Abstract
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.
Highlights
Image feature extraction and classification is a computer vision field that has been studied intensively by researchers due to its practical relevance for various scenarios, including that of precision agriculture, [1]
OUTEX_TC_00013 RESULTS In the first experiment, we considered extracting the features from the last layer located before the classification layers of the four pre-trained Convolutional Neural Network (CNN) models from Table I: ResNet18, AlexNet, Vgg16, and ResNet50
To obtain a relevant classification performance even for small datasets, we based our work on the transfer learning concept and adapted to the task popular CNN models (AlexNet, Vgg16, ResNet) pre-trained on the very large ImageNet object-based dataset
Summary
Image feature extraction and classification is a computer vision field that has been studied intensively by researchers due to its practical relevance for various scenarios, including that of precision agriculture, [1]. In order to improve the robustness to Gaussian noise, the Block Matching and 3D Filtering Extended Local Binary Patterns (BM3DELBP) was introduced by us in [13] Another popular texture feature descriptor is the Gray-Level Cooccurrence Matrix (GLCM) [14] which achieved significant performance for texture classification tasks as reported in the literature. CNN-based methods that explored the transfer learning approach by using features derived from pre-trained CNNs on large image datasets can be found in the work of [22]–[26] and others. In [33], the authors proposed the Texture CNN architecture which is based on AlexNet but uses an energy measure derived from the last convolutional layer They arrived at the conclusion that the size of the dataset strongly influences the performance.
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