An Efficient Guided Backpropagation Approach for Detection of Plant Diseases Using Deep Learning Models
Keywords:
Plant Disease Detection, Deep Learning, Pre-Trained Models, Transfer Learning, Region of Interest, Guided BackpropagationAbstract
Technology driven agriculture or precision agriculture is an important research area as there is need for significant changes in agriculture for productivity. Researchers are inspired by the emergence of deep learning techniques as they are suitable for computer vision applications like plant disease detection. There are many pre-trained deep learning models already being used for this purpose. However, they are applied to entire leaf images leading to time and space complexity. In this paper, we proposed a novel deep learning framework that exploits pre-trained deep learning models along with transfer learning towards faster convergence and higher level of accuracy. Besides our framework is enhanced with Region of Interest (ROI) computation to leverage detection accuracy and reduction of computational complexity. We proposed an algorithm known as Learning based Plant Disease Detection using Guided Backpropagation for ROI Computation (LbPDD-GBROIC). The proposed framework exploits pre-trained deep models such as AlexNet, DenseNet169, Inception V3, ResNet50, Squeezenet v1 and VGG19 along with transfer learning and ROI computation. Our empirical study using PlantVillege dataset revealed that ROI computation has significant impact on all models. Inception V3 model outperformed other models with 99.76% accuracy.
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