Author:
Lee Sangyeon,Yun Choa Mun
Abstract
AbstractCrop 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 CO2 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.
Funder
Rural Development Administration, Republic of Korea
Publisher
Springer Science and Business Media LLC
Subject
Plant Science,Genetics,Biotechnology
Reference39 articles.
1. Hardwick NV. Disease forecasting. In: Jones DG, editor. The epidemiology of plant diseases. Dordrecht: Springer; 1998. (10.1007/978-94-017-3302-1_10).
2. Savary S, Willocquet L, Pethybridge SJ, et al. The global burden of pathogens and pests on major food crops. Nat Ecol Evol. 2019;3:430–9. https://doi.org/10.1038/s41559-018-0793-y.
3. Savary S, Bregaglio S, Willocquet L, et al. Crop health and its global impacts on the components of food security. Food Sec. 2017;9:311–27. https://doi.org/10.1007/s12571-017-0659-1.
4. Sharma S, Kooner R, Arora R. Insect pests and crop losses. In: Arora R, Sandhu S, editors. Breeding insect resistant crops for sustainable agriculture. Singapore: Springer; 2017. (10.1007/978-981-10-6056-4_2).
5. Mahmud MS, Zaman QU, Esau TJ, Price GW, Prithiviraj B. Development of an artificial cloud lighting condition system using machine vision for strawberry powdery mildew disease detection. Comput Electron Agric. 2019;158:219–25.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献