Forecasting Rice Status for a Food Crisis Early Warning System Based on Satellite Imagery and Cellular Automata in Malang, Indonesia

Sujarwo, Sujarwo; Putra, Aditya Nugraha; Setyawan, Raden Arief; Teixeira, Heitor Mancini; Khumairoh, Uma

doi:10.3390/su14158972

Cited by 6 publications

(4 citation statements)

References 42 publications

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“…The name of Cluster #6 was the artificial neural network (ANN). Without complete information and even any prior knowledge, ANN models can still identify nonlinear relationships and predict dependent response, and thus were applied in food image analysis, quality detection, food safety risk prediction, crop distribution, and yield prediction, and various thermal and non-thermal food-processing operations [ 173 , 177 , 179 , 182 , 188 , 191 , 197 ]. Geng et al (2017) introduced a predictive model based on AHP integrated extreme learning machine (ELM), rather than a traditional artificial neural network (ANN), to monitor the food safety system in China [ 193 ].…”

Section: Abstract and Hot Spotsmentioning

confidence: 99%

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Liu

Wang

Zhang

et al. 2023

Foods

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Artificial Intelligence (AI) technologies have been powerful solutions used to improve food yield, quality, and nutrition, increase safety and traceability while decreasing resource consumption, and eliminate food waste. Compared with several qualitative reviews on AI in food safety, we conducted an in-depth quantitative and systematic review based on the Core Collection database of WoS (Web of Science). To discover the historical trajectory and identify future trends, we analysed the literature concerning AI technologies in food safety from 2012 to 2022 by CiteSpace. In this review, we used bibliometric methods to describe the development of AI in food safety, including performance analysis, science mapping, and network analysis by CiteSpace. Among the 1855 selected articles, China and the United States contributed the most literature, and the Chinese Academy of Sciences released the largest number of relevant articles. Among all the journals in this field, PLoS ONE and Computers and Electronics in Agriculture ranked first and second in terms of annual publications and co-citation frequency. The present character, hot spots, and future research trends of AI technologies in food safety research were determined. Furthermore, based on our analyses, we provide researchers, practitioners, and policymakers with the big picture of research on AI in food safety across the whole process, from precision agriculture to precision nutrition, through 28 enlightening articles.

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Section: Abstract and Hot Spotsmentioning

confidence: 99%

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Liu

Wang

Zhang

et al. 2023

Foods

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show abstract

“…Therefore, the state is obliged to ensure that every citizen receives the right to food (Nasution, Lubis, & Syaifuddin, 2020). The problem of food security is a very important issue for countries with high population levels, such as Indonesia (Sujarwo, Putra, Setyawan, Teixeira, & Khumairoh, 2022). One of the most widely cultivated food commodities in Indonesia is paddy, which is the main source of rice for the majority of the population (Khusna & Mariana, 2021).…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Machine Learning Algorithms for Predicting Paddy Production

Aditya,

Munthe,

Sihombing

2024

SinkrOn

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For countries with large populations, such as Indonesia, food security is a very important issue. The majority of Indonesia's population depends on rice as their main food, and paddy is one of the most widely cultivated food commodities. The very good and accurate national paddy production prediction results really support decisions regarding national paddy production targets for the coming period. Therefore, to ensure supply and price stability, paddy availability must be predicted. Many studies have used machine learning to predict crop yields. By learning important patterns and relationships from input data, machine learning can combine the advantages of other methods to make better predictions of paddy yields. The aim of this research is to conduct a comparative analysis between three machine learning algorithms, namely, random forest, decision tree, and k-nearest neighbors, in predicting paddy production. To determine which algorithm is the best, a model evaluation is carried out using the coefficient of determination (R2-score), mean absolute error (MAE), and mean squared error (MSE). This research goes through methodological stages, starting from collecting datasets, data preprocessing, training and testing split datasets, applying algorithms, and evaluating the model. From this research, results were obtained for the random forest algorithm with an R2-score of 82.38%, MAE of 261726.20, and MSE of 2.19495E+11. For the decision tree, the R2-score was 79.62%, MAE was 323257.99, and MSE was 2.49304E+11. Meanwhile, k-nearest neighbors obtained an R2-score of 76.25%, MAE of 318433.42, and MSE of 2.90577E+11. The results of this research show that the random forest algorithm is the best for predicting paddy production because it obtains a larger R2-score as well as smaller MAE and MSE results.

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“…Despite being awarded by IRRI for "Acknowledgment for Achieving Agri-food System Resiliency and Rice Self-Sufficiency during 2019-2021 through the Application of Rice Innovation Technology", rice production is still a major concern in Indonesia. Rice production is challenged by a continuously growing population, land-use changes, and limited access to innovation, technologies, and resources (Sujarwo et al, 2022). A huge work is necessary to achieve the rice availability target of 46,84 million tons in 2024.…”

Section: Introductionmentioning

confidence: 99%

Application of RGB UAV images to identify spectral patterns and estimate rice production

Munibah,

Iskandar,

Barus

et al. 2024

J. Agron. Indonesia (Indonesian J. Agron.)

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Monitoring rice plant growth is crucial for evaluating rice field management and yield production. RGB images are generated from Unmanned Aerial Vehicles (UAV) with RGB cameras. UAVs produce high spatial and temporal resolution, while RGB cameras are commonly used and cheap. The objectives of this study were to identify the spectral pattern of rice plant growth and to estimate yield production based on the spectral value of RGB images. The spectral pattern and yield estimation were analyzed using confidence interval (CI) and regression, respectively. Results show that spectral pattern during the vegetative until ripening stage forms a concave with minimum value in the generative stage and decreases towards the harvest stage. Based on the CI value, the high interval between upper and lower happened in the vegetative and ripening stages while the low interval happened in the generative stage. The high CI in the vegetative and ripening stages was due to the soil background and complexity of the rice plant canopy, respectively while the low CI in the generative stage was due to the homogeneous response of the leaf canopy. The best rice yield estimation based on the spectral value occurs in the ripening stage with an R2 of 0.84. Keywords: chlorophyll content, confidence interval, drone images, rice plant, regression

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Forecasting Rice Status for a Food Crisis Early Warning System Based on Satellite Imagery and Cellular Automata in Malang, Indonesia

Cited by 6 publications

References 42 publications

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

A Comparative Analysis of Machine Learning Algorithms for Predicting Paddy Production

Application of RGB UAV images to identify spectral patterns and estimate rice production

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