Design of Plant Disease Detection System: A Transfer Learning Approach Work in Progress
Proposed System Architecture
The use of Information and communication technologies(ICT) has become essential to help farmers collect important and updated information and knowledge which are valuable resources that farming depend. The study embarked investigates the problem sources of unavailability or lack of timely, relevant and accurate farming information and knowledge for small-scale farmers. The main target is to deal with plant diseases and how to manage them by carefully diagnosing the plants leaves. This work proposes to use image analysis and convolution neural networks and the ever increasing capability of machine learning such as supervised learning to offer a mobile solution. A Design Science Research Methodology was followed in shaping skeleton of the proposed prototype. The developed prototype will be subjected to three usability measures to test if indeed it is timely, relevant and accurate as the farmers need it to be. A small-scale farmer refers to individuals who rear less or about twenty-five cattle for beef production. This group solely rely on knowledge and information to manage and maintain their farming. It is evident that the knowledge from our forefathers about farming is not shared; it is not captured and structured in a way to assist the farming sector. The information and knowledge is also susceptible to being altered in transit from one recipient of the other.
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