Deep learning based detector for identification of woodiness disease in passion fruits
MetadataShow full item record
In this project, Artificial Intelligence based algorithms were developed based on simple leaves to detect woodiness disease, a passion fruit disease in presence of the other classes of diseases through deep learning methodologies. Training of the algorithms was performed with the use of a data-set of 10,510 images that contained several distinct classes of plant disease combinations including the healthy plants that were obtained after visiting several farms in Hoima and Luwero using a 24MP NIKON D5300 with an APS-C sensor camera. Training and Testing were done with frameworks(Faster Regional Convolutional Neural Network V2 COCO with ResNet and Single Shot Multibox detector with ResNet), using a single graphical processor Unit (GPU), the NVIDIA-1080TI. The best performance reached a 99% success rate in identifying woodiness disease with Faster RCNN with ResNet framework. The significantly high success rate makes the model a very useful advisory or early warning tool, and an approach that could be further expanded to support an integrated plant disease identification.