A Survey on Various Unimodal Biometric Techniques

Agriculture plays a very important role as it helps to accomplish the need of food among people. The production in agriculture consequentially contributes to the economy of every country. The grain crops rice, wheat, maize, and legumes are suffering a lot due to some viral, bacterial, and fungal diseases. The pest and variety of diseases can bring a heavy loss to the global economy. The monitoring of crops health and identification of diseases at early days is very challenging and emerging task in agriculture. So, it is very important to prevent crops from fatal diseases in the early stage, but the manual process of disease discovery can lead to erroneous magnitude of pesticides. The trouble is figure out by automate discovery of diseases and supplication of relevant medication on time. It is very necessary to find out accurate disease to overcome heavy loss to economy. From the few decades, to detect disease correctly, the process of detection become automate using emerging technologies and techniques using computer vision, machine learning and image processing. This article presents the extensive literature on existing methodologies utilized for recognition and classification of leaves diseases. The studies addresses that there is still many limitations and challenges find in different phases in plant disease detection system. The presented research also highlights the pros and cons of different techniques that help out the researchers for contribution in future.