Digital Disease Modeling for Crop Risk Prediction
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Solution Overview
Problem
Agricultural field managers lack an effective method to determine the likelihood of disease onset in crops, leading to unnecessary fungicide application and potential yield loss, as they cannot manually monitor large areas for disease symptoms.
Innovation Solution
A server computer system that models disease risk using environmental, crop, and management data to predict disease onset, generating recommendations for fungicide application and automating preventative measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fungicide is applied to prevent disease, then crop protection is improved, but cost and environmental impact increase due to unnecessary application
Solution Approach 1:
The system performs preliminary disease risk assessment by analyzing environmental conditions, crop susceptibility, and historical data before fungicide application. This allows field managers to take preventive action only when disease risk is predicted, avoiding unnecessary fungicide application and associated costs while maintaining crop protection where needed.
Solution Approach 2:
The system continuously monitors field conditions, disease symptoms, and environmental parameters, then feeds this information back to update disease risk predictions. This feedback loop enables dynamic adjustment of fungicide application timing and targeting, ensuring protection is applied only when and where disease risk is confirmed, reducing waste and improving cost-efficiency.
2Measurement precision
If manual disease monitoring is performed, then disease detection accuracy is improved, but labor requirements and time consumption increase
Solution Approach 1:
The system enables self-service disease monitoring through automated data collection from field sensors, satellite imagery, and environmental stations. The disease risk prediction model operates autonomously, analyzing data and generating predictions without requiring manual field inspection, thereby maintaining detection accuracy while eliminating time-consuming manual monitoring activities.
Solution Approach 2:
The system replaces manual mechanical inspection with automated digital monitoring using remote sensing, environmental sensors, and computer vision technology. This substitution maintains or improves disease detection accuracy through continuous automated observation while dramatically reducing the time and labor required compared to manual field scouting.
3Reliability
If fungicide is applied broadly across the field, then disease prevention coverage is improved, but resource waste increases in areas not affected by disease
Solution Approach 1:
The system applies fungicide with local quality by targeting specific zones or individual plants within the field that are predicted to be at high disease risk. Based on spatial analysis of environmental conditions, crop susceptibility, and early disease symptoms, fungicide application is concentrated only in affected areas rather than applied uniformly across the entire field, reducing waste while maintaining effective prevention coverage where needed.
Solution Approach 2:
The system segments the field into management zones based on disease risk predictions, dividing the homogeneous field into heterogeneous sub-areas with different disease probabilities. This segmentation enables differentiated fungicide application strategies, applying treatment only to high-risk segments while leaving low-risk areas untreated, thereby maintaining prevention coverage effectiveness while minimizing fungicide waste.
Data Source
AI summary
A system and method for identifying a probability of disease affecting a crop based on data received over a network is described herein, and may be implemented using computers for providing improvements in plant pathology, plant pest control, agriculture, or agricultural management. In an embodiment, a server computer receives environmental risk data, crop data, and crop management data relating to one or more crops on a field. Agricultural intelligence computer system 130 computes one or more crop risk factors based, at least in part, the crop data, one or more environmental risk factors based, at least in part, the environmental data, and one or more crop management risk factors based, at least in part, on the crop management data. Using a digital model of disease probability, agricultural intelligence computer system 130 computes a probability of onset of a particular disease for the one or more crops on the field based, at least in part, on the one or more crop risk factors, the one or more environmental risk factors, and the one or more crop management factors.