A multi-layer perceptron-based approach for early detection of BSR disease in oil palm trees using hyperspectral images

A deep learning model for rapid classification of tea coal disease

BACKGROUND

The common tea tree disease known as "tea coal disease" (Neocapnodium theae Hara) can have a negative impact on tea yield and quality. The majority of conventional approaches for identifying tea coal disease rely on observation with the human naked eye, which is labor- and time-intensive and frequently influenced by subjective factors. The present study developed a deep learning model based on RGB and hyperspectral images for tea coal disease rapid classification.

RESULTS

Both RGB and hyperspectral could be used for classifying tea coal disease. The accuracy of the classification models established by RGB imaging using ResNet18, VGG16, AlexNet, WT-ResNet18, WT-VGG16, and WT-AlexNet was 60%, 58%, 52%, 70%, 64%, and 57%, respectively, and the optimal classification model for RGB was the WT-ResNet18. The accuracy of the classification models established by hyperspectral imaging using UVE-LSTM, CARS-LSTM, NONE-LSTM, UVE-SVM, CARS-SVM, and NONE-SVM was 80%, 95%, 90%, 61%, 77%, and 65%, respectively, and the optimal classification model for hyperspectral was the CARS-LSTM, which was superior to the model based on RGB imaging.

CONCLUSIONS

This study revealed the classification potential of tea coal disease based on RGB and hyperspectral imaging, which can provide an accurate, non-destructive, and efficient classification method for monitoring tea coal disease.

Classification of basal stem rot using deep learning: a review of digital data collection and palm disease classification methods

Oil palm is a key agricultural resource in Malaysia. However, palm disease, most prominently basal stem rot caused at least RM 255 million of annual economic loss. Basal stem rot is caused by a fungus known as Ganoderma boninense. An infected tree shows few symptoms during early stage of infection, while potentially suffers an 80% lifetime yield loss and the tree may be dead within 2 years. Early detection of basal stem rot is crucial since disease control efforts can be done. Laboratory BSR detection methods are effective, but the methods have accuracy, biosafety, and cost concerns. This review article consists of scientific articles related to the oil palm tree disease, basal stem rot, Ganoderma Boninense, remote sensors and deep learning that are listed in the Web of Science since year 2012. About 110 scientific articles were found that is related to the index terms mentioned and 60 research articles were found to be related to the objective of this research thus included in this review article. From the review, it was found that the potential use of deep learning methods were rarely explored. Some research showed unsatisfactory results due to limitations on dataset. However, based on studies related to other plant diseases, deep learning in combination with data augmentation techniques showed great potentials, showing remarkable detection accuracy. Therefore, the feasibility of analyzing oil palm remote sensor data using deep learning models together with data augmentation techniques should be studied. On a commercial scale, deep learning used together with remote sensors and unmanned aerial vehicle technologies showed great potential in the detection of basal stem rot disease.

An ensemble deep learning approach for predicting cocoa yield

One important aspect of agriculture is crop yield prediction. This aspect allows decision-makers and farmers to make adequate planning and policies. Before now, various statistical models have been used for crop yield prediction but this approach experienced some hiccups such as time wastage, inaccurate prediction, and difficulties in model usage. Recently, a new trend of deep learning and machine learning are now adopted for crop yield prediction. Deep learning can extract patterns from a large volume of the dataset, thus, they are suitable for prediction. The research work aims to propose an efficient deep-learning technique in the field of cocoa yield prediction. This research presents a deep learning approach for cocoa yield prediction using a Convolutional Neural Network and Recurrent Neural Network (CNN-RNN) with Long Short Term Memory (LSTM). The ensemble approach was adopted because of the nature of the dataset used. Two different sets of the dataset were used, namely; the climatic dataset and the cocoa yield dataset. CNN-RNN with LSTM has some salient features, where CNN was used to handle the climatic dataset, and RNN was employed to handle the cocoa yield prediction in southwest Nigeria. Two major problems generated by the CNN-RNN model are vanishing and exploding gradients and this was handled by LSTM. The proposed model was benchmarked with other machine learning algorithms based on Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). CNN-RNN with LSTM gave the least mean of absolute error as compared to the other machine learning algorithms which shows the efficiency of the model.