The Plant Leaf Disease Diagnosis And Spectral Data Analysis Using Machine Learning – A Review

  • Shila Pawar, Megha Bhushan, Manoj Wagh

Abstract

Indian economy largely depends on Gross Domestic products (GDP) and the agriculture sector plays a key role. Plants are the key sources of energy supply to mankind. Various disease in the plant causes the effect on fruits, plant leaves and overall nourishment of plant. Now a days, plant diseases are identified by an agricultural expert with the aid of visual symptoms. In remote areas, due to insufficient knowledge of farmer’s it becomes challenging to identify disease(s) in plants. Earlier monitoring of the crop diseases and infections will be helpful to control and recover the crops so that permanent wilting can be avoided. Minimum use of pesticides is a great challenge for a developing country such as India. The meticulous, precise and primary analysis of plant diseases may curtail the dose of the pesticide. This review summarizes the pros and cons of all research through various important research parameters as well as illustrates the different techniques such as Spectral data analysis, Machine Learning, etc. These techniques will be able to detect the infections and diseases of plants or crops. This review illustrates the summary of various technologies such as digital image processing, Machine Learning and Spectral data analysis to detect, enumerate and categorize crop diseases. 

Published
2020-05-15
How to Cite
Shila Pawar, Megha Bhushan, Manoj Wagh. (2020). The Plant Leaf Disease Diagnosis And Spectral Data Analysis Using Machine Learning – A Review. International Journal of Advanced Science and Technology, 29(9s), 3343 - 3359. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/15945