Bacterial Leaf Blight Detection in Rice Crops Using Ground-Based Spectroradiometer Data and Multi-temporal Satellites Images

Yudarwati, R.; Hongo, Chuki; Sigit, Gunardi; Barus, Baba; Utoyo, Budi

doi:10.5539/jas.v12n2p38

Cited by 9 publications

(8 citation statements)

References 26 publications

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“…Besides, high spatial resolution remote sensing data provide detailed information on crop nutrition and structure. The increase in the PSRI can well reflect the stress of the crop canopy [11], and the other three parameters can offer information on crop growth. However, the Landsat-8 lack a red-edge band.…”

Section: Discussionmentioning

confidence: 99%

“…For instance, Yudarwati et al analyzed multi-temporal satellite images to put forward a vegetation index suitable for monitoring rice bacterial blight. The results showed that three indexes, Normalized Difference Green/Red Index (NGRDI), Normalized Pigment Chlorophyll Index (NPCI), and Plant Senescence Reflectance Index (PSRI), had the highest correlation with the occurrence of bacterial blight, with an R 2 0.44, 0.63, and 0.67, respectively [11]. Such methods take into consideration the habitat or the growth conditions, respectively, during the most susceptible stages of the crop to the disease, which proved to be useful in disease prediction.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Remote Sensing Prediction for Wheat Fusarium Head Blight by Combining Host and Habitat Conditions

et al. 2020

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Remote sensing technology provides a feasible option for early prediction for wheat Fusarium head blight (FHB). This study presents a methodology for the dynamic prediction of this classic meteorological crop disease. Host and habitat conditions were comprehensively considered as inputs of the FHB prediction model, and the advantages, accuracy, and generalization ability of the model were evaluated. Firstly, multi-source satellite images were used to predict growth stages and to obtain remote sensing features, then weather features around the predicted stages were extracted. Then, with changes in the inputting features, the severity of FHB was dynamically predicted on February 18, March 6, April 23, and May 9, 2017. Compared to the results obtained by the Logistic model, the prediction with the Relevance Vector Machine performed better, with the overall accuracy on these four dates as 0.71, 0.78, 0.85, and 0.93, and with the area under the receiver operating characteristic curve as 0.66, 0.67, 0.72, and 0.75. Additionally, compared with the prediction with only one factor, the integration of multiple factors was more accurate. The results showed that when the date of the remote sensing features was closer to the heading or flowering stage, the prediction was more accurate, especially in severe areas. Though the habitat conditions were suitable for FHB, the infection can be inhibited when the host’s growth meets certain requirements.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Remote Sensing Prediction for Wheat Fusarium Head Blight by Combining Host and Habitat Conditions

et al. 2020

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“…Fewer studies on RFS have been based on near earth remote sensing. A large number of researchers have emphasized other crop diseases, such as satellite remote sensing for wheat Fusarium head blight [8], soybean sudden death syndrome [9], tobacco crop [10], rice bacterial leaf blight [11], soybean sudden death syndrome [12], near earth remote sensing for cucumber leaves in response to angular leaf spot disease [13], early disease in wheat fields [14], watermelon disease detection [15], rye leaf rust symptoms [16], paddy leaf disease [17], onion purple blotch [18], etc.…”

Section: Introductionmentioning

confidence: 99%

Rice False Smut Detection and Prescription Map Generation in a Complex Planting Environment, with Mixed Methods, Based on Near Earth Remote Sensing

Chen

Zhang

et al. 2022

Remote Sensing

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Rice false smut is known as the cancer of rice. The disease is becoming increasingly prominent and is one of the major diseases in rice. However, prevention and treatment of this disease relies on “Centralized pesticide spraying”. However, indiscriminate spraying leads to more pesticide residue, and impacts ecological and food safety. To obtain more objective results, different experimental planting forms are necessary. This study collected data at a complex planting environment based on “near earth remote sensing” using a frame-based hyperspectral device. We used mixed detection methods to differentiate between healthy rice and U. virens infected rice. There were 49 arrangements and more than 196 differentiation models between healthy and diseased rice, including 7 sowing data plots, 2 farm management types, and 23 pattern recognition methods. Finally, the real accuracy was mostly above 95%. In particular, with the increase of epoch and iteration, feature sequences based on deep learning could achieve better results; most of the accuracies were 100% with 100 epochs. We also found that differentiation accuracy was not necessarily correlated with the sowing dates and farm management. Finally, the detection method was verified according to the actual investigation results in the field. The prescription map of disease incidence was generated, which provided a theoretical basis for the follow-up precision plant protection work.

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“…Some approach to improve the damage assessment method were reported. According Yudarwati, et al, 2020 reported a method for detecting rice crop damage due to bacterial leaf blight (BLB) analyzed using a handheld spectroradiometer. On the other hand, Giamerti et al, 2021 evaluated multispectral imaging for assessing bacterial leaf blight damage for agriculture insurance purpose.…”

Section: Relation Between Disease Percentage With Grain Weight and St...mentioning

confidence: 99%

The Observation of Blast Disease and Its Effect to Rice Yield Using Existing Assessment Method to Support the Indonesian Agriculture Insurance

Sudiarta¹,

Prayoga²,

Temaja³

et al. 2021

SOCA

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One of the causes for the low productivity of rice in Indonesia is the occurrence of rice blast disease. Blast disease is one of the main diseases of rice plants around the world. Therefore to minimise the risk of the lost yield by blast disease, the Ministry of Agriculture of the Republic of Indonesia has the agriculture insurance program. To support these program, the research about observations of blast disease in a fixed location are needed. The objective this research is to confirm pathogens causing the blast disease in the fields, to determine the development of blast disease and its effect to the rice yield (grain weight and straw weight). The study was conducted in a fixed paddy field at Subak Uma Dalem, Sobangan Village, Mengwi District, Badung Regency, Bali Province from February 2019 to April 2019. The data showed that the disease incidence of rice blast increased every week starting from 1st week (28 DAT) until 10th week (104 ATP). Our study confirmed that the fungus caused the blast disease in the field was Magnaporthe oryzae. The higher incidence of blast disease in the field resulted reduced the rice yield by decreasing both grain and straw weights.

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Bacterial Leaf Blight Detection in Rice Crops Using Ground-Based Spectroradiometer Data and Multi-temporal Satellites Images

Cited by 9 publications

References 26 publications

Dynamic Remote Sensing Prediction for Wheat Fusarium Head Blight by Combining Host and Habitat Conditions

Dynamic Remote Sensing Prediction for Wheat Fusarium Head Blight by Combining Host and Habitat Conditions

Rice False Smut Detection and Prescription Map Generation in a Complex Planting Environment, with Mixed Methods, Based on Near Earth Remote Sensing

The Observation of Blast Disease and Its Effect to Rice Yield Using Existing Assessment Method to Support the Indonesian Agriculture Insurance

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