Hyperspectral remote sensing for assessment of chlorophyll sufficiency levels in mature oil palm (Elaeis guineensis) based on frond numbers: Analysis of decision tree and random forest

Amirruddin, Amiratul Diyana; Muharam, Farrah Melissa; Ismail, Mohd Hasmadi; Ismail, Mohd Khairi; Tan, Ngai Paing; Karam, Daljit Singh

doi:10.1016/j.compag.2020.105221

Cited by 61 publications

(27 citation statements)

References 42 publications

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“…This clearly proved the merit of the RF classifier in modeling high-dimensional data because it intrinsically works with a random subset of features instead of all of the features of the model at each splitting point of an individual tree in the forest, thereby averaging away the feature variance. Numerous pathological and entomological vegetation studies have reported that SVM succeeded in modeling f VIs extracted from spectral bands [ 39 , 89 , 90 ], while at the same time the modeling performance of for the RF classifier was found to be stable and superlative with transformed spectral reflectance data [ 91 , 92 , 93 ].…”

Section: Discussionmentioning

confidence: 99%

Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

Nguyen

Sagan

Maimaitiyiming

et al. 2021

Sensors

133

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Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially detect and quantify viral diseases in a nondestructive manner. This study utilized hyperspectral imagery at the plant level to identify and classify grapevines inoculated with the newly discovered DNA virus grapevine vein-clearing virus (GVCV) at the early asymptomatic stages. An experiment was set up at a test site at South Farm Research Center, Columbia, MO, USA (38.92 N, −92.28 W), with two grapevine groups, namely healthy and GVCV-infected, while other conditions were controlled. Images of each vine were captured by a SPECIM IQ 400–1000 nm hyperspectral sensor (Oulu, Finland). Hyperspectral images were calibrated and preprocessed to retain only grapevine pixels. A statistical approach was employed to discriminate two reflectance spectra patterns between healthy and GVCV vines. Disease-centric vegetation indices (VIs) were established and explored in terms of their importance to the classification power. Pixel-wise (spectral features) classification was performed in parallel with image-wise (joint spatial–spectral features) classification within a framework involving deep learning architectures and traditional machine learning. The results showed that: (1) the discriminative wavelength regions included the 900–940 nm range in the near-infrared (NIR) region in vines 30 days after sowing (DAS) and the entire visual (VIS) region of 400–700 nm in vines 90 DAS; (2) the normalized pheophytization index (NPQI), fluorescence ratio index 1 (FRI1), plant senescence reflectance index (PSRI), anthocyanin index (AntGitelson), and water stress and canopy temperature (WSCT) measures were the most discriminative indices; (3) the support vector machine (SVM) was effective in VI-wise classification with smaller feature spaces, while the RF classifier performed better in pixel-wise and image-wise classification with larger feature spaces; and (4) the automated 3D convolutional neural network (3D-CNN) feature extractor provided promising results over the 2D convolutional neural network (2D-CNN) in learning features from hyperspectral data cubes with a limited number of samples.

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Section: Discussionmentioning

confidence: 99%

Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

Nguyen

Sagan

Maimaitiyiming

et al. 2021

Sensors

133

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“…This type of classification uses the Jenks optimization algorithm to classify attributes. It seeks to minimize the average deviation from the mean for each class while maximizing the deviation from the means of other classes-that is, to reduce the variance within classes and maximize the variance among classes [73,74].…”

Section: Data Analysis and Resultsmentioning

confidence: 99%

Placement of Infrastructure for Urban Electromobility: A Sustainable Approach

Machado

Takiya²,

Yamamura

et al. 2020

Sustainability

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Over the last few years, electric vehicles (EVs) have turned into viable urban transportation alternatives. Charging infrastructure is an issue, since high investment is needed and there is a lot of demand uncertainty. Seeking to fill gaps in past studies, this investigation proposes a set of procedures to identify the most adequate places for implementing the EV charging infrastructure. In order to identify the most favorable districts for the installation and operation of electric charging infrastructure in São Paulo city, the following public available information was considered: the density of points of interest (POIs), distribution of the average monthly per capita income, and number of daily trips made by transportation mode. The current electric vehicle charging network and most important business corridors were additionally taken into account. The investigation shows that districts with the largest demand for charging stations are located in the central area, where the population also exhibits the highest purchasing power. The charging station location process can be applied to other cities, and it is possible to use additional variables to measure social inequality.

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“…The observed family is a valuable source of new palm trees with higher oil yields and improved bunch characteristics. The work in [42] proposed Jenks natural breaks (JNB) for the classification of chlorophyll sufficiency levels and relative chlorophyll content. 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.…”

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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Hyperspectral remote sensing for assessment of chlorophyll sufficiency levels in mature oil palm (Elaeis guineensis) based on frond numbers: Analysis of decision tree and random forest

Cited by 61 publications

References 42 publications

Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

Placement of Infrastructure for Urban Electromobility: A Sustainable Approach

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

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