Design of an iOS App Architecture for Cotton Plant Disease Detection Using Artificial Intelligence and Machine Learning Techniques

Abstract

AbstractIdentifying the correct crop disease is very crucial to timely control of the disease. The mobile app will be one of the easy ways to identify crop diseases using the latest machine learning and artificial intelligence techniques. This paper proposed the architecture design of the iOS app which will be used to identify cotton plant diseases. The proposed architecture is designed with the latest iOS design pattern, the latest tech stack, and compliance with SDLC. The proposed architecture is based on scalability, high performance, and usability. This proposed architecture will be a blueprint of the actual development of an iOS mobile app. The proposed architecture also supports the usage of ML model in iOS app. The proposed architecture is also supporting the latest machine learning and artificial techniques that are used in identifying the cotton plant disease. Based on the development of this app, the future enhancement can be done by using the server-side implementation when network available in device. The performance of the app is measured by properly doing the memory management using the instruments and profiling of the source code.KeywordsiOSMachine learningArtificial intelligenceCotton leafDisease detectionAppleArchitectureDesigningSoftware architecture VIPERCoreMLCreateMLSDLCML modelMobile appApp development