Online ISSN: 2515-8260

Keywords : Diseases


S. Vidyashri; M.P. Brundha; Priyadharshini R

European Journal of Molecular & Clinical Medicine, 2021, Volume 8, Issue 3, Pages 3153-3164

Introduction: Diseases are of two types based on the etiological agents- food borne poisoning and food borne infections. Food poisoning is caused mainly due to Poisonous chemicals or other toxic substances present in food. Ingestion of contaminated drink or food with toxins or bacteria or chemical substances causes acute gastritis. The main problems faced by the students residing in hostels are quality of the food served, sanitation and cleanliness of the hostel, pure water supply and first aid facilities in case of need. Food directly impacts the health of the student and unsanitary food causes a negative outlook of the student which in turn impacts the students desire to stay in the hostel. Potentially risky procedures include preservation of leftovers in an unsanitary environment, reheating of food in a possible unsafe way and food preparation with unprotected wounds on their hands. Student education regarding food poisoning needs to be in focus in an effort to decrease diseases which are food borne.

Identification and Detection of Plant Diseases by Convolutional Neural Networks

A. Iyswariya; V. Ramkumar; Sarvepalli Chandrasekhar; Yaddala Chandrasekhar Reddy; Vunnam Sai Tathwik; V.Praveen Kumar

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2200-2205

Agribusiness is the foundation of Indian economy. Plant health and food safety goes hand in hand. The health of green plants is of vital importance to everyone.Plant diseases being an impairment to the normal state of a plant, it interrupts or modifies plants vital functions. The proposed system helps in identification of plant disease and provides remedies that can be used as a defense mechanism against the disease. The database obtained from the Internet is properly segregated and the different plant species are identified and are renamed to form a proper database then obtain test-database which consists of various plant diseases that are used for checking the accuracy and confidence level of the project .Then using training data we will train our classifier and then output will be predicted with optimum accuracy. We use Convolution Neural Network (CNN) which comprises of different layers are used for prediction.CNNs provide unparalleled performance in tasks related to the classification and detection of crop diseases. They are able to manage complex issues in difficult imaging conditions A prototype drone model is also designed which can be used for live coverage of large agricultural fields to which a high resolution camera is attached and will capture images of the plants which will act as input for the software, based of which the software will tell us whether the plant is healthy or not. With our code and training model we have achieved an accuracy level of 78%. Our software gives us the name of the plant species with its confidence level and also the remedy that can be taken as a cure.