Version 1
: Received: 16 December 2023 / Approved: 19 December 2023 / Online: 19 December 2023 (08:43:36 CET)
How to cite:
Guo, Z.; Shi, D.; Chen, X.; Chi, Y.; Li, M. Construction of Peanut Leaf Spot Disease Prediction Model Based on Improved LSTM Model and Meteorological Data. Preprints2023, 2023121420. https://doi.org/10.20944/preprints202312.1420.v1
Guo, Z.; Shi, D.; Chen, X.; Chi, Y.; Li, M. Construction of Peanut Leaf Spot Disease Prediction Model Based on Improved LSTM Model and Meteorological Data. Preprints 2023, 2023121420. https://doi.org/10.20944/preprints202312.1420.v1
Guo, Z.; Shi, D.; Chen, X.; Chi, Y.; Li, M. Construction of Peanut Leaf Spot Disease Prediction Model Based on Improved LSTM Model and Meteorological Data. Preprints2023, 2023121420. https://doi.org/10.20944/preprints202312.1420.v1
APA Style
Guo, Z., Shi, D., Chen, X., Chi, Y., & Li, M. (2023). Construction of Peanut Leaf Spot Disease Prediction Model Based on Improved LSTM Model and Meteorological Data. Preprints. https://doi.org/10.20944/preprints202312.1420.v1
Chicago/Turabian Style
Guo, Z., Yucheng Chi and Ming Li. 2023 "Construction of Peanut Leaf Spot Disease Prediction Model Based on Improved LSTM Model and Meteorological Data" Preprints. https://doi.org/10.20944/preprints202312.1420.v1
Abstract
Peanut leaf spot is a worldwide disease whose prevalence poses a major threat to peanut yield and quality, and accurate prediction models are urgently needed for timely disease management. In this study, we proposed a novel peanut leaf spot prediction method based on an improved long short-term memory (LSTM) model and multi-year meteorological data combined with disease survey records. Our method employed a combination of convolutional neural network (CNN) and LSTM to capture spatial-temporal patterns from the data and improve the model ability to recognize dynamic features of the disease. In addition, we introduced an attention mechanism module to enhance model performance by focusing on key features. Through several hyper-parameter optimization adjustments, we identified a peanut leaf spot disease condition index prediction model with a learning rate of 0.001, a number of cycles (Epochs) of 800, and an optimizer of Adma. The results showed that the integrated model demonstrated excellent prediction ability, obtaining an RMSE of 0.063 and an R2 of 0.951, which reduced the RMSE by 0.253 and 0.204, and raised the R2 by 0.155 and 0.122, respectively, compared to the single CNN and LSTM. Predicting the incidence and severity of peanut leaf spot disease based on the meteorological conditions and neural networks is feasible and valuable to help growers made accurate management decisions and reduced disease impacts through optimal fungicide application timing.
Keywords
peanut leaf spot; disease prediction; LSTM; CNN; SE attention mechanism
Subject
Biology and Life Sciences, Agricultural Science and Agronomy
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
