Detection of Sugarcane Leaf Disease Using Deep Learning Techniques

In India, agriculture is the major source of revenue for farmers. In India, agriculture has a crucial significance due to its growing population and increased food consumption. Sugarcane growing is one of the greatest organized segments of agriculture for farmers. Due to the favourable conditions for its growth in India, it is the farmers's primary preference for agriculture. This is the reason sugarcane counted as a wealth for Indian farmers. It is cultivated among many cities like Uttar Pradesh, Bihar, Uttarakhand, Karnataka, Tamil Nadu, Maharashtra etc. Diseases caused by bacteria, fungus, and viruses are among the most significant factors influencing low crop production. This can be avoided and managed by utilizing plant disease detection techniques. Machine learning approaches shall be applied in the course of identifying plant illnesses because they primarily use information and provide fantastic techniques for disease diagnosis. Machine learning methods can be utilized to recognize diseases because they rely on superior data results for a specific goal. This paper reviewed different diseases found while identifying sugarcane leaf disease. This paper compares different disease take place due to different environment conditions. For this, approximately 50 papers have been reviewed. Further this paper highlights different technologies and framework used in recognition of sugarcane leaf disease.