Expert systems in oil palm precision agriculture: A decade systematic review

Tan, Xiao Jian; Cheor, Wai Loon; Yeo, Kwok Shien; Leow, W.Z.

doi:10.1016/j.jksuci.2022.02.006

Cited by 18 publications

(12 citation statements)

References 126 publications

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“…those listed in [23][24][25]). Additionally, precision agriculture approaches can use interactive, computer-oriented technological systems to gather site-specific data and/or modeling data, which can be used to monitor, analyze, or predict the effectiveness of different agricultural management interventions [26]. These technologies, specifically remote sensing, are increasingly being used to gather data on existing oil palm plantations, including information on geographic distribution and estimated yield, or to detect potential sites suitable for future conversion to oil palm [27].…”

Section: Potential For More Sustainable Management Of Oil Palmmentioning

confidence: 99%

“…Our stepwise approach included a search of multiple databases and the bibliographies of key publications, removal of duplicate publications, evaluation of relevance at the title and abstract level, and extraction of meta-data at the full text level. A similar mapping method has recently been applied successfully by Tan et al, who used this approach to review studies on the use of expert systems in oil palm management [26].…”

Section: Systematic Mapping Approachmentioning

confidence: 99%

“…We chose to focus on changing environmental management practices and environmental outcomes within the cultivation stage of palm oil production, as this has substantial potential to increase environmental sustainability of the crop [6,[9][10][11][12][13]. This complements a recent systematic map of research studies on precision agriculture and expert systems in oil palm plantations completed by Tan et al, which focused on strategies for collecting data within plantations [26]. To develop our understanding further, it would be valuable to consider sustainability interventions and outcomes at other stages of the palm oil production process.…”

Section: Limitations and Priorities For Future Assessmentmentioning

confidence: 99%

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A systematic map of within-plantation oil palm management practices reveals a rapidly growing but patchy evidence base

Popkin

Reiss-Woolever

Turner

et al. 2022

PLOS Sustain Transform

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Although oil palm expansion has had severe environmental impacts, oil palm also has the highest yield per hectare of any vegetable oil crop. Compared to many other crops, it has the potential to support high complexity habitats, with minimal chemical input, and relatively high levels of biodiversity and ecosystem functioning. However, there has been little synthesis of available research on oil palm management strategies to support delivery of more sustainable cultivation. In this paper, we provide a systematic map compiling all available evidence assessing within-plantation oil palm management practices at the cultivation stage, with a focus on practices that affect biodiversity and environmental processes. Using approaches adapted from systematic review protocols, we catalogued oil palm management publications to provide details of geographic location, year, interventions tested (i.e. agricultural practices), targeted outcomes of interventions, co-occurrences between different interventions and outcomes (including multiple outcomes), and study design. Most studies were conducted in Southeast Asia, with fewer studies conducted in South America or Africa. Twenty-six interventions were observed in the literature, across six categories: soil, understory, within-crop, landscape-level, replanting, and mixed/multiple interventions. The most common interventions tested were landscape-scale interventions, such as maintaining forest fragments/buffer zones, whereas interventions involved in replanting were the least researched. Eight outcomes were considered: soil fertility, soil erosion, water quality and availability, pest control, replanting, maintenance of biodiversity and areas of high conservation value, and reducing air pollution and greenhouse gas emissions. Studies researching biodiversity were the most common, whereas comparatively few studies considered replanting and reducing emissions. Most primary studies were observational, with experimental studies being rarer, especially in biodiversity research. We match our findings to the Roundtable on Sustainable Palm Oil’s environmental sustainability criteria to illustrate how policy-makers and producers may use our map to access evidence supporting cultivation-stage oil palm sustainability management. This study provides valuable information to inform best management practices and direction for necessary future research.

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Section: Potential For More Sustainable Management Of Oil Palmmentioning

confidence: 99%

Section: Systematic Mapping Approachmentioning

confidence: 99%

Section: Limitations and Priorities For Future Assessmentmentioning

confidence: 99%

See 1 more Smart Citation

A systematic map of within-plantation oil palm management practices reveals a rapidly growing but patchy evidence base

Popkin

Reiss-Woolever

Turner

et al. 2022

PLOS Sustain Transform

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“…Machine learning (ML) is increasingly and widely employed in agricultural yield prediction due to the combination of massive volumes of data acquired from several sources by predictors and its capacity to analyse and produce the most reliable predictions (Chlingaryan et al 2018;Liakos et al 2018). Recently, several researchers suggested that deep learning can provide valuable information to analyse oil palm yield prediction (Rashid et al 2021;Tan et al 2022). ML can disengage the effects of co-linear relevant variables and analyse non-linear relationship predictors and response variables, which generally surpassed traditional linear regression (Aghighi et al 2018;Phan et al 2021;Bouras et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Oil Palm Yield Prediction Across Blocks Using Multi-Source Data and Machine Learning

Ang

Shafri

Lee³

et al. 2022

Preprint

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Predicting yields on a bigger scale in a timely and accurate manner is essential for preventing climate risk and ensuring food security, particularly in the light of climate change and the escalation of extreme climatic events. Furthermore, crop yield estimates are affected by various factors including weather, nutrients and management practices. In this study, integrating multi-source data (i.e. satellite-derived vegetation indices (VIs), satellite-derived climatic variables (i.e. land surface temperature (LST) and rainfall precipitation, weather station and field-surveys), we built one multiple linear regression (MLR), three machine learnings (XGBoost, support vector regression, and random forest) and one deep learning (deep neural network) model to predict oil palm yield at block-level within the oil palm plantation. Moreover, time-series moving average and backward elimination feature selection techniques were implemented at the pre-processing stage. The yield prediction models were developed and tested using MLR, XGBoost, support vector regression (SVR), random forest (RF) and deep neural network (DNN) algorithms. Their model performances were then compared using evaluation metrics and generated the final spatial prediction map based on the best performance. DNN achieved the best model performances for both selected (R2=0.91; RMSE= 2.92 tonnes per ha; MAE= 2.56 tonnes per ha and MAPE= 0.09) and full predictors (R2=0.76; RMSE of 3.03 tonnes per ha; MAE of 2.88 tonnes per ha; MAPE of 0.10 tonnes per ha). In addition, advanced ensemble ML techniques such as XGBoost may be utilised as a supplementary for oil palm yield prediction at the block level. Among them, MLR recorded the lowest performance. By using backward elimination to identify the most significant predictors, the performance of all models was improved by 5% - 26% for R2, and that decreased by 3% - 31% for RMSE, 7% - 34% for MAE, and 1% - 15% for MAPE, respectively. DNN generates the most accurate statistical metrics, with an increase of around 15% for R2, 11% for RMSE, 32% for MAE and 1% for MAPE. Our study successfully developed efficient, effective and accurate yield prediction models for timely predicting oil palm yield over a large area by integrating data from multiple sources. These can be potentially handled by plantation management to estimate oil palm yields to speed up the decision-making process for sustainable production.

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“…To date, in various fruit maturity grading and identification systems, deep learning-based computer vision has become a substitute for subjective experience, destructive testing, and non-invasive testing, being objective, consistent, efficient, and economical [ 32 , 33 ]. Deep learning-based computer vision has also been used for the identification of the maturity of different oil fruits, including oil palm [ 34 , 35 , 36 ], coconut [ 37 ], and olive [ 38 ]. Khosravi et al [ 39 ] designed a deep convolutional neural network (CNN) in a study that identified the maturity of on-branch olives for Zard and Roghani cultivars.…”

Section: Introductionmentioning

confidence: 99%

Maturity Grading and Identification of Camellia oleifera Fruit Based on Unsupervised Image Clustering

Zhu

Shen

Wang

et al. 2022

Foods

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Maturity grading and identification of Camellia oleifera are prerequisites to determining proper harvest maturity windows and safeguarding the yield and quality of Camellia oil. One problem in Camellia oleifera production and research is the worldwide confusion regarding the grading and identification of Camellia oleifera fruit maturity. To solve this problem, a Camellia oleifera fruit maturity grading and identification model based on the unsupervised image clustering model DeepCluster has been developed in the current study. The proposed model includes the following two branches: a maturity grading branch and a maturity identification branch. The proposed model jointly learns the parameters of the maturity grading branch and maturity identification branch and used the maturity clustering assigned from the maturity grading branch as pseudo-labels to update the parameters of the maturity identification branch. The maturity grading experiment was conducted using a training set consisting of 160 Camellia oleifera fruit samples and 2628 Camellia oleifera fruit digital images collected using a smartphone. The proposed model for grading Camellia oleifera fruit samples and images in training set into the following three maturity levels: unripe (47 samples and 883 images), ripe (62 samples and 1005 images), and overripe (51 samples and 740 images). Results suggest that there was a significant difference among the maturity stages graded by the proposed method with respect to seed oil content, seed soluble protein content, seed soluble sugar content, seed starch content, dry seed weight, and moisture content. The maturity identification experiment was conducted using a testing set consisting of 160 Camellia oleifera fruit digital images (50 unripe, 60 ripe, and 50 overripe) collected using a smartphone. According to the results, the overall accuracy of maturity identification for Camellia oleifera fruit was 91.25%. Moreover, a Gradient-weighted Class Activation Mapping (Grad-CAM) visualization analysis reveals that the peel regions, crack regions, and seed regions were the critical regions for Camellia oleifera fruit maturity identification. Our results corroborate a maturity grading and identification application of unsupervised image clustering techniques and are supported by additional physical and quality properties of maturity. The current findings may facilitate the harvesting process of Camellia oleifera fruits, which is especially critical for the improvement of Camellia oil production and quality.

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Expert systems in oil palm precision agriculture: A decade systematic review

Cited by 18 publications

References 126 publications

A systematic map of within-plantation oil palm management practices reveals a rapidly growing but patchy evidence base

A systematic map of within-plantation oil palm management practices reveals a rapidly growing but patchy evidence base

Oil Palm Yield Prediction Across Blocks Using Multi-Source Data and Machine Learning

Maturity Grading and Identification of Camellia oleifera Fruit Based on Unsupervised Image Clustering

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