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All Journal International Journal of Electrical and Computer Engineering
Lingaraju, Raviprakash Madenur
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Computer Science & IT Electrical & Electronics Engineering

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Seasonal auto-regressive integrated moving average with bidirectional long short-term memory for coconut yield prediction Jayanna, Niranjan Shadaksharappa; Lingaraju, Raviprakash Madenur
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp783-791

Abstract

Crop yield prediction helps farmers make informed decisions regarding the optimal timing for crop cultivation, taking into account environmental factors to enhance predictive accuracy and maximize yields. The existing methods require a massive amount of data, which is complex to acquire. To overcome this issue, this paper proposed a seasonal auto-regressive integrated moving average-bidirectional long short-term memory (SARIMA-BiLSTM) for coconut yield prediction. The collected dataset is preprocessed through a label encoder and min-max normalization is employed to change non-numeric features into numerical features and enhance model performance. The preprocessed features are selected through an adaptive strategy-based whale optimization algorithm (AS-WOA) to avoid local optima issues. Then, the selected features are given to the SARIMA-BiLSTM to predict the coconut yields. The proposed SARIMA-BiLSTM is adaptable to handling a widespread of various seasonal patterns and captures spatial features. The SARIMA-BiLSTM performance is estimated through the coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE). SARIMA-BiLSTM attains 0.84 of R2, 0.056 of MAE, 0.081 of MSE, and 0.907 of RMSE which is better when compared to existing techniques like multilayer stacked ensemble, convolutional neural network and deep neural network (CNN-DNN) and autoregressive moving average (ARIMA).
A prediction of coconut and coconut leaf disease using MobileNetV2 based classification Gopalakrishna, Kavitha Magadi; Lingaraju, Raviprakash Madenur
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2834-2844

Abstract

This research is aimed at effectively predicting coconut and coconut leaf disease using enhanced MobileNetV2 and ResNet50 methods. The stages involved in this implemented method are data collection, pre-processing, feature extraction, and classification. At first, data is collected from coconut and coconut leaf datasets. Gaussian filter and data augmentation techniques are applied on these images to eliminate noise during the pre-processing phase. Then, features are extracted using ResNet50 technique, while the diseases are classified using MobileNetV2 approach. In comparison to the existing methods namely, EfficientDet-D2, DL-assisted whitefly detection model (DL-WDM), and modified inception net-based hyper tuning support vector machine (MIN-SVM), the proposed method achieves superior classification values with 99.99% and 99.2% accuracy for coconut leaf and for coconuts, respectively.
Co-Authors Gopalakrishna, Kavitha Magadi Jayanna, Niranjan Shadaksharappa