scholarly journals UJI COBA APLIKASI MODEL CLIMEX 1.1 UNTUK MENGANALISIS POTENSI PENYEBARAN HAMA WERENG BATANG COKLAT DAN PENGGEREK BATANG PADI PUTIHRUNNING TEST OF CLIMEX 1.1 TO ANALYZE POTENCY OF RICE PEST DISTRIBUTION OF BROWN PLANTHOPPER AND WHITE STEM BORER

Agromet ◽  
2004 ◽  
Vol 18 (2) ◽  
pp. 58
Author(s):  
Yonny Koesmaryono ◽  
Asmari Amasih ◽  
Wido Hanggoro ◽  
I. Impron
Keyword(s):  
Full Text ◽  
Brown Planthopper ◽  
Stem Borer ◽  
Rice Pest ◽  
Pdf Format

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scholarly journals Caterpillar-induced rice volatiles provide enemy-free space for the offspring of the brown planthopper

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Xiaoyun Hu ◽  
Shuangli Su ◽  
Qingsong Liu ◽  
Yaoyu Jiao ◽  
Yufa Peng ◽  
...  
Keyword(s):  
Control Strategies ◽  
Brown Planthopper ◽  
Stem Borer ◽  
Egg Parasitoid ◽  
Field Cage ◽  
Parasitism Rates ◽  
Enemy Free Space ◽  
Rice Pest ◽  
Herbivore Induced Plant Volatiles

Plants typically release large quantities of volatiles in response to herbivory by insects. This benefits the plants by, for instance, attracting the natural enemies of the herbivores. We show that the brown planthopper (BPH) has cleverly turned this around by exploiting herbivore-induced plant volatiles (HIPVs) that provide safe havens for its offspring. BPH females preferentially oviposit on rice plants already infested by the rice striped stem borer (SSB), which are avoided by the egg parasitoid Anagrus nilaparvatae, the most important natural enemy of BPH. Using synthetic versions of volatiles identified from plants infested by BPH and/or SSB, we demonstrate the role of HIPVs in these interactions. Moreover, greenhouse and field cage experiments confirm the adaptiveness of the BPH oviposition strategy, resulting in 80% lower parasitism rates of its eggs. Besides revealing a novel exploitation of HIPVs, these findings may lead to novel control strategies against an exceedingly important rice pest.

scholarly journals Genomes of the rice pest brown planthopper and its endosymbionts reveal complex complementary contributions for host adaptation

2014 ◽  
Vol 15 (12) ◽  
Author(s):  
Jian Xue ◽  
Xin Zhou ◽  
Chuan-Xi Zhang ◽  
Li-Li Yu ◽  
Hai-Wei Fan ◽  
...  
Keyword(s):  
Brown Planthopper ◽  
Host Adaptation ◽  
Rice Pest

scholarly journals Classification of Level of Rice Pest Incidence Based on Weather Information

2021 ◽  
pp. 32-43
Author(s):  
V. Jinubala ◽  
P. Jeyakumar
Keyword(s):  
Gall Midge ◽  
Weather Conditions ◽  
Stem Borer ◽  
Vital Role ◽  
Classification Model ◽  
Decision Tree Classifier ◽  
Weather Information ◽  
Tree Classifier ◽  
Rice Pest

Aims: To classify the rice pest data based on the weather attributes using a machine learning approach, a decision tree classifier, and to validate the performance results with other existing techniques through comparison. Design: Rice pest classification using C5.0 algorithm Methodology: We collected rice pest data from the crop fields of various regions in the state of Maharashtra of India. The dataset contains the name of the region (Taluk), period (week), pest data, temperature, rainfall, and relative humidity. The data is collected from 39 taluks within four districts in different weeks of the year of 2013-2014. The weather information plays a vital role in this rice pest data analysis, because based on the weather, pest infestation varies in all the regions. The pests considered in this research are Yellow Stem borer, Gall midge, Leaf folder, and Planthopper. The collected dataset is given as input to the classifier, where 75% of data from the dataset is used for training, and 25% of data are used for testing the classifier. Results: The proposed C5.0 algorithm performed better in the classification of rice pest dataset based on weather attributes. The C5.0 algorithm achieved 88.99% accuracy, 78.81% sensitivity, and 89.11% specificity, which are higher in performance when compared with other techniques. Compared with the other different methods, the C5.0 algorithm achieved 1.3 to 8.5% improved accuracy, 2.4 to 9% improved sensitivity, and 0.8 to 7.8% improved specificity. Conclusion: Early detection of pest and pest based diseases is an essential process to avoid major crop losses. The proposed classification model is designed to classify the level of pest infestations based on weather attributes, as level of infestations caused by the rice pest varies based on weather conditions. The C5.0 algorithm classified the rice pest data based on the weather attributes in the dataset.

scholarly journals Sistem Informasi Pendeteksi Hama Penyakit Tanaman Padi Menggunakan Metode Fuzzy Tsukamoto Berbasis Android

2018 ◽  
Author(s):  
Daniel Alfa Puryono
Keyword(s):  
Information Technology ◽  
Technological Change ◽  
Digital Images ◽  
Brown Planthopper ◽  
Stem Borer ◽  
The Internet ◽  
Rice Plants ◽  
Pests And Diseases ◽  
Plant Medicine ◽  
Pest Detection

Technological change is currently growing very rapidly in every year. With the existence of information technology makes people can easily to dig information through the internet world. Especially with regard to agriculture, such as our research is how to detect pests in rice plants. Pest detection is a process of pest analysis to be observed. While the pest of rice plants are brown planthopper, stem borer, green leafhoppers, grasshoppers, ground bunnies, grayak caterpillar. The method used for the application of pest detection applications is the fuzzy tsukamoto method because this method has the precision to detect pests through digital images. The process of this method by knowing the pattern and shape of various pests then calculated using the stages that exist in fuzzy tsukamoto. This android based application is designed to facilitate and can know the type of pest, pest form, pest weakness and time of pest attack on rice plants. So that with the application penguna can easily know the ways of controlling pests and diseases that attack rice plants. Although this application has not provided recommendations to some of the parties of plant medicine

Dynamic Prediction of Chilo suppressalis Occurrence in Rice Based on Deep Learning

Processes ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 2166
Author(s):  
Siqiao Tan ◽  
Yu Liang ◽  
Ruowen Zheng ◽  
Hongjie Yuan ◽  
Zhengbing Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Stem Borer ◽  
Hunan Province ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Chilo Suppressalis ◽  
Weather Factors ◽  
Dynamic Prediction ◽  
Rice Pest

(1) Background: The striped rice stem borer (SRSB), Chilo suppressalis, has severely diminished the yield and quality of rice in China. A timely and accurate prediction of the rice pest population can facilitate the designation of a pest control strategy. (2) Methods: In this study, we applied multiple linear regression (MLR), gradient boosting decision tree (GBDT), and deep auto-regressive (DeepAR) models in the dynamic prediction of the SRSB population occurrence during the crop season from 2000 to 2020 in Hunan province, China, by using weather factors and time series of related pests. (3) Results: This research demonstrated the potential of the deep learning method used in integrated pest management through the qualitative and quantitative evaluation of a reasonable validating dataset (the average coefficient of determination Rmean2 for the DeepAR, GBDT, and MLR models were 0.952, 0.500, and 0.166, respectively). (4) Conclusions: The DeepAR model with integrated ground-based meteorological variables, time series of related pests, and time features achieved the most accurate dynamic forecasting of the population occurrence quantity of SRSB as compared with MLR and GBDT.

scholarly journals Cooperative herbivory between two important pests of rice

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Qingsong Liu ◽  
Xiaoyun Hu ◽  
Shuangli Su ◽  
Yuese Ning ◽  
Yufa Peng ◽  
...  
Keyword(s):  
Host Plant ◽  
Proteinase Inhibitors ◽  
Brown Planthopper ◽  
Stem Borer ◽  
Host Plant Quality ◽  
Plant Insect Interactions ◽  
Rice Pests ◽  
Egg Parasitism ◽  
Insect Interactions ◽  
Parasitoid Attraction

AbstractNormally, when different species of herbivorous arthropods feed on the same plant this leads to fitness-reducing competition. We found this to be different for two of Asia’s most destructive rice pests, the brown planthopper and the rice striped stem borer. Both insects directly and indirectly benefit from jointly attacking the same host plant. Double infestation improved host plant quality, particularly for the stemborer because the planthopper fully suppresses caterpillar-induced production of proteinase inhibitors. It also reduced the risk of egg parasitism, due to diminished parasitoid attraction. Females of both pests have adapted their oviposition behaviour accordingly. Their strong preference for plants infested by the other species even overrides their avoidance of plants already attacked by conspecifics. This cooperation between herbivores is telling of adaptations resulting from the evolution of plant-insect interactions, and points out mechanistic vulnerabilities that can be targeted to control these major pests.

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