scholarly journals Studies on the varietal resistance of bacterial leaf blight of rice plant (1) Relations of the infections bacterial concentrations and their multiplication upon both susceptible and resistant varieties of rice plants

1956 ◽  
Vol 2 ◽  
pp. 110-111
Author(s):  
T. MIZUKAMI ◽  
M. SEKI
Keyword(s):  
Rice Plant ◽  
Leaf Blight ◽  
Bacterial Leaf Blight ◽  
Varietal Resistance ◽  
Rice Plants ◽  
Resistant Varieties ◽  
Bacterial Concentrations

scholarly journals KETAHANAN DUA PULUH SATU VARIETAS PADI TERHADAP PENYAKIT HAWAR DAUN BAKTERI

2009 ◽  
Vol 9 (2) ◽  
pp. 168-173
Author(s):  
Heru Adi Djatmiko ◽  
Fatichin Fatichin
Keyword(s):  
Incubation Period ◽  
Seed Weight ◽  
Block Design ◽  
Leaf Blight ◽  
Bacterial Leaf Blight ◽  
Resistant Variety ◽  
Rice Varieties ◽  
Environment Friendly ◽  
Rice Plants ◽  
Resistant Varieties

Resistance of twentyone rice varieties to Bacterial Leaf Blight.  Bacterial leaf blight is one of the most important diseases of rice plants.  Resistant Variety is one of safe, effective, and environment friendly alternative controls to suppress the bacterial leaf blight on rice.  The objectives of this research were to find the most resistant varieties against bacterial leaf blight, and to study the yield of inoculated rice varieties. The research was carried out experimentally. This experiment was arranged in Randomized completely block design with 22 treatments and three replicates. Varieties of IR64 as control for susceptible varieties. Observed Variables were incubation period, disease intensity, seed weight per panicle, and seed weight per hills.  The result of this research showed that variety IR 70 was the most resistant variety to bacterial leaf blight. Variety having highest yield was Rojolele with seed weight per hill was 31.17 g.

scholarly journals Detection of Rice Plants Diseases Using Convolutional Neural Network (CNN)

2020 ◽  
Vol 3 ◽  
pp. 393-396
Author(s):  
Achmad Ramadhanna’il Rasjava ◽  
Aditya Wisnugraha Sugiyarto ◽  
Yori Kurniasari ◽  
Syaifullah Yusuf Ramadhan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
Rice Plant ◽  
Leaf Blight ◽  
Rice Production ◽  
Training Data ◽  
Brown Spot ◽  
Bacterial Leaf Blight ◽  
Rice Plants

As a rice-producing plant, rice plant (Oryza sativa L.) is one of the most important crops in Indonesia. Rice production is increasing every year along with an increase in rice demand and population.The amount of rice production is affected by the condition of the rice plants. The worse the condition of rice plants, the rice production will also lower. Rice plant is very susceptible to diseases or pests that can reduce its productivity, including brown spot disease, leaf smut and bacterial leaf blight. As the development of science and technology, currently known as Artificial Intelligence. Artificial intelligence is a combination of several scientific disciplines such as mathematics, statistics, computer science, and even social science. Using artificial intelligence, the system now have the ability to interpret external data correctly to learn from the data and then use the learning to achieve certain goals through flexible adaptation. The artificial intelligence fields consists of several branches, such as machine learning and deep learning. Neural Network (NN) is one of the methods used in the deep learning.NN has many types, one of which is the Convolutional Neural Network (CNN). CNN is the best-knownmethod used for processingimages data compared to other types of NN. Therefore, in this study the identification of rice plants diseases was carriedout using CNN method. From this study,better results were obtained compared to other methods, obtaining 100% accuracy for training data and 86,67% for testing data. The model obtained by the CNN method can be used for detecting 3 different types of rice plants diseases, there are brown spots, leaf smuts, or bacterial leaf blight disease based on the physical images of rice plant leaves.

scholarly journals On forecasting of bacterial leaf blight of rice plants by using natural phage

1974 ◽  
Vol 20 ◽  
pp. 62-65
Author(s):  
Norisato GAMO ◽  
Takayuki YAMAGUCHI ◽  
Sadao KIMURA
Keyword(s):  
Leaf Blight ◽  
Bacterial Leaf Blight ◽  
Rice Plants ◽  
Natural Phage

scholarly journals Pendugaan Intensitas Serangan Penyakit BLB (Bacterial Leaf Blight) pada Tanaman Padi melalui Pendekatan Citra Multispektral

2020 ◽  
Vol 8 (2) ◽  
pp. 338
Author(s):  
Gusti Bagus Eka Chandra ◽  
I Made Anom S. Wijaya ◽  
Yohanes Setiyo
Keyword(s):  
Technology Development ◽  
Leaf Blight ◽  
Multispectral Image ◽  
Bacterial Leaf Blight ◽  
Estimation Accuracy ◽  
Multispectral Images ◽  
Multispectral Imagery ◽  
Rice Plants ◽  
Red Edge ◽  
Disease Attack

ABSTRAK Penyakit Bacterial Leaf Blight (BLB) merupakan salah satu penyakit yang berbahaya bagi tanaman padi. Penyakit ini bisa menyerang di setiap fase pertumbuhan. Perhitungan intensitas serangan penyakit BLB saat ini masih dilakukan secara manual. Diperlukan pengembangan teknologi dalam pendugaan intensitas serangan penyakit BLB melalui citra multispektral. Penelitian ini bertujuan untuk (1) untuk mendapatkan nilai korelasi terbaik antara intensitas serangan penyakit BLB dengan parameter citra multispektral (2) Untuk mendapatkan persamaan pendugaan intensitas serangan penyakit BLB berdasarkan pendekatan citra multispektral. Drone DJI Inspire 1 dengan kamera multispektral digunakan untuk menangkap gambar petak padi. Pengolahan data citra multispektral menggunakan Agisoft Photoscan dan software QGIS 3.8. Berdasarkan dari hasil akuisisi, citra multispektral menghasilkan citra band red, NIR, green, red edge, RGB yang kemudian diolah menjadi transformasi citra NDVI, EVI, dan NDRE. Dari ketiga parameter citra multispektral, nilai NDVI memiliki tingkat korelasi yang lebih kuat dengan koefisien determinasi sebesar 97,5% dan menghasilkan persamaan linier sebagai berikut y = -419,6 + 169,3. Dalam perhitungan nilai eror parameter NDVI memilikinilai eror paling rendah dibandingkan parameter EVI dan NDRE yaitu sebesar 4,64% dengan akurasi pendugaan 95,36%. Citra multispektral dapat digunakan dalam pendugaan intensitas serangan penyakit BLB pada tanaman padi karena menghasilkan nilai korelasi yang sangat kuat, dan akurasi pendugaan yang tinggi dengan nilai eror yang rendah tidak melebihi 10%. ABSTRACT  Bacterial Leaf Blight (BLB) is a disease that is dangerous for rice plants. This disease can attack in every phase of growth. Calculation of BLB disease attack intensity is currently still used manually method. Technology development is needed in estimating the intensity of BLB disease through multispectral imagery. This study aims (1) to get the best correlation value between the intensity of BLB disease attack with multispectral image parameters (2) to get the equation for estimating the intensity of BLB based on multispectral images parameter. Drone DJI Inspire 1 with a multispectral camera is used to captured the paddy field. The captured images was processed using Agisoft Photoscan and QGIS 3.8 software. Based on the results of the acquisition, multispectral images produce red, NIR, green, red edge, RGB band images which were then transformed into NDVI, EVI, and NDRE images. Of the three multispectral image parameters, NDVI values ??have a stronger correlation level with a determination coefficient of 97.5% and produce the following linear equation y = -419.6 + 169.3. In calculating the NDVI parameter error value has the lowest error value compared to the EVI and NDRE parameters which is 4.64% with an accuracy estimate of 95.36%. Multispectral imagery can be used in estimating the intensity of BLB disease attacks in rice plants because it produces a very strong correlation value, and high estimation accuracy with a low error value does not exceed 10%.

scholarly journals An effective antibiotic active against bacterial leaf blight of rice plant.

1979 ◽  
Vol 45 (2) ◽  
pp. 192-200 ◽  
Author(s):  
Michiaki IWATA ◽  
Yukio SUZUKI ◽  
Yasumitsu KONDO ◽  
Takeo INOHARA ◽  
Tetsuro WATANABE ◽  
...  
Keyword(s):  
Rice Plant ◽  
Leaf Blight ◽  
Bacterial Leaf Blight

scholarly journals Phenazines as Disinfectants against Bacterial Leaf Blight of the Rice Plant

1966 ◽  
Vol 14 (3) ◽  
pp. 365-367 ◽  
Author(s):  
Makoto Oda ◽  
Yasuharu Sekizawa ◽  
Tetsuro Watanabe
Keyword(s):  
Rice Plant ◽  
Leaf Blight ◽  
Bacterial Leaf Blight
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