Received: July 19th, 2022 ; Accepted: October 20th, 2022 ; Published: November 22nd, 2022 ; Correspondence: [email protected] occurrence of pests and diseases in arecanut crops has always been an important
factor affecting the total production of arecanut. Arecanut is always dependent on environmental
factors during its growth. Thus monitoring and early prediction of the occurrence of the disease
would be very helpful for prevention and therefore more crop production. Here, we propose
artificial intelligence-based deep learning models for fruit rot disease prediction. Historical data
on fruit rot incidence in representative areas of arecanut production in Udupi along with historical
weather data are the parameters used to develop region-specific models for the Udupi district.
The fruit rot disease incidence score value is predicted using recurrent neural network variants
(i.e., Vanilla LSTM, Vanilla GRU, stacked LSTM, and Bidirectional LSTM) for the first time.
The predictive performance of the proposed models is evaluated by mean square error (MSE)
along with the 5-fold cross-validation technique. Further, compared to other deep learning and
machine learning models, the Vanilla LSTM model gives 1.5 MSE, while the Vanilla GRU model
gives 1.3 MSE making it the best prediction model for arecanut fruit rot disease
