Image processing based Smart Weed Removal and Organic Fertilizer Sprinkling Bot – A Systematic Review

2021 ◽  
Author(s):  
Rajyalakshmi Uppada ◽  
Vanaja Kandubothula ◽  
Uppalapati Padma
Keyword(s):  
Systematic Review ◽  
Image Processing ◽  
Organic Fertilizer ◽  
Weed Removal

ATM crime detection using image processing integrated video surveillance: a systematic review

2018 ◽  
Vol 25 (3) ◽  
pp. 229-251 ◽  
Author(s):  
Tasriva Sikandar ◽  
Kamarul Hawari Ghazali ◽  
Mohammad Fazle Rabbi
Keyword(s):  
Systematic Review ◽  
Image Processing ◽  
Video Surveillance ◽  
Crime Detection

scholarly journals Renal structural image processing techniques: a systematic review

Renal Failure ◽  
2019 ◽  
Vol 41 (1) ◽  
pp. 57-68
Author(s):  
Shiva Asadzadeh ◽  
Hamid Tayebi Khosroshahi ◽  
Behzad Abedi ◽  
Yaghoob Ghasemi ◽  
Saeed Meshgini
Keyword(s):  
Systematic Review ◽  
Image Processing ◽  
Image Processing Techniques ◽  
Structural Image ◽  
Processing Techniques

scholarly journals Monitoring Plant Status and Fertilization Strategy through Multispectral Images

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 435 ◽  
Author(s):  
Matheus Cardim Ferreira Lima ◽  
Anne Krus ◽  
Constantino Valero ◽  
Antonio Barrientos ◽  
Jaime del Cerro ◽  
...  
Keyword(s):  
Image Processing ◽  
Evaluation System ◽  
Near Infrared ◽  
Organic Fertilizer ◽  
Vegetation Indices ◽  
Processing System ◽  
Morphological Features ◽  
Water Soluble ◽  
Organic Fertilization ◽  
Tomato Plants

A crop monitoring system was developed for the supervision of organic fertilization status on tomato plants at early stages. An automatic and nondestructive approach was used to analyze tomato plants with different levels of water-soluble organic fertilizer (3 + 5 NK) and vermicompost. The evaluation system was composed by a multispectral camera with five lenses: green (550 nm), red (660 nm), red edge (735 nm), near infrared (790 nm), RGB, and a computational image processing system. The water-soluble fertilizer was applied weekly in four different treatments: (T0: 0 mL, T1: 6.25 mL, T2: 12.5 mL and T3: 25 mL) and the vermicomposting was added in Weeks 1 and 5. The trial was conducted in a greenhouse and 192 images were taken with each lens. A plant segmentation algorithm was developed and several vegetation indices were calculated. On top of calculating indices, multiple morphological features were obtained through image processing techniques. The morphological features were revealed to be more feasible to distinguish between the control and the organic fertilized plants than the vegetation indices. The system was developed in order to be assembled in a precision organic fertilization robotic platform.

scholarly journals Deep Learning-based Models of Molecular Phenotypes for Predicting the Overall Survival in Cancer

2021 ◽  
pp. 218-234
Author(s):  
Gerardo Cazzato ◽  
Anjali Oak ◽  
Asim Mustafa Khan ◽  
. Jayesh
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Image Processing ◽  
Deep Learning ◽  
Cancer Survival ◽  
Primary Sources ◽  
Learning Models ◽  
Molecular Phenotypes ◽  
Cancerous Cells

Aims: The aim of the study is to justify the need of deep learning predictive model in obtaining molecular phenotypes of overall cancer survival. Study Design: The study is based on the secondary qualitative data analysis through usage of systematic review. Methodology: A qualitative study has been conducted to analyse the necessity of deep learning.  It also includes the need for deep learning models to obtain the imaging of the cancer cells. In the study, a detailed discussion on deep learning has been made. The analysis of the primary sources has been obtained by evaluating the quality of the resources in the study. The study also comprises of a thematic analysis that enlightens the benefits of deep learning. The study is based on the analysis of 14 primary research-based articles out of 112 quantitative articles and structuring of a systematic review from the collected data. Results: The morphological and physiological changes that occur in the cancerous cells have been clearly evaluated in the research. The result signifies the prediction can be made by implementing deep learning in terms of cancer survival. Advancements in terms of technology in the medical field can thus be improved with the help of the deep learning process. It states the advancements of the deep learning models that are helpful in predicting the model of cancer to determine survival rate. Conclusion: Deep learning is a process that is considered to be a subset of artificial intelligence. Deep learning programmes are meant to be performed for complex learning models. Although there is difference in the concept of deep learning and image processing still artificial intelligence brings both together so as to ensure better performance in image processing. The need for deep learning models has become invasive, and it helps to build a strong ground for cancer survival.

Some comments about observations and image processing of comet 29P/Schwassmann-Wachmann 1

1999 ◽  
Vol 173 ◽  
pp. 243-248
Author(s):  
D. Kubáček ◽  
A. Galád ◽  
A. Pravda
Keyword(s):  
Image Processing ◽  
Large Scale ◽  
Harvard College ◽  
Oak Ridge ◽  
Large Scale Structures ◽  
Short Period Comet ◽  
Short Period ◽  
Scale Structures ◽  
Harvard College Observatory ◽  
Period Comet

AbstractUnusual short-period comet 29P/Schwassmann-Wachmann 1 inspired many observers to explain its unpredictable outbursts. In this paper large scale structures and features from the inner part of the coma in time periods around outbursts are studied. CCD images were taken at Whipple Observatory, Mt. Hopkins, in 1989 and at Astronomical Observatory, Modra, from 1995 to 1998. Photographic plates of the comet were taken at Harvard College Observatory, Oak Ridge, from 1974 to 1982. The latter were digitized at first to apply the same techniques of image processing for optimizing the visibility of features in the coma during outbursts. Outbursts and coma structures show various shapes.

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