Plant Disease Forecasting Models

Nath, Rupashri

doi:10.18782/2582-2845.8280

Cited by 1 publication

(1 citation statement)

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“…In order to minimize the economic and environmental damages caused by these pests, several attempts have been made to predict pests [1,10]. These disease modeling attempts create several mathematical models that explain the occurrence and development of pests by various factors and use it to predict the occurrence and severity of pests [11,12], understand eases and pests reduce the yield and quality of crops, increase costs due to increased labor for pest treatment, the development of disease [13][14][15], It is used for assisting tactical decision making [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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

Lee,

Yun

2023

Plant Methods

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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.

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