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Fauziah, Wahyu K
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Agriculture, Biological Sciences & Forestry

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Optimalisasi Citra Termal dalam Pertanian Presisi untuk Deteksi Dini Masalah Kesehatan Bibit Kelapa Sawit Melidawati, Melidawati; Sofi’i, Imam; Fauziah, Wahyu K
JURNAL BUDIDAYA PERTANIAN Vol 19 No 2 (2023): Jurnal Budidaya Pertanian
Publisher : Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/jbdp.2023.19.2.106

Abstract

The palm oil industry plays a pivotal role in various sectors, including food and bioenergy. The success of oil palm cultivation relies heavily on the health of the plant's seedlings. Early detection of diseases and stress is crucial to prevent a decline in crop yields and financial losses. Infrared Thermography technology has been widely employed across various fields for non-destructive monitoring, including agriculture. This research focuses on optimizing the use of thermal imaging in precision agriculture to early detect health issues in oil palm seedlings. Thermal imaging enables the precise measurement of plant surface temperatures, facilitating the identification of plant health issues without the need for further visual intervention. This study has the potential to transform approaches to proactively monitoring and managing the health of oil palm plants. Infrared thermography technology is utilized to observe temperature distribution patterns in oil palm seedlings. The objective is to explore the correlation between thermal characteristics and potential health issues or symptoms in these seedlings. Samples used in the research involve Tenera variety oil palm seedlings aged between 5-9 months, a critical growth phase. The study employs the UNI-T UTi120 Mobile thermal camera capable of measuring temperatures ranging from -20°C to 400°C. Subsequently, thermal image processing is conducted to identify thermal characteristics that could serve as indicators of health issues in oil palm seedlings. Statistical analysis is then performed to test significant differences in thermal characteristics between healthy and unhealthy plant samples. The analysis results reveal significant temperature variations between healthy and unhealthy portions of the plant seedlings, with a significance value of 0,025. These findings can serve as a basis for identifying temperature changes as potential early indicators of health issues in oil palm seedlings. This provides a foundation for developing more effective precision agriculture approaches within the palm oil industry.
Optimasi Deteksi Dini Masalah Kesehatan Bibit Kelapa Sawit dengan JST MLP Berbasis Citra Termal Fauziah, Wahyu K; Sofi’i, Imam; Melidawati, Melidawati
JURNAL BUDIDAYA PERTANIAN Vol 19 No 2 (2023): Jurnal Budidaya Pertanian
Publisher : Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/jbdp.2023.19.2.111

Abstract

Palm oil has an important role in the palm oil industry, but health problems in the seeds threaten production results. This research advocates an innovative approach by combining thermal imaging technology and artificial intelligence, especially Multilayer Perceptron Artificial Neural Networks (MLP ANN), for early detection of health problems in oil palm seedlings. The use of thermal cameras makes it easier to measure the temperature of plants and the surrounding environment. Thermal image analysis helps in evaluating thermal characteristics, especially plant temperature, which may be associated with health problems. Temperature data is classified into normal plants and plants affected by health problems, using statistical analysis to strengthen the relationship. A predictive model using MLP ANN was formulated to correlate thermal characteristics with the health condition of oil palm seedlings. The research results show that this model has high validity, with R2 of 0.933 for calibration and 0.930 for validation. In essence, this method accurately predicts the health condition of oil palm seedlings based on thermal images. This approach has the potential to provide early detection of plant health problems quickly, accurately, and efficiently. Through the application of this method, it is hoped that it can reduce losses due to health problems in oil palm seedlings, thereby making a major contribution to increasing productivity and welfare in the palm oil industry.
Co-Authors Melidawati, Melidawati Sofi’i, Imam