Development of genetic algorithm for optimization of yield models in oil palm production

Hilal, Yousif Y.; Ishak, W. I. Wan; Yahya, Azmi; Ash’aari, Zulfa Hanan

doi:10.4067/s0718-58392018000200228

Cited by 14 publications

(12 citation statements)

References 16 publications

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“…Accuracy in oil palm breeding models is achieved through a hybrid method in this study. A model selection problem is presented because it transitions from traditional to modern model [147]. Rather than using inefficient and time-consuming traditional modelling methods to optimise crop yields, farmers are now turning to AI models that are more accurate and efficient.…”

Section: Analysis and Discussionmentioning

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: Analysis and Discussionmentioning

confidence: 99%

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

et al. 2022

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“…Furthermore, the researcher carried out tomato yield prediction [41], but only the RMSE metric was considered. In addition, a prediction of palm yield was proposed using genetic algorithm, but only R 2 and MSE were considered for evaluation [42]. Additionally, wheat and barley yield predictions were proposed using the CNN algorithm; however, only the MAPE metric was considered [43].…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Exploration of Machine Learning Approaches for Paddy Yield Prediction in Eastern Part of Tamilnadu

2021

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Agriculture is the principal basis of livelihood that acts as a mainstay of any country. There are several changes faced by the farmers due to various factors such as water shortage, undefined price owing to demand–supply, weather uncertainties, and inaccurate crop prediction. The prediction of crop yield, notably paddy yield, is an intricate assignment owing to its dependency on several factors such as crop genotype, environmental factors, management practices, and their interactions. Researchers are used to predicting the paddy yield using statistical approaches, but they failed to attain higher accuracy due to several factors. Therefore, machine learning methods such as support vector regression (SVR), general regression neural networks (GRNNs), radial basis functional neural networks (RBFNNs), and back-propagation neural networks (BPNNs) are demonstrated to predict the paddy yield accurately for the Cauvery Delta Zone (CDZ), which lies in the eastern part of Tamil Nadu, South India. The performance of each developed model is examined using assessment metrics such as coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), coefficient of variance (CV), and normalized mean squared error (NMSE). The observed results show that the GRNN algorithm delivers superior evaluation metrics such as R2, RMSE, MAE, MSE, MAPE, CV, and NSME values about 0.9863, 0.2295 and 0.1290, 0.0526, 1.3439, 0.0255, and 0.0136, respectively, which ensures accurate crop yield prediction compared with other methods. Finally, the performance of the GRNN model is compared with other available models from several studies in the literature, and it is found to be high while comparing the prediction accuracy using evaluation metrics.

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“…The average monthly temperature eight months prior to harvest of 27.83°C led to low FFB yield [42][43]. Wind speed is found to have an impact on oil palm cultivation [44]. The total sunshine hours is not the only site-specific factor for oil palm production [45].…”

Section: Introductionmentioning

confidence: 99%

“…In 2014, 2015, and 2016, the palm oil yield dropped by 0.3%, 1.9%, and 17%, respectively, to 3.84, 3.78, and 3.21 t ha -1 , compared to the previous year's record of 3.84, 3.78, and 3.21 t ha -1 . The decrease in palm oil yield has been attributed to a decrease in FFB yield in recent years [50][51].…”

Section: Introductionmentioning

confidence: 99%

Modeling the Influence of Certain Weather Parameters on Oil Palm Production in Peninsular Malaysia

Ahmed¹,

Ishak²,

Uddin³

et al. 2021

Preprint

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Oil palm is one of the most important crops in Malaysia. Lately, the production of oil palm has been reduced due to a variety of factors, including the weather and climate. Temperature, wind speed, relative humidity, sunshine, and rainfall distribution all have an impact on palm tree growth and development, which in turn has an impact on oil palm production. This paper aims to investigate the effects of some weather elements (temperature, wind speed, relative humidity, sunshine, and rainfall) on oil palm production in Peninsular Malaysia. Data were analyzed using the Statistical Package for Social Sciences (SPSS 20.0 version), with descriptive statistics, and multiple linear regression (MLR). The MLR model determined the strength of the relationship between oil palm yield (dependent variable) and the changing variables of temperature, sunshine, wind speed, relative humidity, and rainfall (independent variables). The findings revealed that temperature, wind speed, relative humidity, sunshine, and rainfall have a low impact on oil palm production and yield turnover. The R2 value of 0.202 shows that the independent variables explained only 20.2% of the fluctuation in palm oil production. The study recommends working within an integrated approach involving scientific research, planting, improving variety, improving regional academic leadership, and engaging private and public stakeholders, emphasized collaborative efforts with researchers in consumer countries, and strengthening the capacity of growers to best agroecological practices.

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Development of genetic algorithm for optimization of yield models in oil palm production

Cited by 14 publications

References 16 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

Exploration of Machine Learning Approaches for Paddy Yield Prediction in Eastern Part of Tamilnadu

Modeling the Influence of Certain Weather Parameters on Oil Palm Production in Peninsular Malaysia

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