Crops Leaf Diseases Recognition: A Framework of Optimum Deep Learning Features

Abstract

Manual diagnosis of crops diseases is not an easy process; thus, a computerized method is widely used. From a couple of years, advancements in the domain of machine learning, such as deep learning, have shown substantial success. However, they still faced some challenges such as similarity in disease symptoms and irrelevant features extraction. In this article, we proposed a new deep learning architecture with optimization algorithm for cucumber and potato leaf diseases recognition. The proposed architecture consists of five steps. In the first step, data augmentation is performed to increase the numbers of training samples. In the second step, pre-trained DarkNet19 deep model is opted and fine-tuned that later utilized for the training of fine-tuned model through transfer learning. Deep features are extracted from the global pooling layer in the next step that is refined using Improved Cuckoo search algorithm. The best selected features are finally classified using machine learning classifiers such as SVM, and named a few more for final classification results. The proposed architecture is tested using publicly available datasets-Cucumber National Dataset and Plant Village. The proposed architecture achieved an accuracy of 100.0%, 92.9%, and 99.2%, respectively. A comparison with recent techniques is also performed, revealing that the proposed method achieved improved accuracy while consuming less computational time.

An Integrated Framework of Two-Stream Deep Learning Models Optimal Information Fusion for Fruits Disease Recognition

“…The selection of the best features from the original set of the feature vector is known as the best feature selection. The primary purpose of best feature selection is to reduce the size of the original feature vector in terms of redundant information and noisy features [59,60]. This process helps fast execution of a framework and may improve accuracy.…”

Palm tree disease detection and classification using residual network and transfer learning of inception ResNet

“…In this approach, the features were selected with a wrapper approach consisting of FPA and SVM to keep the classifier performance high and an accuracy of 99.6% was achieved. The authors in [ 10 ] proposed a deep learning model based on an optimization algorithm for recognizing cucumber and potato leaf diseases. They extracted deep features from the global pooling layer in the next step tuned using enhanced Cuckoo search algorithm and they achieved an accuracy of 99.2%.…”