Analysis of various Plant Disease detection Techniques

2019 ◽  
Vol 7 (7) ◽  
pp. 308-311
Author(s):  
Gazzal Thukral ◽  
Lal Chand
2021 ◽  
Author(s):  
Maki K. Habib ◽  
Hashem Rizk

As the agriculture industry is growing fast, many efforts are introduced to ensure a high quality of produce. Diseases and defects found in plants and crops affect greatly the agriculture industry. Hence, many techniques and technologies have been developed to help solve or reduce the impact of plant diseases. Imagining analysis tools and gas sensors are becoming more frequently integrated into smart systems for plant disease detection. Many disease detection systems incorporate imaging analysis tools and VOC (Volatile Organic Compound) profiling techniques to detect early symptoms of diseases and defects of plants, fruits, and vegetative produce. These disease detection techniques can be further categorized into two main groups: preharvest disease detection and postharvest disease detection techniques. This paper aims to introduce the available disease detection techniques and to compare them with the latest innovative smart systems that feature visible imaging, hyperspectral imaging, and VOC profiling. In addition, this paper considers the efforts to automate imaging techniques to help accelerate the disease detection process. Different approaches are analyzed and compared in terms of work environment, automation, implementation, and accuracy of disease identification along with the future evolution perspective in this field.


Author(s):  
. Anika ◽  
Navpreet Kaur

The paper exhibits a formal audit on early detection of heart disease which are the major cause of death. Computational science has potential to detect disease in prior stages automatically. With this review paper we describe machine learning for disease detection. Machine learning is a method of data analysis that automates analytical model building.Various techniques develop to predict cardiac disease based on cases through MRI was developed. Automated classification using machine learning. Feature extraction method using Cell Profiler and GLCM. Cell Profiler a public domain software, freely available is flourished by the Broad Institute's Imaging Platform and Glcm is a statistical method of examining texture .Various techniques to detect cardio vascular diseases.


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