Oil Palm Yield Forecasting Based on Weather Variables Using Artificial Neural Network

Kartika, Nadia Dwi; Astika, I Wayan; Santosa, Edi

doi:10.11591/ijeecs.v3.i3.pp626-633

Cited by 16 publications

(16 citation statements)

References 11 publications

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“…Temperature and water turbidity are other environmental factors that affect palm oil production. Five environmental parameters facilitate the oil palm stress tolerance [56]: rainfall, temperature, relative humidity, light intensity, and wind speed. The inefficient production of palm oil may occur in low humidity levels, especially in the dry seasons when the watering holes and rivers have dried up.…”

Section: Factors Affecting Palm Oil Growth and Qualitymentioning

confidence: 99%

A Review of an Artificial Intelligence Framework for Identifying the Most Effective Palm Oil Prediction

et al. 2022

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Machine Learning (ML) offers new precision technologies with intelligent algorithms and robust computation. This technology benefits various agricultural industries, such as the palm oil sector, which possesses one of the most sustainable industries worldwide. Hence, an in-depth analysis was conducted, which is derived from previous research on ML utilisation in the palm oil in-dustry. The study provided a brief overview of widely used features and prediction algorithms and critically analysed current the state of ML-based palm oil prediction. This analysis is extended to the ML application in the palm oil industry and a comparison of related studies. The analysis was predicated on thoroughly examining the advantages and disadvantages of ML-based palm oil prediction and the proper identification of current and future agricultural industry challenges. Potential solutions for palm oil prediction were added to this list. Artificial intelligence and ma-chine vision were used to develop intelligent systems, revolutionising the palm oil industry. Overall, this article provided a framework for future research in the palm oil agricultural industry by highlighting the importance of ML.

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Section: Factors Affecting Palm Oil Growth and Qualitymentioning

confidence: 99%

A Review of an Artificial Intelligence Framework for Identifying the Most Effective Palm Oil Prediction

et al. 2022

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“…The higher spatial resolution of UAV images can acquire more detailed data about objects on the ground, which makes renders many techniques less applicable for traditional satellite imaging. [144] utilized only one type of feature to predict palm oil yield. Crop yield prediction model using a large number of feature set could be more accurate than the model with a small group of features.…”

Section: ) Effect Of Climate Change In Oil Palm Cultivationmentioning

confidence: 99%

A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction

et al. 2021

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“…In which training and testing performed via several classifier such as k-means neighbor, radial basis function, artificial neural networks and support vector machine. SVM has been also found to be very promising to achieve efficient classification of leaf disease [15], [16].…”

Section: Classificationmentioning

confidence: 99%

Classification of Leaf Disease from Image Processing Technique

Kamal¹,

Masazhar²,

Rahman³

2018

IJEECS

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<p class="Abstract">Disease in palm oil sector is one of the major concerns because it affects the production and economy losses to Malaysia. Diseases appear as spots on the leaf and if not treated on time, cause the growth of the palm oil tree. This work presents the use of digital image processing technique for classification oil palm leaf disease sympthoms. Chimaera and Anthracnose is the most common symtoms infected the oil palm leaf in nursery stage. Here, support vector machine (SVM) acts as a classifier where there are four stages involved. The stages are image acquisition, image enhancement, clustering and classification. The classification shows that SVM achieves accuracy of 97% for Chimaera and 95% for Anthracnose.</p>

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Oil Palm Yield Forecasting Based on Weather Variables Using Artificial Neural Network

Abstract: Forecasting of oil palm yield has become a main factor in the management

Cited by 16 publications

References 11 publications

A Review of an Artificial Intelligence Framework for Identifying the Most Effective Palm Oil Prediction

A Review of an Artificial Intelligence Framework for Identifying the Most Effective Palm Oil Prediction

A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction

Classification of Leaf Disease from Image Processing Technique

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