Open AccessPlant Leaf Disease and Fertilizer Prediction using Image Processing and CNN Algorithm
Author(s) -
Gaurav Dahake,
Vaishnav Karadale,
Sakolkar Rushikesh,
Niranjan Bankar,
Prof. Sonali Sethi
Publication year - 2022
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-2664
Subject(s) - agriculture , agricultural productivity , plant disease , robustness (evolution) , computer science , image processing , disease , image segmentation , artificial intelligence , agricultural engineering , productivity , segmentation , machine learning , microbiology and biotechnology , engineering , image (mathematics) , biology , medicine , economics , biochemistry , macroeconomics , pathology , gene , ecology
Agricultural products are the most basic requirement of every country. Infected plants have a negative influence on the country's agricultural production and economic resources. Early disease detection is critical in agriculture for a high crop yield. After recognizing the symptoms of leaf diseases, automatic methods for classification of plant diseases can also assist in taking action. Plant disease detection is critical in the agricultural sector because it affects the plant's robustness and health, both of which are important factors in agricultural productivity. These issues are prevalent in plants, and if suitable preventative measures are not implemented, the culture may be severely harmed. In the actual world, the existing approach of detecting disease relies on an expert's opinion and physical analysis, which is time-consuming and costly. We're introducing automatic plant leaf disease detection and classification based on artificial intelligence for rapid and easy disease diagnosis and classification. This main aim of our system is towards increasing the productivity of crops in agriculture. We followed numerous phases in this approach, including image collecting, image pre-processing, segmentation, and classification.