Open AccessDetection of Plant Diseases Using Convolutional Neural Networks
Author(s) -
Krishna Vamsi Kurumaddali
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.37641
Subject(s) - computer science , productivity , agriculture , agricultural engineering , agricultural productivity , deep learning , convolutional neural network , food security , image processing , artificial neural network , artificial intelligence , risk analysis (engineering) , business , image (mathematics) , engineering , ecology , macroeconomics , economics , biology
Introduction of various technologies like Artificial Intelligence, Image Processing, etc. there has been a significant improvement in the growth of various sectors. They have automated a lot of existing tasks that happen to be difficult to be handled manually thereby reducing the load simultaneously providing precision, efficiency and productivity. This research provides various ways to improve the agricultural sector in terms of productivity and also in terms of efficiency. Image processing of crops for analysis, crop disease detection, etc. are some of the various applications of technology in agriculture. This also provides an effective way of monitoring various internal and external factors like soil fertility, water logging capacity, temperature, etc. Providing a much more cost-effective way of increasing agricultural output and improved efficiency, the implementation of modern technologies improves agricultural sector in various ways. Technological improvements provide the farmers security of their crops getting infected by any pests, being impacted by climate changes, etc. These improvements also reduce the time the farmer needs to spend on the farm by utilizing the concept of deep learning and neural networks. There are various other ways in which technology can benefit the agricultural sectors. Keywords: Agriculture, Artificial Intelligence, Deep Learning, Image Processing, Neural Networks.