediction system (100), comprising: at least one processor provided with a plurality of software modules comprising: an insect identification module (103) configured to process at least one insect digital image (IM) to provide a presence value (IPD), representing the presence of insects in an area of interest for insect attack; a data collecting module (102) configured to acquire insect behavioural data associated to said area and comprising at least one of the following data groups: meteorological data; environmental data; historical data of insect presence. The system further comprises a prediction module (104) configured to process the presence value (IPD) and the insect behavioural data according to a mathematical prediction algorithm (302) to estimate a risk of attack (PRB) to the area of interests.