Design of an iOS App Architecture for Cotton Plant Disease Detection Using Artificial Intelligence and Machine Learning Techniques

2022 ◽  
pp. 679-690
Author(s):  
Sandeep Kumar ◽  
Rajeev Ratan ◽  
J. V. Desai
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cotton Plant ◽  
Plant Disease ◽  
Machine Learning Techniques ◽  
Disease Detection ◽  
Learning Techniques

Machine Learning Techniques for Plant Disease Detection

2021 ◽  
Author(s):  
Divyanshu Varshney ◽  
Burhanuddin Babukhanwala ◽  
Javed Khan ◽  
Deepika Saxena ◽  
Ashutosh kumar Singh
Keyword(s):  
Machine Learning ◽  
Plant Disease ◽  
Machine Learning Techniques ◽  
Disease Detection ◽  
Learning Techniques

scholarly journals MACHINE LEARNING TECHNIQUES IN PLANT DISEASE DETECTION AND CLASSIFICATION – A STATE OF THE ART

2021 ◽  
pp. 362-372
Author(s):  
John Sreya ◽  
Leena Rose Arul
Keyword(s):  
Machine Learning ◽  
Plant Disease ◽  
Machine Learning Algorithms ◽  
Plant Diseases ◽  
Machine Learning Techniques ◽  
Small Scale ◽  
Disease Detection ◽  
Detection Systems ◽  
Learning Techniques ◽  
Processing Techniques

As we belong to a developing country, the agricultural importance is a known criterion. Majority of the Indians depend on agriculture for their basic living. It also serves as the backbone of the Indian economy. Therefore this sector should be considered important and taken care of. Diseases affecting the plants and pest are the two major threats of agriculture production. Naked eye observation followed by the addition of chemical fertilizers is the traditional method adopted by most of the farmers to avoid plant diseases. But the main limitation to this method is that it works only in the case of small scale farming. In order to tackle this issue many automatic plant disease detection systems have been developed from the early 70s. This paper is intended to survey some of the existing works in plant disease recognition that include various procedures, materials and approaches. They use different machine learning algorithms, image processing techniques and deep learning methods for disease detection. This paper also compares and suggests novel methods to recognize and classify the various kinds of infections affecting agricultural plants.

scholarly journals Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa

2021 ◽  
Vol 18 (15) ◽  
pp. 7890
Author(s):  
Bruce Mellado ◽  
Jianhong Wu ◽  
Jude Dzevela Kong ◽  
Nicola Luigi Bragazzi ◽  
Ali Asgary ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Developed Countries ◽  
Machine Learning Techniques ◽  
Multiple Data ◽  
Learning Techniques ◽  
Unequal Access ◽  
Roll Out

COVID-19 is imposing massive health, social and economic costs. While many developed countries have started vaccinating, most African nations are waiting for vaccine stocks to be allocated and are using clinical public health (CPH) strategies to control the pandemic. The emergence of variants of concern (VOC), unequal access to the vaccine supply and locally specific logistical and vaccine delivery parameters, add complexity to national CPH strategies and amplify the urgent need for effective CPH policies. Big data and artificial intelligence machine learning techniques and collaborations can be instrumental in an accurate, timely, locally nuanced analysis of multiple data sources to inform CPH decision-making, vaccination strategies and their staged roll-out. The Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) has been established to develop and employ machine learning techniques to design CPH strategies in Africa, which requires ongoing collaboration, testing and development to maximize the equity and effectiveness of COVID-19-related CPH interventions.

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