Prediction of fruit rot disease incidence in Arecanut based on weather parameters
¹Manipal Academy of Higher Education, Manipal Institute of Technology, Department of Computer Science and Engineering, Madhav Nagar, Manipal, IN576104 Karnataka India
²Manipal Academy of Higher Education, Manipal Institute of Technology, Department of Computer Science and Engineering, BSF Campus, Yelahanka, Bangalore, IN560064 Karnataka, India
³PES University, PES Institute of Technology, Department of Electronics and Communication, Dwaraka Nagar, Banashankari, Bangalore, IN560085 Karnataka, India
*Correspondence: raji.krish@manipal.edu
Abstract:
The 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.
Key words:
crop disease, cross-validation, deep learning, fruit rot disease, prediction