Automated Pest Forecasting via Image Recognition and AI
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current pest monitoring methods are time-consuming and inefficient, leading to delayed responses to pest pressure, inappropriate pesticide use, and increased risk of pest resistance, due to the need for manual trap inspections and reliance on phenology models that require extensive data collection and human intervention.
Innovation Solution
An automated system using a network of insect traps with image recognition and artificial intelligence to forecast pest populations, incorporating weather data and external sources, providing real-time alerts for optimal crop protection measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual trap inspection is performed regularly, then pest population data can be collected, but the reaction time to pest pressure is delayed by 7-10 days
Solution Approach 1:
The patent replaces manual mechanical inspection of traps with an automated image recognition system using cameras and AI algorithms. The system automatically captures images of trap contents, identifies pest species, and counts individuals, eliminating the need for human field visits and enabling real-time data collection without the 7-10 day delay inherent in manual inspection schedules.
Solution Approach 2:
The system enables self-service by allowing the trap network to automatically monitor and report pest populations without requiring human intervention for data collection. The automated image processing and pest identification system continuously operates, providing real-time alerts when pest thresholds are exceeded, thus serving itself rather than requiring external manual inspection.
2Loss of time
If trap inspection frequency is increased to reduce reaction time, then real-time pest alerts can be provided, but personnel time and fuel costs increase significantly
Solution Approach 1:
The patent substitutes human personnel with an automated electronic system for trap inspection. Cameras mounted at trap locations continuously monitor pest capture, and image recognition algorithms process the data automatically. This eliminates the need for human field visits, reducing personnel time and fuel consumption while providing real-time pest population monitoring and immediate alerts when intervention is needed.
Solution Approach 2:
The system creates digital copies (images) of the trap contents that can be analyzed remotely without physical inspection. Instead of transporting personnel to field locations, the system captures and transmits digital images of trapped pests to a central processing system, enabling real-time monitoring without the resource costs of physical travel and manual counting.
3Measurement precision
If phenology models are used to predict insect development, then pest forecast can be improved, but extensive data collection and human intervention are required
Solution Approach 1:
The patent replaces complex manual phenology modeling with automated image-based pest identification and counting systems. Instead of requiring extensive environmental data collection and complex mathematical modeling, the system directly observes actual pest populations through camera images, using AI algorithms to identify species and estimate development stages based on visual characteristics of trapped insects.
Solution Approach 2:
The system uses digital images as copies of actual pest populations to infer development stages and forecast trends. Rather than relying on indirect phenology models that require extensive environmental parameter measurement, the system directly captures visual evidence of pest presence and characteristics, simplifying the data collection process while maintaining forecast accuracy.
4Measurement precision
If manual pest identification and counting is performed, then pest data can be obtained, but staff motivation and qualification significantly affect data consistency
Solution Approach 1:
The patent replaces human staff with automated image recognition technology for pest identification and counting. The system uses trained AI algorithms that consistently identify pest species and count individuals based on image analysis, eliminating variability introduced by human factors such as motivation, fatigue, training level, and interpretation differences. This ensures uniform data quality across all trap locations and time periods.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method for performing a pest forecast, comprising: at a plurality of traps (1), capturing data (10) comprising: pictures of trap contents and associated metadata captured for a certain period of time (10a) and other data (10b) comprising GPS coordinates of the respective trap; and sending said data (10, 10a, 10b) through communication means to a remote end (2) located in the cloud; from the pictures of trap contents and associated metadata (10a) captured at each trap (1), applying a visual processing and learning stage (21) comprising applying a predictive model (22) for estimating a number of identified insects (31) in each trap (1) for said certain period of time; preprocessing (25) at least said estimated number of identified insects (31) in each trap (1), said metadata and said other data (10b), said preprocessing (25) comprising applying machine learning techniques; storing the preprocessed data (32) in data storage means (27), thus keeping historical data along time; from the recently preprocessed data (32) and the historical data, applying decision making algorithms (26) for performing a pest forecast (33) for each trap (1) for the next N days. System and computer program.