scholarly journals Automated Quality Assessment of Crops Using CNN - Keras

2021 ◽  
pp. 641-645
Author(s):  
Meghashree ◽  
Alwyn Edison Mendonca ◽  
Ashika S Shetty
Keyword(s):  
Image Classification ◽  
Agricultural Production ◽  
Detection System ◽  
Cost Effective ◽  
Farming System ◽  
Modern Method ◽  
Innovative Technology ◽  
Disease Detection ◽  
Agricultural Field ◽  
Plant Leaf

Plant disease is an on-going challenge for the farmers and it has been one of the major threats to the income and the food security. This project is used to classify plant leaf into diseased and healthy leaf,to improve the quality and quantity of agricultural production in the country. The innovative technology that helps in improve the quality and quantity in the agricultural field is the smart farming system. It represented the modern method that provides cost-effective disease detection and deep learning with convolutional neural networks (CNNs) has achieved large successfulness in the categorisation of different plant leaf diseases. CNN reads a really very larger picture in a simple way. CNN nearly utilised to examine visual imagery and are frequently working behind the scenes in image classification. To extract the general features and then classify them under multiple based upon the features detected. This project will help the farmers financially in increasing the production of the crop yield as well as the overall agricultural production. The paper reviews the expected methods of plant leaf disease detection system that facilitates the advancement in agriculture. It includes various phases such as image preprocessing, image classification, feature extraction and detecting healthy or diseased.

Plant Disease Detection Using Machine Learning Approaches

2021 ◽  
pp. 122-130
Author(s):  
Sukanta Ghosh ◽  
Shubhanshu Arya ◽  
Amar Singh
Keyword(s):  
Plant Disease ◽  
Crop Yields ◽  
Detection System ◽  
Visual Observation ◽  
Plant Diseases ◽  
Disease Detection ◽  
Learning Approaches ◽  
Factors Affecting ◽  
Plant Leaf ◽  
Market Situation

Agricultural production is one of the main factors affecting a country's domestic market situation. Many problems are the reasons for estimating crop yields, which vary in different parts of the world. Overuse of chemical fertilizers, uneven distribution of rainfall, and uneven soil fertility lead to plant diseases. This forces us to focus on effective methods for detecting plant diseases. It is important to find an effective plant disease detection technique. Plants need to be monitored from the beginning of their life cycle to avoid such diseases. Observation is a kind of visual observation, which is time-consuming, costly, and requires a lot of experience. For speeding up this process, it is necessary to automate the disease detection system. A lot of researchers have developed plant leaf detection systems based on various technologies. In this chapter, the authors discuss the potential of methods for detecting plant leaf diseases. It includes various steps such as image acquisition, image segmentation, feature extraction, and classification.

scholarly journals Plant Leaf Disease Detection Using Support Vector Machine

2019 ◽  
Vol 30 (1) ◽  
pp. 105
Author(s):  
Mohammed Hussein ◽  
Amel H. Abbas
Keyword(s):  
Support Vector Machine ◽  
Knowledge Base ◽  
Plant Protection ◽  
Detection System ◽  
Training Data ◽  
Support Vector ◽  
Disease Detection ◽  
Second Phase ◽  
Special Importance ◽  
Plant Leaf

Abstract Agriculture has special importance in that it is a major source of food and clothing and is an important economic source for countries. Agriculture is affected by a variety of factors, biotic such as diseases resulting from bacteria, fungi, viruses and non-biotic such as water and temperature and other environmental factors. detection of these diseases requires people to expert in addition to a set of equipment and it is expensive in terms of time and money Therefore, the development of a computer based system that detection the diseases of plants is very helpful for farmers As well as to specialists in the field of plant protection. the proposed plant disease detection system consists of two phases, in the first phase we establish the knowledge base and this by introducing a set of training samples in a series of processing that include first use pre-processing techniques such cropping , resizing, fuzzy histogram equalization ,next extract a set of color and texture feature and used to great the knowledge base that used as training data for support vector machine classifier . In the second phase of the work we use the classifier that was trained using the knowledge base for detection and diagnosis of plant leaf diseases. To create the knowledge base we used 799 sample images and divided it by 80% training and 20% testing. We have use Three crops each yield three diseases in addition to the proper state of each crop .the accuracy of disease detection was 88.1% .

PLANT LEAF DISEASE DETECTION SYSTEM USING CONVOLUTIONAL NEUTRAL NETWORKS

IJARCCE ◽  
2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Mr. M Ravikumar ◽  
Afaf Kuppanath ◽  
Dharsith N S ◽  
Syam Krishnan P K
Keyword(s):  
Detection System ◽  
Disease Detection ◽  
Plant Leaf ◽  
Neutral Networks ◽  
Leaf Disease

scholarly journals CRISPR/Cas12a-Based Ultrasensitive and Rapid Detection of JAK2 V617F Somatic Mutation in Myeloproliferative Neoplasms

Biosensors ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 247
Author(s):  
Miaomiao Chen ◽  
Chunhua Zhang ◽  
Zhiqing Hu ◽  
Zhuo Li ◽  
Menglin Li ◽  
...  
Keyword(s):  
Detection System ◽  
Myeloproliferative Neoplasms ◽  
Cost Effective ◽  
Jak2 V617f ◽  
Jak2 V617f Mutation ◽  
Minimum Detectable Concentration ◽  
Lateral Flow Strip ◽  
Whole Process ◽  
Detectable Concentration ◽  
V617f Mutation

The JAK2 V617F mutation is a major diagnostic, therapeutic, and monitoring molecular target of Philadelphia-negative myeloproliferative neoplasms (MPNs). To date, numerous methods of detecting the JAK2 V617F mutation have been reported, but there is no gold-standard diagnostic method for clinical applications. Here, we developed and validated an efficient Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR associated protein 12a (Cas12a)-based assay to detect the JAK2 V617F mutation. Our results showed that the sensitivity of the JAK2 V617F/Cas12a fluorescence detection system was as high as 0.01%, and the JAK2 V617F/Cas12a lateral flow strip assay could unambiguously detect as low as 0.5% of the JAK2 V617F mutation, which was much higher than the sensitivity required for clinical application. The minimum detectable concentration of genomic DNA achieved was 0.01 ng/μL (~5 aM, ~3 copies/μL). In addition, the whole process only took about 1.5 h, and the cost of an individual test was much lower than that of the current assays. Thus, our methods can be applied to detect the JAK2 V617F mutation, and they are highly sensitive, rapid, cost-effective, and convenient.

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