Citrus Disease Management Using Convolutional Neural Networks

Abstract

Huanglongbing (HLB) is an incurable disease that affects citrus trees. This citrus disease is also often referred to as "citrus greening". This disease was first spotted in China in the early 1900s and was kept under check for a century. The disease started infecting plants in Florida in 2005 – the largest orange-producing state in the United States. By 2012 HLB had spread much of citrus farms in the entire state of Florida and started to wreak havoc in the billion-dollar citrus industry in Florida. The disease also threatens the citrus industry in California and other states. There are microscopic, molecular, and spectroscopic techniques to detect HLB on citrus plants today but require a laboratory setting. These techniques are practically hard to implement on a large citrus grove. The only way growers could manage this disease today is by visually inspecting the plants and then removing the infected plants as the best way to control the problem. In this research project, an AI-powered method and process is defined, and an economically sustainable system is developed to identify HLB disease on the citrus plants and provide early detection signals on the citrus growers. The goal of this project is to research and develop a predictive model using Computer Vision, Artificial Intelligence, and Deep Learning using Convolutional Neural Networks to identify citrus leaves infected with the HLB disease and to implement a remotely piloted aerial system to detect the citrus leaves infected with the disease in citrus groves using the predictive AI model.

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