scholarly journals Pest Detection in Plants Using Convolutional Neural Network

2021 ◽  
Vol 9 (11) ◽  
pp. 1583-1592
Author(s):  
Savita Sharma
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Climate Changes ◽  
Agricultural Sector ◽  
Predictive Analytics ◽  
Early Stage ◽  
Maximum Yield ◽  
Disease Classification ◽  
The World ◽  
Pest Detection

Abstract: Agriculture or farming is an imperative occupation since the historical backdrop of humanity is kept up. Artificial Intelligence is leading to a revolution in the agricultural practices. This revolution has safeguarded the crops from being affected by distinct factors like climate changes, porosity of the soil, availability of water, etc. The other factors that affect agriculture includes the increase in population, changes in the economy, issues related to food security, etc. Artificial Intelligence finds a lot of applications in the agricultural sector also which includes crop monitoring, soil management, pest detection, weed management and a lot more. Significant problems for sustainable farming include detection of illness and healthy monitoring of plants. Therefore, plant disease must automatically be detected with higher precision by means of image processing technology at an early stage. It consists of image capturing, preprocessing images, image segmentation, extraction of features and disease classification. The digital image processing method is one of those strong techniques used far earlier than human eyes could see to identify the tough symptoms. Considering different climatic situations in various regions of the world that impact local weather conditions. These climate changes affect crop yield directly. There is a great demand for such a platform in the world of today which would enable the farmer market his farm products. We have proposed in this study a system where farmers can sell their products directly to customers without the intervention of distributors and traders. The predictive analytics system is necessary for the farmer to get the maximum yield which benefit the farmer. This may be done if the environment, market conditions and knowledge of the timely planning of farms are known properly. Keywords: Pest Detection, Artificial Intelligence, Agriculture, Image processing, Convolutional Neural Networks

Early Stage Detection of Psoriasis Using Artificial Intelligence and Image Processing

2020 ◽  
pp. 1199-1208
Author(s):  
D. R. Kalbande ◽  
Uday Khopkar ◽  
Avinash Sharma ◽  
Neil Daftary ◽  
Yash Kokate ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Early Stage

scholarly journals Artificial Intelligence for early Detection of Breast Cancer and Classification of Mammographic Masses

2020 ◽  
Vol 8 (6) ◽  
pp. 4314-4320
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Image Processing ◽  
Early Stage ◽  
Probabilistic Neural Network ◽  
Breast Lesions ◽  
Test Image ◽  
False Positive Detection ◽  
Processing Techniques

Every single year thousands of women endure painful and invasive surgery to remove breast lesions. Most of the time the mammographic image analysis leads to false positive detection and the majority of this actions reveal the lesions to be benign. Refining present detection and diagnostic tool is a major priority of our work. MATLAB R2015a is been used to develop the algorithm, which aids in detection of breast cancer in its early stage. The algorithm comprises of image processing and applying artificial intelligence where in the system is trained with a set of images so that when the input or the test image is given, the algorithm performs the image processing techniques and then applies the Probabilistic Neural Network (PNN) technique for detection of cancer. The system performance is also been calculated in order to estimate its reliability.

Cotton Leaf Disease Detection by Feature Extraction

2016 ◽  
pp. 89-104
Author(s):  
Savita N. Ghaiwat ◽  
Parul Arora
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Feature Extraction ◽  
Early Stage ◽  
Cotton Leaf ◽  
Control Methods ◽  
The Neural Network ◽  
The World ◽  
Leaf Image

Cotton leaf diseases have occurred all over the world, including India. They adversely affect cotton quality and yield. Technology can help in identifying disease in early stage so that effective treatment can be given immediately. Now, the control methods rely mainly on artificial means. This paper propose application of image processing and machine learning in identifying three cotton leaf diseases through feature extraction. Using image processing, 12 types of features are extracted from cotton leaf image then the pattern was learned using BP Neural Network method in machine learning process. Three diseases have been diagnosed, namely Powdery mildew, Downy mildew and leafminer. The Neural Network classification performs well and could successfully detect and classify the tested disease.

scholarly journals Crop Diseases and Pest Detection using Deep Learning and Image Processing Techniques

2021 ◽  
Vol 9 (VI) ◽  
pp. 372-380
Author(s):  
Rohit V
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Crop Production ◽  
Early Stage ◽  
Diagnostic System ◽  
Yield Reduction ◽  
Pests And Diseases ◽  
Crop Diseases ◽  
Crop Disease ◽  
Pest Detection

Crop pests and diseases play a significant role in yield reduction and quality. Controlling and preventing pests and crop diseases has therefore become a priority. If disease is detected at an early stage, this can increase crop production and provide benefit to farmers. Manual detection of these diseases and pests can be very tedious and time consuming for farmers, especially if they have large farms. We plan to model a crop disease and pest diagnostic system using image processing and deep learning techniques. Crop disease and pest detection can be done using deep learning and image recognition techniques on leaves and other areas of the crop.

Comparative Study of Systems Based on AI to Diagnostic of COVID-19

2021 ◽  
Vol 10 (1) ◽  
pp. 382-390
Keyword(s):  
Artificial Intelligence ◽  
Health Service ◽  
Comparative Study ◽  
Healthcare System ◽  
New Technologies ◽  
Early Stage ◽  
X Ray ◽  
The World ◽  
International Issue

The pandemic COVID-19 keep spreading all over the world after it first appeared in China in December 2019. COVID-19 became an international issue for the health service. The role of each individual in our society is important to fight against this pandemic, the exceptional effort and various measures have been done and took by the healthcare and government to minimize the spread of this disease. Despite all this effort made, the number of COVID-19 cases increase and put a lot of pressure on the healthcare system all over the world, this end leads to many cases of deaths. The diagnostic of this virus in the early stage will help to treat the infected patients and save their life. The use of new technologies especially Artificial Intelligence (AI) can help radiologists and physicians to make fast, early, and exact diagnoses of this disease. In this paper, we elaborate a comparative study of various implemented methods for the diagnostic of COVID-19 based on the AI. And especially we will focus on the systems that combine the AI methods and medical imaging such as X-ray and CT

scholarly journals Artificial Intelligence and Platform Law for the Development of the Digital Agricultural Complex of the Russian Federation

Lex Russica ◽  
2021 ◽  
pp. 122-129
Author(s):  
S. Yu. Kashkin ◽  
A. V. Altukhov
Keyword(s):  
Artificial Intelligence ◽  
Russian Federation ◽  
Large Scale ◽  
Agricultural Sector ◽  
Positive Impact ◽  
Legal Regulation ◽  
Industrial Complex ◽  
Innovative Technologies ◽  
The World ◽  
The Russian Federation

Today, many processes are being digitalized in the world: production, technological, social, legal, economic, food, and this is not a fashionable trend, but a vital necessity. The state policy of Russia is also aimed at large-scale digitalization of various industries. Agricultural complex is of great importance for ensuring sovereignty, national security and supplying the population with necessary products. According to economists, the introduction of platform and other innovative technologies will have an extremely positive impact on the economy of our state; will increase the export potential, which will eventually enhance the country’s prestige in the world. However, lawyers rightly point out that the introduction of innovative technologies requires adequate innovation legislation. The paper deals with the problems of legal regulation of digitalization of the Russian agricultural complex based on artificial intelligence and the need to introduce elements of modern “platform law” into it. The possibilities and importance of digitalization are shown, the expediency of creating an appropriate legal platform is indicated. Definitions and explanations of the functioning of platforms and platform law are given. Approaches to digitalization in the European Union and the Russian Federation are considered. It is noted that an important mechanism for the functioning of the platform, including for the agricultural sector, is the standardization of mechanisms and norms of interaction from a technical and legal point of view. It is concluded that when training specialists of agricultural enterprises, it is important to include the study of the legal component, which will effectively use the emerging elements of complex legal platforms necessary for the innovative development of the agro-industrial complex.

scholarly journals Pest Detection using Image Processing

2019 ◽  
Vol 9 (2) ◽  
pp. 1496-1498
Keyword(s):  
Image Processing ◽  
Economic Activity ◽  
Large Scale ◽  
Early Stage ◽  
Productivity Loss ◽  
Morphological Operations ◽  
Image Processing Techniques ◽  
Pest Detection ◽  
Processing Techniques ◽  
Leaf Image

Agriculture is one of the most significant economic activity. They are many ways that leads to the low productivity of agriculture, but the best method to protect the crop is by detecting the diseases in the early stage. In most of the cases diseases are caused by pest, insects, pathogens which reduce the productivity of the crop at the large scale. If pests are detected on the leaves then, precautions should be taken to avoid huge productivity loss at the end. The main objective of this paper is to identify the pests using image processing techniques like Gaussian blur, segmentation, watershed separation, morphological operations. These techniques are more efficient and less time consuming while identifying the pests over the leaf image with high intensity.

Plant Diseases Detection and Classification Based on Image Processing

2020 ◽  
Vol 9 (3) ◽  
pp. 2989-2992
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Decision Making ◽  
Sustainable Development ◽  
Food Production ◽  
Real Life ◽  
Plant Diseases ◽  
Disease Classification ◽  
Life Problems ◽  
Agriculture And Food

To further the progress of sustainable development and solve real-life problems we have seen many processes are applied in our life, like artificial intelligence and decisionmaking. Morocco is one of the countries that rely heavily on agriculture and food production. So, food production is considered the basic needs of a human being for that we have seen fast advancements in agriculture productivity to meet the projected demand. However, with the time passing by, all species of plants are subjected to various types of diseases that cause huge damage. Although the observation of variation in the infected part of the leaf plant is very important but not enough because the perception of the human eye is not so much stronger. The identification of plant diseases is a very important task in the agriculture area. So, the best identification means there is a huge gain on agricultural productivity, quality, and quantity. To detect plant diseases in an earlier stage we require efficient and precise techniques to assist farmers in decision-making. This article presents, first, an overview of plant diseases from leaves images and different disease classification approaches that can be used for plant leaf disease detection.

scholarly journals Classification Techniques for Plant Disease Detection

2020 ◽  
Vol 8 (6) ◽  
pp. 5423-5430
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Plant Disease ◽  
Agricultural Sector ◽  
Early Stage ◽  
Support Vector ◽  
Disease Detection ◽  
Neural Network Approach ◽  
Traditional Approaches

Production of crops with better quality is the necessary attribute for the economic growth of any country. The agricultural sector provides employment to many people and accounts for major portion of gross domestic product in many countries around the world. Therefore, for enhanced agricultural productivity the detection of diseases in plants at an early stage is quite significant. The traditional approaches for disease detection in plants required considerable amount of time, intense research, and constant monitoring of the farm. However, optimized solutions have been obtained over the past few years due to technological advances that have resulted in better yields for the farmers. Machine learning and image processing are used to detect the disease on the agricultural harvest. The image processing steps for plant disease identification include acquiring of images, pre-processing, segmentation and feature extraction. In this review paper, we focused mainly on the most utilized classification mechanisms in disease detection of plants such as Convolutional Neural Network, Support Vector Machine, KNearest Neighbor, and Artificial Neural Network. It has been observed from the analysis that Convolutional Neural Network approach provides better accuracy compared to the traditional approaches.

scholarly journals Leaf Disease Classification using Advanced SVM Algorithm

2019 ◽  
Vol 8 (6S) ◽  
pp. 712-718
Keyword(s):  
Early Stage ◽  
Disease Classification ◽  
Green House ◽  
Leaf Disease ◽  
Svm Algorithm ◽  
Pest Detection ◽  
Color Conversion ◽  
System Focus

Presently there are many alternates of pesticides and unfortunately a very big portion of the industry is relies and using such poisons to protects crops to prevent from bugs attack and spreading of infection. Such pesticides are seriously very harmful and used unorganic chemicals. Even some of such pesticides are beneficial for insects too. Even some times there is also an possibility that such chemicals may be automatically washed during rain or watering the crops. So the research since years on green house agro system focus on early pest detection. Such methodology focus on observing plants by camera. The images captured by cameras can be used to analyzed that weather the plants are infected or not. A number of methods and algorithms such as color conversion, segmentation, k-mean, knn etc are used to classified such images. This research is focusing on the interpretation of image for early stage pest detection so that the crop should be prevented from damage.

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