Potassium nutrition in oil palm: The potential of metabolomics as a tool for precision agriculture

The information on biophysical parameters—such as height, crown area, and vegetation indices such as the normalized difference vegetation index (NDVI) and normalized difference red edge index (NDRE)—are useful to monitor health conditions and the growth of oil palm trees in precision agriculture practices. The use of multispectral sensors mounted on unmanned aerial vehicles (UAV) provides high spatio-temporal resolution data to study plant health. However, the influence of UAV altitude when extracting biophysical parameters of oil palm from a multispectral sensor has not yet been well explored. Therefore, this study utilized the MicaSense RedEdge sensor mounted on a DJI Phantom–4 UAV platform for aerial photogrammetry. Three different close-range multispectral aerial images were acquired at a flight altitude of 20 m, 60 m, and 80 m above ground level (AGL) over the young oil palm plantation area in Malaysia. The images were processed using the structure from motion (SfM) technique in Pix4DMapper software and produced multispectral orthomosaic aerial images, digital surface model (DSM), and point clouds. Meanwhile, canopy height models (CHM) were generated by subtracting DSM and digital elevation models (DEM). Oil palm tree heights and crown projected area (CPA) were extracted from CHM and the orthomosaic. NDVI and NDRE were calculated using the red, red-edge, and near-infrared spectral bands of orthomosaic data. The accuracy of the extracted height and CPA were evaluated by assessing accuracy from a different altitude of UAV data with ground measured CPA and height. Correlations, root mean square deviation (RMSD), and central tendency were used to compare UAV extracted biophysical parameters with ground data. Based on our results, flying at an altitude of 60 m is the best and optimal flight altitude for estimating biophysical parameters followed by 80 m altitude. The 20 m UAV altitude showed a tendency of overestimation in biophysical parameters of young oil palm and is less consistent when extracting parameters among the others. The methodology and results are a step toward precision agriculture in the oil palm plantation area.

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POTASSIUM NUTRITION IN THE OIL PALM: A MOLECULAR PERSPECTIVE

Journal of Oil Palm Research ◽

10.21894/jopr.2019.0029 ◽

2020 ◽

Keyword(s):

Oil Palm ◽

Potassium Nutrition

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Expert System for Oil Palm Leaves Deficiency to Support Precision Agriculture

10.1109/icimtech53080.2021.9535083 ◽

2021 ◽

Author(s):

Dian Pratama Putra ◽

Prasanto Bimantio ◽

Teddy Suparyanto ◽

Bens Pardamean

Keyword(s):

Expert System ◽

Oil Palm ◽

Precision Agriculture

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Evolution of Precision Agriculture Computing towards Sustainable Oil Palm Industry

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v11.i2.pp725-732 ◽

2018 ◽

Vol 11 (2) ◽

pp. 725

Author(s):

K.C. Goh ◽

S.Y. Sim ◽

H.H. Goh ◽

K. Bilal ◽

T.H. Sam ◽

...

Keyword(s):

Oil Palm ◽

Precision Agriculture ◽

Environmental Sustainability ◽

System Development ◽

Geographical Information ◽

Phase System ◽

Development Life Cycle ◽

Global Positioning ◽

Oil Palm Industry ◽

Yield Monitoring

Precision technology elements have not been implemented yet into the sustainable oil palm industry because the knowledge and technology gap. To resolve the gaps, promote sustainability and integrate the technologies, Oil Palm Management System (OPAMS) was introduced. The precision technologies in OPAMS comprises of Geographical Information System (GIS), Global Positioning System (GPS), remote sensing and yield monitoring. A phase by phase System Development Life Cycle (SDLC) methodology was used to generate the said system with feedbacks from oil palm planters as the inputs for OPAMS’s key features. OPAMS ultimately aims to increase the awareness of the industry on the benefits of utilizing technology to improve plantation performances, increase business and environmental sustainability.

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Oto-BaCTM: An Automated Artificial Intelligence (AI) Detector and Counter for Bagworm (Lepidoptera: Psychidae) Census

Advances in Agricultural and Food Research Journal ◽

10.36877/aafrj.a0000218 ◽

2021 ◽

Author(s):

Mohd Najib Ahmad ◽

Abdul Rashid Mohamed Shariff ◽

Ishak Aris ◽

Izhal Abdul Halin ◽

Ramle Moslim

Keyword(s):

Deep Learning ◽

Oil Palm ◽

Precision Agriculture ◽

Insect Pest ◽

Peninsular Malaysia ◽

Training Dataset ◽

Detection Accuracy ◽

Processing Unit ◽

Image Processing Algorithm ◽

Human Errors

The bagworm species of Metisa plana, is one of the major species of leaf-eating insect pest that attack oil palm in Peninsular Malaysia. Without any treatment, this situation may cause 43% yield loss from a moderate attack. In 2020, the economic loss due to bagworm attacks was recorded at around RM 180 million. Based on this scenario, it is necessary to closely monitor the bagworm outbreak at infested areas. Accuracy and precise data collection is debatable, due to human errors. . Hence, the objective of this study is to design and develop a specific machine vision that incorporates an image processing algorithm according to its functional modes. In this regard, a device, the Automated Bagworm Counter or Oto-BaCTM is the first in the world to be developed with an embedded software that is based on the use of a graphic processing unit computation and a TensorFlow/Teano library setup for the trained dataset. The technology is based on the developed deep learning with Faster Regions with Convolutional Neural Networks technique towards real time object detection. The Oto-BaCTM uses an ordinary camera. By using self-developed deep learning algorithms, a motion-tracking and false colour analysis were applied to detect and count number of living and dead larvae and pupae population per frond, respectively, corresponding to three major groups or sizes classification. Initially, in the first trial, the Oto-BaCTM has resulted in low percentages of detection accuracy for the living and dead G1 larvae (47.0% & 71.7%), G2 larvae (39.1 & 50.0%) and G3 pupae (30.1% & 20.9%). After some improvements on the training dataset, the percentages increased in the next field trial, with amounts of 40.5% and 7.0% for the living and dead G1 larvae, 40.1% and 29.2% for the living and dead G2 larvae and 47.7% and 54.6% for the living and dead pupae. The development of the ground-based device is the pioneer in the oil palm industry, in which it reduces human errors when conducting census while promoting precision agriculture practice.

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Sustainability

10.5772/intechopen.100156 ◽

2021 ◽

Author(s):

Dickson Osei Darkwah ◽

Meilina Ong-Abdullah

Keyword(s):

Oil Palm ◽

Precision Agriculture ◽

Large Scale ◽

Biodiversity Loss ◽

Scale Production ◽

Oil Crops ◽

Sustainability Strategies ◽

Large Scale Production ◽

Agriculture Sustainability ◽

Negative Impacts

The oil palm (Elaies guineensis Jacq) is the largest produced and highly traded vegetable oil globally yet has the lowest cost of production and significantly higher productivity compared to other oil crops. The crop has the potential of alleviating poverty for smallholders and lifting the economies of countries with large scale production notably, Malaysia and Indonesia and currently on high demand for use as biofuel feedstock. Irrespective of these advantages of the oil palm, there is a global concern on the devastating impact of the crop on the environment and ecosystem during plantation developments and expansions. Deforestation, biodiversity loss, water and air pollution and toxic compounds from palm oil mill effluents (POME) are some of the negative impacts of the oil palm. For the industry to be more beneficial and impactful globally, sustainability strategies becomes urgent need. Sustainability strategies such as increasing the yield of oil palm, precision agriculture, sustainability certification, support for smallholders and circular economy have been put across to curtail the negative impacts of oil palm expansion.

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Fundamental Research on Unmanned Aerial Vehicles to Support Precision Agriculture in Oil Palm Plantations

Agricultural Robots - Fundamentals and Applications ◽

10.5772/intechopen.80936 ◽

2019 ◽

Cited By ~ 1

Author(s):

Redmond Ramin Shamshiri ◽

Ibrahim A. Hameed ◽

Siva K. Balasundram ◽

Desa Ahmad ◽

Cornelia Weltzien ◽

...

Keyword(s):

Unmanned Aerial Vehicles ◽

Oil Palm ◽

Precision Agriculture ◽

Aerial Vehicles ◽

Fundamental Research

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DEVELOPMENT OF RAPID LOW-COST LARS PLATFORM FOR OIL PALM PLANTATION

Jurnal Teknologi ◽

10.11113/jt.v77.6563 ◽

2015 ◽

Vol 77 (20) ◽

Cited By ~ 1

Author(s):

Nasruddin Abu Sari ◽

A Ahmad ◽

MY Abu Sari ◽

S Sahib ◽

AW Rasib

Keyword(s):

Remote Sensing ◽

Oil Palm ◽

Precision Agriculture ◽

Low Cost ◽

Digital Cameras ◽

Large Area ◽

Oil Palm Plantation ◽

Personal Remote Sensing ◽

Path Distance ◽

Low Altitude

The need to produce high temporal remote sensing imagery for supporting precision agriculture in oil palm deserves a new low-altitude remote sensing (LARS) technique. Consumer over the shelf unmanned aerial vehicles (UAV) and digital cameras have the potential to serve as Personal Remote Sensing Toolkits which are low-cost, efficient, rapid and safe. The objectives of this study were to develop and test a new technique to rapidly capturing nadir images of large area oil palm plantation (1 km2 ~ 4 km2). Using 5 different multi-rotor UAV models several imagery missions were carried out. Multi-rotors were chosen as a platform due to its vertical take-off and landing (VTOL) feature. Multi-rotor’s VTOL was crucial for imagery mission success. Post processing results showed that for an area of 1 km2, it needs 2 to 6 sorties of quad-rotor UAV with 4000x3000 pixel digital cameras flying at altitude of 120m above ground level and an average of 50m cross-path distance. The results provide a suitability assessment of low-cost digital aerial imagery acquisition system. The study has successfully developed a decent workhorse quad-rotor UAV for Rapid Aerial Photogrammetry Imagery and Delivery (RAPID) in oil palm terrain. Finally we proposed the workhorse UAV as Low-Altitude Personal Remote Sensing (LAPERS) basic founding element.

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Precision agriculture in oil palm plantations: diagnostic tools for sustainable N and K nutrient supply

OCL ◽

10.1051/ocl/2019001 ◽

2019 ◽

Vol 26 ◽

pp. 5

Author(s):

Bernard Dubos ◽

Victor Baron ◽

Xavier Bonneau ◽

Olivier Dassou ◽

Albert Flori ◽

...

Keyword(s):

Oil Palm ◽

Precision Agriculture ◽

Nutrient Balance ◽

Diagnostic Tools ◽

Soil Mineral ◽

Potential Yield ◽

Fertilizer Rates ◽

Sufficient Precision ◽

Simple Indicator

Predicting the fertilizer requirements of an oil palm plantation has long been a difficult task. Two main methods have emerged. Leaf analyses (LA) were used for fertilization management as early as the 1950s. Leaf contents are compared to optimum references, making it possible to adjust the fertilizer rates applied in each block. Another approach, based on the nutrient balance (NB), is to evaluate and replace nutrients that are exported from the field, or immobilized by the plant. Plantations must adopt environmentally friendly practices; in particular, fertilizer inputs must be estimated with sufficient precision to achieve the highest possible yields, without applying excessive amounts of nutrients in relation to plant demand and the storage capacity of soils. We questioned the relevance of each method for achieving these objectives. We did so using some long-term fertilization trials to compare the optimum N and K rates recommended by each method in the adult phase. It appeared that LA led to moderate rates compared to NB. It also appeared that calculating a precise nutrient balance on a field scale was hampered by a lack of precise information (i) about the biomasses produced and their composition and (ii) about the highly variable outputs of the environmental losses. On the other hand, LA provided a simple indicator of the ability for each block to achieve its potential yield. We believe that this perfectible method is more protective of the environment, without the risk of a significant decrease in yields or a decrease in soil mineral reserves.

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UNMANNED AERIAL VEHICLE (UAV) DERIVED NORMALISED DIFFERENCE VEGETATION INDEX (NDVI) AND CROWN PROJECTION AREA (CPA) TO DETECT HEALTH CONDITIONS OF YOUNG OIL PALM TREES FOR PRECISION AGRICULTURE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w16-611-2019 ◽

2019 ◽

Vol XLII-4/W16 ◽

pp. 611-614

Author(s):

S. A. Suab ◽

M. S. Syukur ◽

R. Avtar ◽

A. Korom

Keyword(s):

Oil Palm ◽

Precision Agriculture ◽

Vegetation Index ◽

Point Clouds ◽

Health Conditions ◽

Projection Area ◽

Biophysical Parameters ◽

Palm Trees ◽

Normalised Difference Vegetation Index ◽

Oil Palms

Abstract. Malaysia currently is one of the biggest global producers and exporters of palm oil. The world’s expanding oil palm plantation areas contribute to climate change and in-return, climate is change also affecting the health of oil palms through a range of abiotic and biotic stresses. Current advancements in Precision Agriculture research using UAV gives an advantage to detect the health conditions of oil palm at early stages. Thus, remedial actions can be taken to prolong the life and increase oil palms productivity. This paper explores the use of UAV derived NDVI and CPA of young oil palm to detect the health conditions. NDVI of individual oil palm were extracted using ground masking layer from the dense point clouds and visual on-screen manual editing was done for removing trees other than oil palm in ENVI software. The classified individual crown NDVI were then processed to extract the mean NDVI also conversion to vector to obtain the individual crown outline. Extracted mean NDVI was classified into un-healthy and healthy trees while the CPA was classified into small, medium and big size classes. These classes of NDVI and CPA were analysed using GIS overlay method thus revealing the spatial patterns of individual oil palm trees and its health conditions. Overall, the majority of oil palm trees of the study area are healthy but average performing. However, few oil palm trees detected having health problems which has low NDVI and small CPA. This study demonstrates that biophysical parameters such as the CPA can be used to detect individual young oil palm trees health conditions and problems when combined with vegetation indices such as NDVI.

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