Pepper production is very important to Sarawak, especially to the rural community which supporting of about 67,000 rural pepper farmers. Currently, these farmers cultivate their farms using a conventional method that relies heavily on human labor and intuition. This method has proven to be time-consuming, a wastage of agriculture inputs, rely heavily on labor, and is non-proactive when it comes to pepper vines management. This paper is to study the use of smart farming by adopting digital agriculture using the internet of things (IoT) and unmanned aerial vehicles or drones (UAV) to monitor pepper vines farms, particularly on fertilisation management. The IoT and drone are used to collect environmental data such as soil humidity, air temperature, air humidity, and pH sensor, and drone are used to collect pepper vines’ health status in terms of Normalize Difference Vegetation Index (NDVI). The data was then analysed using machine learning to reveal the interrelation between the variables that underlie the pepper vines production. In our analysis, we implemented Pearson correlation to show the relationship among selected variables in the project. Linear Regression machine learning is used to predict the usage of fertiliser for 20 pepper vines. To support decision-making, data visualization in term of graphical reports were presented in the cloud database. Short messaging and emails were sent to farmers when the pepper vines required attention in regard to soil moisture, pH, and irrigation requirements. The result of this analysis can support farmers in taking appropriate decisions and actions about their pepper vines management in terms of fertilization and irrigation.
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20 November 2023
THE 5TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE IN INFORMATION SYSTEMS (CIIS 2022): Intelligent and Resilient Digital Innovations for Sustainable Living
17–19 October 2022
Brunei Darussalam
Research Article|
November 20 2023
Machine learning and internet of things for fertiliser prediction - Pepper vines
Anding Nyuak;
Anding Nyuak
a)
1
Universiti Malaysia Sarawak (UNIMAS)
, 94300 Kota Samarahan, Sarawak, MALAYSIA
a)Corresponding author: [email protected]
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Edwin Mit;
Edwin Mit
b)
1
Universiti Malaysia Sarawak (UNIMAS)
, 94300 Kota Samarahan, Sarawak, MALAYSIA
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Kedung Fletcher
Kedung Fletcher
c)
2
Politeknik Kuching Sarawak
, KM 22, Jalan Matang, 93050, Kuching, Sarawak, MALAYSIA
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AIP Conf. Proc. 2968, 050001 (2023)
Citation
Anding Nyuak, Edwin Mit, Kedung Fletcher; Machine learning and internet of things for fertiliser prediction - Pepper vines. AIP Conf. Proc. 20 November 2023; 2968 (1): 050001. https://doi.org/10.1063/5.0180304
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