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A deep learning model for predicting risks of crop pests and diseases from sequential environmental data.
- Source :
- Plant Methods; 12/14/2023, Vol. 19 Issue 1, p1-8, 8p
- Publication Year :
- 2023
-
Abstract
- Crop pests reduce productivity, so managing them through early detection and prevention is essential. Data from various modalities are being used to predict crop diseases by applying machine learning methodology. In particular, because growth environment data is relatively easy to obtain, many attempts are made to predict pests and diseases using it. In this paper, we propose a model that predicts diseases through previous growth environment information of crops, including air temperature, relative humidity, dew point, and CO<subscript>2</subscript> concentration, using deep learning techniques. Using large-scale public data on crops of strawberry, pepper, grape, tomato, and paprika, we showed the model can predict the risk score of crop pests and diseases. It showed high predictive performance with an average AUROC of 0.917, and based on the predicted results, it can help prevent pests or post-processing. This environmental data-based crop disease prediction model and learning framework are expected to be universally applicable to various facilities and crops for disease/pest prevention. [ABSTRACT FROM AUTHOR]
- Subjects :
- PLANT diseases
DEEP learning
AGRICULTURAL pests
DISEASE risk factors
DEW point
Subjects
Details
- Language :
- English
- ISSN :
- 17464811
- Volume :
- 19
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Plant Methods
- Publication Type :
- Academic Journal
- Accession number :
- 174206604
- Full Text :
- https://doi.org/10.1186/s13007-023-01122-x