Expert System for Oil Palm Leaves Deficiency to Support Precision Agriculture

2019 ◽

Vol 1 ◽

pp. 444

Author(s):

Dimas Satria ◽

Poningsih Poningsih ◽

Widodo Saputra

Keyword(s):

Expert System ◽

Programming Languages ◽

Palm Oil ◽

Oil Palm ◽

Oil Production ◽

Plant Diseases ◽

Accurate Information ◽

Forward Chaining ◽

Web Programming ◽

Level Of Confidence

The purpose of this paper is to create an expert system to detect oil palm plant diseases in order to help farmers / companies in providing accurate information about the diseases of oil palm plants and how to overcome them and to help reduce the risk of decreasing palm oil production. This system is designed to mimic the expertise of an expert who is able to detect diseases that attack oil palm plants. The method used is forward chaining that is starting from a set of data and proving a fact by describing the level of confidence and uncertainty found in a hypothesis. The results of this study are to diagnose diseases of oil palm plants and their computerization using web programming languages.

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Assessing the Influence of UAV Altitude on Extracted Biophysical Parameters of Young Oil Palm

Remote Sensing ◽

10.3390/rs12183030 ◽

2020 ◽

Vol 12 (18) ◽

pp. 3030

Author(s):

Ram Avtar ◽

Stanley Anak Suab ◽

Mohd Shahrizan Syukur ◽

Alexius Korom ◽

Deha Agus Umarhadi ◽

...

Keyword(s):

Oil Palm ◽

Precision Agriculture ◽

Point Clouds ◽

Aerial Images ◽

Surface Model ◽

Palm Tree ◽

Flight Altitude ◽

Biophysical Parameters ◽

Oil Palm Plantation ◽

Red Edge

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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Rancang Bangun Sistem Pakar Mendiagnosa Penyakit Tanaman Kelapa Sawit Menggunakan Metode Bayes Berbasis Android (Studi Kasus : Perkebunan PTPN 4 Air Batu)

JURNAL TEKNOLOGI INFORMASI ◽

10.36294/jurti.v2i1.403 ◽

2018 ◽

Vol 2 (1) ◽

pp. 15

Author(s):

Muhammad Dedi Irawan ◽

Muhammad Khairi Ikhsan Nasution

Keyword(s):

Expert System ◽

Palm Oil ◽

Oil Palm ◽

Good Method ◽

Training Data ◽

Cellular Phones ◽

Conditional Probabilities ◽

Bayes Method ◽

Palm Fruit ◽

Fruit Oil

Abstract - PT. Perkebunan Nusantara IV Air Batu (PTPN IV) is a government-owned oil palm plantation that is engaged in the production of palm fruit, oil palm plants will grow well and produce optimally if the plant is protected from disease. However, there is an imbalance where every year palm oil needs increase, while oil palm production decreases. This is due to lack of understanding of plantation assistants on the types of diseases found in oil palm plants which can cause continuous damage to oil palm plants. The Bayes method is one method that is suitable for selection, because the Bayes method is a good method in machine learning based on training data using conditional probabilities as the basis. With this expert system it is expected that plantation assistants can find out the type of disease and its solution quickly so that the problem of decreasing oil palm production can be overcome. The results of the research in the form of an expert system diagnose the disease of oil palm plants using the Android-based bayes method thus, this application can be used to analyze diseases using cellular phones. Keywords - Palm Oil Disease, Expert Systems, Bayes Method, Android.

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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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Precision Data Acquisition and Analysis for Nutrient Management of Tomatoes

Asian Journal of Computer Science and Technology ◽

10.51983/ajcst-2019.8.s2.2032 ◽

2019 ◽

Vol 8 (S2) ◽

pp. 20-23

Author(s):

G. Yogeswari ◽

A. Padmapriya

Keyword(s):

Expert System ◽

Data Collection ◽

Plant Growth ◽

Data Acquisition ◽

Precision Agriculture ◽

Nutrient Management ◽

Weather Conditions ◽

Precision Data ◽

Changing Climate ◽

Factors Influencing

Agriculture is the soul of every nation, where it considers some factors such as uneven rainfall, changing climate and weather conditions, monsoon for soil and nutrient during the crop growth. Agriculture is predominantly essential and the main source of our livelihood. Nutrient management is a major thirst area and to be the focus in the field of agriculture. Due to the paucity of nutrients in plants, the human is forced to face many challenges in day-to-day life. Restoration of nutrient is crucial, in this view there is need to espouse the precision agriculture system which alters crop related plan and policies. The main aim of this research is to collect some of the factors influencing nutrients in plant growth and analyze them. The data collection is done by both manual and precision methods. The plant chosen for the analysis is Tomato – a horticulture crop. This is an attempt towards developing an expert system based on precision data.

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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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