E-Citrus: A Cloud-Based Citrus Pest and Disease Detection, Diagnostic and Prevention using Convolutional Neural Network

Abstract

Citrus fruit yields in the Philippines have been fluctuating dramatically in recent years. Diseases, pests, and soil inadequacy have all contributed to the citrus industry's severe decline. More than 15 viruses and virus-like diseases have infected Citrus. Agricultural productivity must improve for a country to be progressive. Resources should be utilized to their full potential, diseases and pests should be controlled efficiently, and technological advancements must be adopted. This application will identify and map common pests and diseases of citrus fruits in Oriental Mindoro, apply image processing techniques to analyze diseases of citrus fruits with corresponding solutions caused by bacteria, and give information about diseases related to citrus fruits and how to cure them. The researchers used the Spiral Model as a Software Development Life Cycle (SDLC) model to develop this application. In this model, researchers can plan the flow of the application. If the application did not meet the desired result, the researchers could revise it again until it met the desired one. The researchers used the convolutional neural network to classify and process the captured images of the citrus fruits’ diseases and pests. The researchers asked the selected Citrus farmers in Oriental Mindoro to evaluate the project using the different ISO 25010 criteria and rated the application as very acceptable overall.