Convolutional Neural Networks for Texture Feature Extraction. Applications to Leaf Disease Classification in Precision Agriculture

• Published in: IEEE access 2021, Vol.9, p.160085-160103
• Main Authors: Barburiceanu, Stefania, Meza, Serban, Orza, Bogdan, Malutan, Raul, Terebes, Romulus
• Format: Article
• Language: English
• Subjects: Agriculture; Applied convolutional neural networks; Artificial neural networks; Convolutional neural networks; Datasets; Deep learning; Diseases; Economic impact; Feature extraction; image classification; Impact analysis; leaf disease detection; Machine learning; Medical imaging; Neural networks; Object oriented modeling; Plant diseases; Smart sensors; Species classification; Support vector machines; Task analysis; Texture; texture classification; texture feature extraction; Transfer learning
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3131002

This paper studies the use of deep-learning models (AlexNet, VggNet, ResNet) pre-trained on object categories (ImageNet) in applied texture classification problems such as plant disease detection tasks. Research related to precision agriculture is of high relevance due to its potential economic impact on agricultural productivity and quality. Within this context, we propose a deep learning-based feature extraction method for the identification of plant species and the classification of plant leaf diseases. We focus on results relevant to real-time processing scenarios that can be easily transferred to manned/unmanned agricultural smart machinery (e.g. tractors, drones, robots, IoT smart sensor networks, etc.) by reconsidering the common processing pipeline. In our approach, texture features are extracted from different layers of pre-trained Convolutional Neural Network models and are later applied to a machine-learning classifier. For the experimental evaluation, we used publicly available datasets consisting of RGB textured images and datasets containing images of healthy and non-healthy plant leaves of different species. We compared our method to feature vectors derived from traditional handcrafted feature extraction descriptors computed for the same images and end-to-end deep-learning approaches. The proposed method proves to be significantly more efficient in terms of processing times and discriminative power, being able to surpass traditional and end-to-end CNN-based methods and provide a solution also to the problem of the reduced datasets available for precision agriculture.