scholarly journals First Report of Cercospora concors Causing Cercospora Leaf Blotch of Potato in Inner Mongolia, North China

Plant Disease ◽  
2008 ◽  
Vol 92 (4) ◽  
pp. 654-654
Author(s):  
S. M. Tian ◽  
P. Ma ◽  
D. Q. Liu ◽  
M. Q. Zou
Keyword(s):  
Late Blight ◽  
Inner Mongolia ◽  
Early Blight ◽  
North China ◽  
Eastern Africa ◽  
Potato Leaf ◽  
American Phytopathological Society ◽  
Leaf Blotch ◽  
First Report ◽  
Potato Diseases

Cercospora leaf blotch disease of potato (Solanum tuberosum L.) caused by Cercospora concors (Casp.) Sacc (synonym Mycovellosiella concors (Casp.) Deighton) occurs worldwide but mainly has been reported in the cool and temperate climates of Europe, Asia, North America, and eastern Africa. Cercospora leaf blotch is usually a minor disease and may go unnoticed since it commonly occurs simultaneously with other potato leaf diseases such as late blight (caused by Phytophthora infestans) and early blight (caused by Alternaria solani) (2). Symptoms of Cercospora leaf blotch first appear on lower leaves as small, yellowish green, irregular blotches and later may appear on middle and upper leaves. As the leaves expand, the blotches enlarge and become purplish brown or black. Conidiophores and conidia form on the underside of the lesions, giving the lesions a mildewed appearance similar to late blight. Necrotic lesions are distinguished from those caused by the early blight pathogen A. solani by the lack of concentric rings (1). In more severe epidemics of Cercospora leaf blotch, potato leaves may be killed, stem lesions become dark and entire plants die, but no resulting yield loss from the disease has been documented. Potato tubers are not infected. From August to September of 2005, yellow-brown lesions appeared on the upper side of potato leaves (cv. Zihuabai, certified virus free) and gray mildew developed on the underside of leaves in potato field trials conducted in Jining County, 41°N, 113°E of Inner Mongolia, North China. The infections were observed mostly on lower and middle leaves of plants; 20 to 30% of plants were infected. In the laboratory, the mildew was scraped with a sterile scalpel and examined microscopically. The conidiophores were irregular in width, grayish, and highly branched. The conidia were numerous, light to dark, straight or slightly bent, cylindrical or obclavate, with conspicuous scars, and zero to six septa. The mature spores were from 16 to 59 μm long and 4 to 6 μm wide. The teleomorph of the fungus was not found. On the basis of the morphological characters, the causal agent was identified as C. concors. C. concors has been previously identified from potato leaves in the Engshi District of Hubei Province, China (3), but to our knowledge, this is the first report of the fungus causing Cercospora leaf blotch of potato in Inner Mongolia, North China. References: (1) G. D. Franc and B. I. Christ. Page 22 in: Compendium of Potato Diseases. 2nd ed. W. R. Stevenson et al., eds. American Phytopathological Society, St. Paul, MN, 2001. (2) E. R. French. Page 19 in: Compendium of Potato Diseases. 2nd ed. W. R. Stevenson et al., eds. American Phytopathological Society, St. Paul, MN, 2001. (3) S. M. Tian et al. China Potato J. 1:13, 1997.

scholarly journals Early Prediction of Potato Leaf Diseases Using ANN Classifier

2021 ◽  
Vol 13 (0203) ◽  
pp. 129-134
Author(s):  
Kumar Sanjeev ◽  
Narendra Kumar Gupta ◽  
W. Jeberson Jeberson ◽  
Suneeta Paswan
Keyword(s):  
Neural Network ◽  
Late Blight ◽  
Early Blight ◽  
Potato Crop ◽  
Low Cost ◽  
Feed Forward Neural Network ◽  
Potato Leaf ◽  
Major Loss ◽  
Ann Classifier

Potatoes are cultivated in several states of India. Potatoes provides a low-cost energy in human diet. Potatoes are used in industry for making dried food products. Early blight and Late blight are major disease of potato leaf. It is estimated that the major loss occurred in potato yield due to these diseases. In this research, we have collected sample of potato leaf images from Plant Village dataset. This dataset contains 2152 images of potato leaf. It has 3 class of sample of Healthy Leaf, Early Blight and Late Blight. The 76 features are extracted from these images regarding color, texture and area. The extracted features are used to develop a classifier. The developed classifier is based on neural network for prediction and classification of potato image samples. The Feed Forward Neural Network (FFNN) Model is used for prediction and classification of unknown leaf. The accuracy of model is achieved 96.5%. Classifier is helpful in early and accurate prediction of the leaf diseases of potato crop.

scholarly journals Deteksi Penyakit Pada Daun Kentang Menggunakan Pengolahan Citra dengan Metode Convolutional Neural Network

2021 ◽  
Vol 8 (1) ◽  
pp. 22
Author(s):  
Abdul Jalil Rozaqi ◽  
Andi Sunyoto ◽  
M rudyanto Arief
Keyword(s):  
Neural Network ◽  
Late Blight ◽  
Convolutional Neural Network ◽  
Early Blight ◽  
Batch Size ◽  
Crop Failure ◽  
Potato Leaf ◽  
Identification Process ◽  
Potato Plants ◽  
Leaf Disease

Produk pertanian kentang menjadi sangat penting karena termasuk makanan utama bagi manusia. Kentang memiliki kandungan karbohidrat yang menjadikanya sebagai makanan utama. Dalam mengelola pertanian kentang ini tentu memiliki beberapa kendala diantaranya adalah penyakit yang menyerang pada daun kentang yang jika dibiarkan akan menghasilkan produksi yang buruk atau bahkan gagal panen. Late blight dan early blight adalah penyakit yang sering ditemui pada daun kentang. Penyakit ini memiliki gejala masing-masing sehingga para petani dapat melakukan pencegahan jika melihat gejala pada daun kentang, tetapi langkah ini memliki kelemahan yaitu proses identifikasi yang lama, dan jika penanganan pada penyakit daun ini sangat lambat akan mengakibatkan penambahan biaya perawatan. Dengan memanfaatkan teknologi yaitu berupa pengolahan citra digital maka hal ini bisa diatasi, jadi pada penelitian ini akan mengusulkan metode yang tepat dalam mendeteksi penyakit pada daun kentang. Klasifikasi akan dilakukan dengan tiga kelas berupa daun sehat, early blight, dan late blight menggunakan metode Deep Learning mengguanakan arsitektur Convolutional Neural Network (CNN). Hasil pada peneltian ini dianggap baik karena pada epoch ke 10 dengan batch size 20 menghasilkan training akurasi 95% dan validation accuracy 94%.Kata Kunci—Penyakit daun kentang, late blight, early blight, identifikasi, CNNPotato agricultural products are essential because they are the leading food. Potatoes have carbohydrate content, which makes them the leading food for humans. But in carrying out this potato farming certainly has several obstacles, including the disease that attacks the potato leaves which if left unchecked will result in poor production or even crop failure. late blight and early blight are diseases that are often found in potato leaves. This disease has its own symptoms so that farmers can take precautions if they see symptoms on potato leaves, but this step has a weakness that is a long identification process, and if the handling of this leaf disease is very slow will result in additional maintenance costs. By utilizing technology in the form of digital image processing, this can be overcome, so this research will propose an appropriate method in detecting diseases in the leaves of potato plants. Classification will be carried out with three classes in the form of healthy leaves, early blight, and late blight using the Convolutional Neural Network (CNN) algorithm. The results of this research are considered good because on the 10th epoch with batch size 20 produces 95% accuracy training and 94% validation accuracy.Keywords—Potato leaf disease, late blight, early blight, identification, CNN

scholarly journals Inventory of the potato diseases and impact on growth and yield traits in far North Cameroon

2020 ◽  
Vol 14 (8) ◽  
pp. 2826-2836
Author(s):  
Jules Patrice Ngoh Dooh ◽  
Frederic Ulrich Boydoul ◽  
Abdoul Madjerembe ◽  
Dany Brice Tchoupou Tsouala ◽  
Djile Bouba Hawaou Adagoro ◽  
...  
Keyword(s):  
Solanum Tuberosum ◽  
Late Blight ◽  
Solanum Tuberosum L ◽  
Leaf Roll ◽  
Morphological Characteristics ◽  
Growth And Yield ◽  
Potato Leaf ◽  
Potato Diseases ◽  
Far North ◽  
Number Of Stems

Potato (Solanum tuberosum L) production in the Far North Region of Cameroon is faced with scarcity or inequality of rains and with diseases that affect yields. To improve the production a study was conducted in the production area of Mogode subdivision with the objective of identifying potato diseases and pathogens agents. The experimental design was in complete randomized blocks. The plant material used was a local variety of potato (Dosa). Diseases and pathogens have been identified on a base of symptoms and morphological characteristics. The incidence, severity and rainfall were assessed. Yield, number of stems and diameter of the tubers were evaluated. The diseases identified are fungal (late blight, Alternaria or early blight and Fusarium wilt), viral (Virosis M, Rust stain and Potato leaf roll (PLR) and bacterial (Bacterial wilt and Common scab). Conidia of Phytophthora infestans, and Alternaria spp. were found.Whatever the site and disease, the incidence has remained below 25%. Late blight was more present in the Gouria site, Alternaria and virosis M more present in the Mouvou site. The yield was roughly the same at the two sites, around 3 t/ha. The highest number of stems was obtained at Gouria, 18 ± 0.75. The potato is attacked by several diseases. The results of this study represent an important baseline data for the implementation of integrated disease management in Cameroon.Keywords: Solanum tuberosum, diseases, pathogens, incidence, severity, yield.

The Late Permian highly fractionated I-type granites from Sishijia pluton in southestern Inner Mongolia, North China: A post-collisional magmatism record and its implication for the closure of Paleo-Asian Ocean

Lithos ◽  
2019 ◽  
Vol 328-329 ◽  
pp. 262-275 ◽  
Author(s):  
Xiang-Dong Liao ◽  
Song Sun ◽  
Huan-Zhao Chi ◽  
Ding-Yu Jia ◽  
Ze-Yu Nan ◽  
...  
Keyword(s):  
Inner Mongolia ◽  
North China ◽  
Late Permian

Integrated Exploration Model for Concealed Ore Deposit: A Case Study from Shuitou Fluorite Deposit, Inner Mongolia, North China

2021 ◽  
Vol 32 (2) ◽  
pp. 370-389
Author(s):  
Liang Wang ◽  
Shouting Zhang ◽  
Yi Fang ◽  
Li Tang
Keyword(s):  
Inner Mongolia ◽  
North China ◽  
Ore Deposit

scholarly journals Re-Discussion on the Metallogenic Age of the Hadamengou Gold Deposit in Inner Mongolia, China

2016 ◽  
Vol 5 (2) ◽  
pp. 85
Author(s):  
Yu Zhang ◽  
Yangyang Chen
Keyword(s):  
Gold Deposit ◽  
Inner Mongolia ◽  
North China ◽  
Northern Margin ◽  
The North ◽  
Geological Evolution ◽  
Western Segment ◽  
Mineralization Age ◽  
Extensive Collection ◽  
Metallogenic Age

The Hadamengou gold deposit is located in the western segment of the northern margin of the North China Craton (NCC). The mineralization age of the Hadamengou gold deposit is a matter of controversy. Based on the extensive collection the results of previous research, we infer that the Hadamengou gold deposit is exposed to prolonged geological evolution. It was formed as early as the Middle Hercynian orogen. The metallization mainly took place in the Early Indosinian epoch.

scholarly journals First report ofAlternaria malicausing apple leaf blotch disease in Iran

2007 ◽  
Vol 2 (1) ◽  
pp. 57 ◽  
Author(s):  
Mohammad Javad Soleimani ◽  
Marzieh Esmailzadeh
Keyword(s):  
Leaf Blotch ◽  
First Report

SPRAYING TOMATOES FOR THE CONTROL OF LEAF SPOT, EARLY BLIGHT AND LATE BLIGHT

1957 ◽  
Vol 37 (4) ◽  
pp. 385-391
Author(s):  
K. M. Graham ◽  
A. G. Donaldson
Keyword(s):  
Late Blight ◽  
Leaf Spot ◽  
Early Blight ◽  
Tomato Leaf

Tomato leaf spot, early blight, and late blight were controlled effectively in the Ottawa district by six applications of the fixed copper COCS 55 or of Manzate (maneb). The split schedule consisting of three sprays with Manzate, followed by three sprays with COCS 55, or the tank mixture of these two fungicides, gave results comparable to those obtained with each of them alone. Some of the fungicides tested showed a degree of specificity in the control of certain diseases.

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