Hyperspectral spectroscopy and imbalance data approaches for classification of oil palm's macronutrients observed from frond 9 and 17

Amirruddin, Amiratul Diyana; Muharam, Farrah Melissa; Ismail, Mohd Hasmadi; Tan, Ngai Paing

doi:10.1016/j.compag.2020.105768

Cited by 17 publications

(7 citation statements)

References 19 publications

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“…DL strategy involves data manipulation activities during the pre-processing phase in machine learning. An example of oversampling-related method is Synthetic Minority Oversampling Technique (SMOTE) [51], while an example of undersampling related method is Random Under sampling (RUS) [52]. From the literature review conducted, several literatures related to HIMC data have been found.…”

Section: Strategies In Handling Imbalanced Datamentioning

confidence: 99%

Survey on Highly Imbalanced Multi-class Data

Hamid¹,

Yusoff²,

Mohamed³

2022

IJACSA

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Machine learning technology has a massive impact on society because it offers solutions to solve many complicated problems like classification, clustering analysis, and predictions, especially during the COVID-19 pandemic. Data distribution in machine learning has been an essential aspect in providing unbiased solutions. From the earliest literatures published on highly imbalanced data until recently, machine learning research has focused mostly on binary classification data problems. Research on highly imbalanced multi-class data is still greatly unexplored when the need for better analysis and predictions in handling Big Data is required. This study focuses on reviews related to the models or techniques in handling highly imbalanced multi-class data, along with their strengths and weaknesses and related domains. Furthermore, the paper uses the statistical method to explore a case study with a severely imbalanced dataset. This article aims to (1) understand the trend of highly imbalanced multi-class data through analysis of related literatures; (2) analyze the previous and current methods of handling highly imbalanced multi-class data; (3) construct a framework of highly imbalanced multi-class data. The chosen highly imbalanced multi-class dataset analysis will also be performed and adapted to the current methods or techniques in machine learning, followed by discussions on open challenges and the future direction of highly imbalanced multi-class data. Finally, for highly imbalanced multi-class data, this paper presents a novel framework. We hope this research can provide insights on the potential development of better methods or techniques to handle and manipulate highly imbalanced multiclass data.

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Section: Strategies In Handling Imbalanced Datamentioning

confidence: 99%

Survey on Highly Imbalanced Multi-class Data

Hamid¹,

Yusoff²,

Mohamed³

2022

IJACSA

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“…The best subset of frond number, chlorophyll-sensitive wavelengths, and the classifier to categorize the chlorophylls according to the nominated sufficiency levels were suggested using a hyperspectral remote sensing platform. Correspondingly, the use of hyperspectral sensing combined with imbalance approaches and MLAs to track the nutrients levels of mature oil palm is highlighted in [43]. Hyperspectral spectroscopy has emerged as a promising alternative to conventional foliar analysis in assessing the nutritional status of oil palms, as the former one is costly and time-consuming.…”

Section: Multipurpose Classificationmentioning

confidence: 99%

Oil Palm and Machine Learning: Reviewing One Decade of Ideas, Innovations, Applications, and Gaps

et al. 2021

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Machine learning (ML) offers new technologies in the precision agriculture domain with its intelligent algorithms and strong computation. Oil palm is one of the rich crops that is also emerging with modern technologies to meet global sustainability standards. This article presents a comprehensive review of research dedicated to the application of ML in the oil palm agricultural industry over the last decade (2011–2020). A systematic review was structured to answer seven predefined research questions by analysing 61 papers after applying exclusion criteria. The works analysed were categorized into two main groups: (1) regression analysis used to predict fruit yield, harvest time, oil yield, and seasonal impacts and (2) classification techniques to classify trees, fruit, disease levels, canopy, and land. Based on defined research questions, investigation of the reviewed literature included yearly distribution and geographical distribution of articles, highly adopted algorithms, input data, used features, and model performance evaluation criteria. Detailed quantitative–qualitative investigations have revealed that ML is still underutilised for predictive analysis of oil palm. However, smart systems integrated with machine vision and artificial intelligence are evolving to reform oil palm agri-business. This article offers an opportunity to understand the significance of ML in the oil palm agricultural industry and provides a roadmap for future research in this domain.

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“…This study considered the AUC and PRC value of 0.50 as fail, 0.51-0.69 as poor, 0.70-0.79 as acceptable, 0.80-0.89 as excellent, and 0.90-1.00 as outstanding [82]. Meanwhile, this study classified the success rate of classifying the non-infected and BSR-infected trees as poor if it was less than 40.00 percent, moderate if it was 40.00-80.00 percent, and robust if it was more significant than 80.00 percent [83].…”

Section: Accuracy Assessmentmentioning

confidence: 99%

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

et al. 2021

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Basal stem rot (BSR) disease occurs due to the most aggressive and threatening fungal attack of the oil palm plant known as Ganoderma boninense (G. boninense). BSR is a disease that has a significant impact on oil palm crops in Malaysia and Indonesia. Currently, the only sustainable strategy available is to extend the life of oil palm trees, as there is no effective treatment for BSR disease. This study used thermal imagery to identify the thermal features to classify non-infected and BSR-infected trees. The aims of this study were to (1) identify the potential temperature features and (2) examine the performance of machine learning (ML) classifiers (naïve Bayes (NB), multilayer perceptron (MLP), and random forest (RF) to classify oil palm trees that are non-infected and BSR-infected. The sample size consisted of 55 uninfected trees and 37 infected trees. We used the imbalance data approaches such as random undersampling (RUS), random oversampling (ROS) and synthetic minority oversampling (SMOTE) in these classifications due to the different sample sizes. The study found that the Tmax feature is the most beneficial temperature characteristic for classifying non-infected or infected BSR trees. Meanwhile, the ROS approach improves the curve region (AUC) and PRC results compared to a single approach. The result showed that the temperature feature Tmax and combination feature TmaxTmin had a higher correct classification for the G. boninense non-infected and infected oil palm trees for the ROS-RF and had a robust success rate, classifying correctly 87.10% for non-infected and 100% for infected by G. boninense. In terms of model performance using the most significant variables, Tmax, the ROS-RF model had an excellent receiver operating characteristics (ROC) curve region (AUC) of 0.921, and the precision–recall curve (PRC) region gave a value of 0.902. Therefore, it can be concluded that the ROS-RF, using the Tmax, can be used to predict BSR disease with relatively high accuracy.

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Hyperspectral spectroscopy and imbalance data approaches for classification of oil palm's macronutrients observed from frond 9 and 17

Cited by 17 publications

References 19 publications

Survey on Highly Imbalanced Multi-class Data

Survey on Highly Imbalanced Multi-class Data

Oil Palm and Machine Learning: Reviewing One Decade of Ideas, Innovations, Applications, and Gaps

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

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