A Mobile application based system for tomato pest and disease detection

Abstract

Precision farming is an approach to farming that uses technology and data to reduce farming costs, improve crop yields, and optimize resource utilization. It relies on a range of tools and technologies to collect, process, and analyse data to derive actionable information that is used to make informed decisions about planting, disease and pest control, and harvesting. Tools used in data collection include Internet of Things (IoT) devices, Unmanned Aerial Vehicles (UAVs), Geographic Information Systems (GIS), and weather stations. Data analytics, GIS mapping and machine learning are used to process, analyse, and make sense of the data. Several factors limit smallholder farmers from adopting precision farming solutions. These include high cost of implementation, limited digital infrastructure such as the Internet, lack of awareness and limited knowledge in utilising these solutions. This research aimed to bridge this gap by developing a system that uses machine learning to detect tomato pests and diseases and suggest treatment and prevention measures through the mobile phone. A review of the factors influencing agricultural productivity among smallholder farmers, and existing solutions that attempt to mitigate these factors was carried out. A requirement analysis was carried out to establish the viability of developing a mobile application-based system for tomato disease detection. Subsequently, this dissertation developed a deep learning Convolutional Neural Network (CNN) model trained using 16,880 images of tomato diseases. The trained model achieved a validation accuracy of 98.22%. A mobile application was developed, and the machine learning model embedded into it. Prototyping software development methodology was adopted to develop the system. The system was tested at several stages, to ensure that it met the set requirements for performance and functional requirements. The system was able to detect tomato diseases and provide the user with disease treatment and prevention recommendations.

Keywords: precision farming, machine learning, convolutional neural network, smallholder farming.