Machine-Learning Approach Using SAR Data for the Classification of Oil Palm Trees That Are Non-Infected and Infected with the Basal Stem Rot Disease

Abstract

Basal stem rot disease (BSR) in oil palm plants is caused by the Ganoderma boninense (G. boninense) fungus. BSR is a major disease that affects oil palm plantations in Malaysia and Indonesia. As of now, the only available sustaining measure is to prolong the life of oil palm trees since there has been no effective treatment for the BSR disease. This project used an ALOS PALSAR-2 image with dual polarization, Horizontal transmit and Horizontal receive (HH) and Horizontal transmit and Vertical receive (HV). The aims of this study were to (1) identify the potential backscatter variables; and (2) examine the performance of machine learning (ML) classifiers (Multilayer Perceptron (MLP) and Random Forest (RF) to classify oil palm trees that are non-infected and infected by G. boninense. The sample size consisted of 55 uninfected trees and 37 infected trees. We used the imbalance data approach (Synthetic Minority Over-Sampling Technique (SMOTE) in these classifications due to the differing sample sizes. The result showed backscatter variable HV had a higher correct classification for the G. boninense non-infected and infected oil palm trees for both classifiers; the MLP classifier model had a robust success rate, which correctly classified 100% for non-infected and 91.30% for infected G. boninense, and RF had a robust success rate, which correctly classified 94.11% for non-infected and 91.30% for infected G. boninense. In terms of model performance using the most significant variables, HV, the MLP model had a balanced accuracy (BCR) of 95.65% compared to 92.70% for the RF model. Comparison between the MLP model and RF model for the receiver operating characteristics (ROC) curve region, (AUC) gave a value of 0.92 and 0.95, respectively, for the MLP and RF models. Therefore, it can be concluded by using only the HV polarization, that both the MLP and RF can be used to predict BSR disease with a relatively high accuracy.

Prediction of Pest Insect Appearance Using Sensors and Machine Learning

“…Reference [ 16 ] uses ML models to classify crops and monitor plant growth status. Using the ML approach, it is also possible to classify plant leaves based on image analysis [ 17 ], plant segmentation [ 18 ] or palm tree classification, depending on whether they are infected with plant diseases [ 19 ].…”

Vegetation Effects on Soil Moisture Retrieval from Water Cloud Model Using PALSAR-2 for Oil Palm Trees

“…In this study, the L band from PALSAR-2 was considered to give a better penetration in oil palm tree structure where backscatter coefficient characterizes the nature of oil palm structure. It was reported that oil palm trees using L band were capable to penetrate to the basal trunk in the oil palm plantation using both HH and HV polarizations [68]. Using the WCM model, the backscatter coefficients were simulated using a combination of vegetation descriptors.…”

Classification of Non-Infected and Infected with Basal Stem Rot Disease Using Thermal Images and Imbalanced Data Approach

“…Recent studies have demonstrated that hyperspectral and multispectral remote sensing methods can distinguish healthy and BSR-infected trees [14][15][16][17][18][19][20]. Terrestrial laser scanning (TLS) [21][22][23], synthetic aperture radar (SAR) data [24,25], intelligent electronic nose (E-Nose) systems [26,27], tomographic sensors [28,29], and microfocus X-ray fluorescence [30] also showed positive results in detecting BSR-infected trees. These reports showed that the approaches employed can detect BSR early and distinguish healthy from BSR-infected trees.…”