Apple Leaf Disease Identification Based on Optimized Deep Neural Network

2021 ◽  
pp. 167-185
Author(s):  
Anitha Ruth J. ◽  
Uma R. ◽  
Meenakshi A.
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Monarch Butterfly ◽  
Economic Losses ◽  
Apple Trees ◽  
Leaf Disease ◽  
Disease Identification ◽  
Apple Leaves ◽  
The World ◽  
Apple Production

Apples are the most productive fruits in the world with a lot of medicinal and nutritional value. Significant economic losses occur frequently due to various diseases that occur on a huge scale of apple production. Consequently, the effective and timely discovery of apple leaf infection becomes compulsory. The proposed work uses optimal deep neural network for effectively identifying the diseases of apple trees. This work utilizes a convolution neural network to capture the features of Apple leaves. Extracted features are optimized with the help of the optimization algorithm. The optimized features are utilized in the leaf disease identification process. Here the traditional DNN algorithm is modified by means of weight optimization using adaptive monarch butterfly optimization (AMBO) algorithm. The experimental results show that the proposed disease identification methodology based on the optimized deep neural network accomplishes an overall accuracy of 98.42%.

scholarly journals Incidence and Molecular Identification of Apple Necrotic Mosaic Virus (ApNMV) in Southwest China

Plants ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 415
Author(s):  
Wensen Shi ◽  
Rundong Yao ◽  
Runze Sunwu ◽  
Kui Huang ◽  
Zhibin Liu ◽  
...  
Keyword(s):  
Mosaic Virus ◽  
Southwest China ◽  
Great Influence ◽  
Mosaic Disease ◽  
Pcr Analysis ◽  
Rt Pcr ◽  
Apple Trees ◽  
Apple Leaves ◽  
Prunus Necrotic Ringspot Virus ◽  
Apple Production

Apple mosaic disease has a great influence on apple production. In this study, an investigation into the incidence of apple mosaic disease in southwest China was performed, and the pathogen associated with the disease was detected. The results show that 2869 apple trees with mosaic disease were found in the Sichuan, Yunnan, and Guizhou Provinces, with an average incidence of 9.6%. Although apple mosaic virus (ApMV) is widespread in apples worldwide, the diseased samples were negative when tested for ApMV. However, a novel ilarvirus (apple necrotic mosaic virus, ApNMV) was identified in mosaic apple leaves which tested negative for ApMV. RT-PCR analysis indicated that ApNMV was detected in 322 out of 357 samples with mosaic symptoms. Phylogenetic analysis of coat protein (CP) sequences of ApNMV isolates suggested that, compared with ApMV, ApNMV was closer to prunus necrotic ringspot virus (PNRSV). The CP sequences of the isolates showed the diversity of ApNMV, which may enable the virus to adapt to the changeable environments. In addition, the pathology of mosaic disease was observed by microscope, and the result showed that the arrangement of the tissue and the shape of the cell, including the organelle, were seriously destroyed or drastically changed.

scholarly journals Fake News Detection using Deep Learning

2020 ◽  
Vol 9 (9) ◽  
pp. 226-228
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Deep Neural Network ◽  
Negative Impact ◽  
Research Area ◽  
Fake News ◽  
The World

News is a routine in everyone's life. It helps in enhancing the knowledge on what happens around the world. Fake news is a fictional information madeup with the intension to delude and hence the knowledge acquired becomes of no use. As fake news spreads extensively it has a negative impact in the society and so fake news detection has become an emerging research area. The paper deals with a solution to fake news detection using the methods, deep learning and Natural Language Processing. The dataset is trained using deep neural network. The dataset needs to be well formatted before given to the network which is made possible using the technique of Natural Language Processing and thus predicts whether a news is fake or not.

scholarly journals Monitoring Population Phenology of Asian Citrus Psyllid Using Deep Learning

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Maria Bibi ◽  
Muhammad Kashif Hanif ◽  
Muhammad Umer Sarwar ◽  
Muhammad Irfan Khan ◽  
Shouket Zaman Khan ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Maximum Temperature ◽  
Economic Losses ◽  
Asian Citrus Psyllid ◽  
Average Maximum ◽  
Average Minimum Temperature ◽  
Network Approaches

Asian citrus psyllid, Diaphorina citri Kuwayama (Liviidae: Hemiptera) is a menacing and notorious pest of citrus plants. It vectors a phloem vessel-dwelling bacterium Candidatus Liberibacter asiaticus, which is a causative pathogen of the serious citrus disease known as Huanglongbing. Huanglongbing disease is a major bottleneck in the export of citrus fruits from Pakistan. It is being responsible for huge citrus economic losses globally. In the current study, several prediction models were developed based on regression algorithms of machine learning to monitor different phenological stages of Asian citrus psyllid to predict its population about different abiotic variables (average maximum temperature, average minimum temperature, average weekly temperature, average weekly relative humidity, and average weekly rainfall) and biotic variable (host plant phenological patterns) in citrus-growing regions of Pakistan. The pest prediction models can be used for proper applications of pesticides only when needed for reducing the environmental and cost impacts of pesticides. Pearson’s correlation analysis was performed to find the relationship between different predictor (abiotic and biotic) variables and pest infestation rate on citrus plants. Multiple linear regression, random forest regressor, and deep neural network approaches were compared to predict population dynamics of Asian citrus psyllid. In comparison with other regression techniques, a deep neural network-based prediction model resulted in the least root mean squared error values while predicting egg, nymph, and adult populations.

scholarly journals Analysis of the state and global trends in the cultivation and breeding of apple trees

2021 ◽  
pp. 76-85
Author(s):  
E. V. Atazhanova ◽  
L. A. Lukicheva
Keyword(s):  
The State ◽  
Federal State ◽  
World Production ◽  
Apple Trees ◽  
Technical Literature ◽  
Global Trends ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
The World ◽  
Apple Production

The objective of the research is to analyze scientific and technical literature, to identify trends in world production and trends in apple breeding. The article uses official data from the FAO - Food and Agriculture Organization of the United Nations (Statistics Division). In addition, the information from the websites of the Federal State Statistics Service and the Federal State Budgetary Institution "Gossortcommission" - the State Commission for Selection Achievements, Test and Protection - were used. Statistical reports for the period from 2000 to 2019 were taken for analysis. The volume of fruits produced in the world is increasing every year. Apple production is in third place in the world, second only to coffee and olives. The leading apple suppliers are China, USA, Turkey, Poland, Iran, Italy, India, France, Russia, Chile. In 2000-2019 the gross harvest of apples increased from 59 million tons. up to 87 million tons, while the number of cultivated areas decreased from 5.4 to 4.7 million hectares. The main producing regions of this crop are Asia (60.7%), Europe (22.1%), America (12.9%), Africa (3.2%) and Oceania (1.1%). At the same time, the world production of apple trees has significantly stepped towards intensification, thanks to new cultivars and new cultivation technologies. Over the past twenty years, global apple production has grown significantly due to the intensification of production and the introduction of new breeding cultivars.

Plant leaf disease classification using deep neural network

2021 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
T. Meera Devi ◽  
Arivazhagan T. Shangar ◽  
R. Yashwin ◽  
J.S. Shabhareesh ◽  
N. Kasthuri
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Disease Classification ◽  
Plant Leaf ◽  
Leaf Disease

scholarly journals Automatic Classification System of Arrhythmias Using 12-Lead ECGs with a Deep Neural Network Based on an Attention Mechanism

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1827
Author(s):  
Dengao Li ◽  
Hang Wu ◽  
Jumin Zhao ◽  
Ye Tao ◽  
Jian Fu
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Attention Mechanism ◽  
Public Database ◽  
Ecg Signals ◽  
The World ◽  
Neural Network Structure ◽  
Test Sets ◽  
Gated Recurrent Units

Nowadays, a series of social problems caused by cardiovascular diseases are becoming increasingly serious. Accurate and efficient classification of arrhythmias according to an electrocardiogram is of positive significance for improving the health status of people all over the world. In this paper, a new neural network structure based on the most common 12-lead electrocardiograms was proposed to realize the classification of nine arrhythmias, which consists of Inception and GRU (Gated Recurrent Units) primarily. Moreover, a new attention mechanism is added to the model, which makes sense for data symmetry. The average F1 score obtained from three different test sets was over 0.886 and the highest was 0.919. The accuracy, sensitivity, and specificity obtained from the PhysioNet public database were 0.928, 0.901, and 0.984, respectively. As a whole, this deep neural network performed well in the multi-label classification of 12-lead ECG signals and showed better stability than other methods in the case of more test samples.

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