2021
DOI: 10.3390/agronomy12010022
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Climate-Based Modeling and Prediction of Rice Gall Midge Populations Using Count Time Series and Machine Learning Approaches

Abstract: The Asian rice gall midge (Orseolia oryzae (Wood-Mason)) is a major insect pest in rice cultivation. Therefore, development of a reliable system for the timely prediction of this insect would be a valuable tool in pest management. In this study, occurring between the period from 2013–2018: (i) gall midge populations were recorded using a light trap with an incandescent bulb, and (ii) climatological parameters (air temperature, air relative humidity, rainfall and insulations) were measured at four intensive ric… Show more

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Cited by 12 publications
(7 citation statements)
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References 41 publications
(36 reference statements)
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“…By considering the MAPE values, a significant difference between the actual and forecasted values can be obtained by the DM test. The DM test was similarly used to compare the inter-combinational significance comparison between the models [45][46][47][48]. The results of the DM test revealed that in two sets (training and testing set) of data, the extreme learning machine intervention model performed better than all other models (Table 8).…”
Section: Discussionmentioning
confidence: 99%
“…By considering the MAPE values, a significant difference between the actual and forecasted values can be obtained by the DM test. The DM test was similarly used to compare the inter-combinational significance comparison between the models [45][46][47][48]. The results of the DM test revealed that in two sets (training and testing set) of data, the extreme learning machine intervention model performed better than all other models (Table 8).…”
Section: Discussionmentioning
confidence: 99%
“…The third article published in the presented SI also deals with rice cultivation, but the research topic presented is related to forecasting the occurrence of the main pest in this crop-Asian rice gall midge (Orseolia oryzae (Wood-Mason)) [9]. A six-year study on the existence of this pathogen was conducted in rice plantations in four different agroecosystems in India.…”
Section: Papers In This Special Issuementioning
confidence: 99%
“…This study provides insight into the potential use of the AtACR2 gene as a genetic tool for effective arsenic phytoremediation. In contrast, the accumulation of As in rice grains is still one of the major environmental concerns, as rice is a staple food for most people [ 90 , 91 ]. In the study of [ 92 ], As transporter OsPht1:8 and Lsi1/2 mutations in rice reduced the accumulation of As in rice plants and reduced its accumulation in rice grains.…”
Section: Biotechnological Strategies For Improvement Of Phytoremediat...mentioning
confidence: 99%