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

Classification of aggressive behaviour in pigs by activity index and multilayer feed forward neural network

2014 ◽  
Vol 119 ◽  
pp. 89-97 ◽  
Author(s):  
Maciej Oczak ◽  
Stefano Viazzi ◽  
Gunel Ismayilova ◽  
Lilia T. Sonoda ◽  
Nancy Roulston ◽  
...  
Keyword(s):  
Neural Network ◽  
Aggressive Behaviour ◽  
Activity Index ◽  
Feed Forward Neural Network ◽  
Feed Forward

A feed forward neural network for classification of bull's-eye myocardial perfusion images

1995 ◽  
Vol 22 (2) ◽  
pp. 108-115 ◽  
Author(s):  
David Hamilton ◽  
Peter J. Riley ◽  
Ueber J. Miola ◽  
Ahmed A. Amro
Keyword(s):  
Neural Network ◽  
Myocardial Perfusion ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Bull's Eye

scholarly journals Modeling of Speech Recognition Using Artificial Neural Network

2019 ◽  
Vol 8 (9) ◽  
pp. 1225-1229
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Pattern Matching ◽  
Back Propagation ◽  
Experimental Result ◽  
Communication Link ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
The Voice

Automatic speech recognition has attained a lot of significance as it can act as easy communication link between machines and humans. This mode of communication is easy for man to use as it is effortless and easy. Many approaches for extraction of the features of the speech and classification of speech have been considered. This paper unveils the importance of neutral network and the way it can be used for recognition of speech. Mel Frequency Cepstrum Coefficients is made use of for extraction of the features from the voice. For pattern matching neural network has been used. MATLAB has been used to show how the speech is recognized. In this paper the speech recognition has been done firstly by multilayer feed forward neural network using Back propagation algorithm. Then the process of speech recognition is shown by using Radial basis function neural network. The paper then analyzes the performance of both the algorithms and experimental result shows that BPNN outperforms the RBFNN.

scholarly journals Classification of 41 Hand and Wrist Movements via Surface Electromyogram Using Deep Neural Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Panyawut Sri-iesaranusorn ◽  
Attawit Chaiyaroj ◽  
Chatchai Buekban ◽  
Songphon Dumnin ◽  
Ronachai Pongthornseri ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Low Cost ◽  
Sampling Rate ◽  
Assessment Procedure ◽  
Semg Signal ◽  
Neural Network Approach ◽  
Assessment Protocol ◽  
Wide Range

Surface electromyography (sEMG) is a non-invasive and straightforward way to allow the user to actively control the prosthesis. However, results reported by previous studies on using sEMG for hand and wrist movement classification vary by a large margin, due to several factors including but not limited to the number of classes and the acquisition protocol. The objective of this paper is to investigate the deep neural network approach on the classification of 41 hand and wrist movements based on the sEMG signal. The proposed models were trained and evaluated using the publicly available database from the Ninapro project, one of the largest public sEMG databases for advanced hand myoelectric prosthetics. Two datasets, DB5 with a low-cost 16 channels and 200 Hz sampling rate setup and DB7 with 12 channels and 2 kHz sampling rate setup, were used for this study. Our approach achieved an overall accuracy of 93.87 ± 1.49 and 91.69 ± 4.68% with a balanced accuracy of 84.00 ± 3.40 and 84.66 ± 4.78% for DB5 and DB7, respectively. We also observed a performance gain when considering only a subset of the movements, namely the six main hand movements based on six prehensile patterns from the Southampton Hand Assessment Procedure (SHAP), a clinically validated hand functional assessment protocol. Classification on only the SHAP movements in DB5 attained an overall accuracy of 98.82 ± 0.58% with a balanced accuracy of 94.48 ± 2.55%. With the same set of movements, our model also achieved an overall accuracy of 99.00% with a balanced accuracy of 91.27% on data from one of the amputee participants in DB7. These results suggest that with more data on the amputee subjects, our proposal could be a promising approach for controlling versatile prosthetic hands with a wide range of predefined hand and wrist movements.

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.

2A2-U05 Development of Low-cost Patient "MIMAMORT" Device : Classification of patient's state based on a depth image using a deep neural network

2015 ◽  
Vol 2015 (0) ◽  
pp. _2A2-U05_1-_2A2-U05_3
Author(s):  
Hiroaki MAEGAWA ◽  
Takayuki Nakamura ◽  
Etsuko UEDA ◽  
Atsutoshi IKEDA ◽  
Tsukasa OGASAWARA
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Low Cost ◽  
Depth Image ◽  
Device Classification
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