A deep learning model for predicting risks of crop pests and diseases from sequential environmental data

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 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.

Research Progress on Plant Responses to Stress Combinations in the Context of Climate Change

“…Integrated application and deeper mining of these data in combination with meta-analysis, CRISPR/Cas9 gene editing, and nanotechnology can improve our understanding of stress combinations [ 27 ]. Precision agriculture is the future direction of agricultural development, and the use of remote sensing data and machine learning, coupled with improved phenotyping and breeding methods, allows for the rapid discrimination of resistance phenotypes in plants through high-throughput methods [ 102 ], predicting plant pest and disease risks [ 103 , 104 , 105 ], controlling weeds [ 106 , 107 ], identifying environmental and nutrient status [ 108 ], and monitoring plant growth [ 109 ]. The combined use of these can accelerate the development of resistant plant varieties, favoring plant growth efficiency and tolerance to stress combinations [ 63 ].…”