A Novel Convolutional-Recurrent Hybrid Network for Sunn Pest–Damaged Wheat Grain Detection

Sabancı, Kadir; Aslan, Muhammet Fatih; Ropelewska, Ewa; Ünlerşen, Muhammed Fahri; Durdu, Akif

doi:10.1007/s12161-022-02251-0

Cited by 31 publications

(16 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other than CNN, it is observed that other DL algorithms such as ANN are used too [20,21]. Also, BiLSTM [22] is used for classification with an accuracy score of 99.50%. From these studies, we see that although DL algorithms are used very often, traditional ML algorithms also produce good results when adapted to the problem.…”

Section: Discussionmentioning

confidence: 99%

Sunn Pest Damaged and Healthy Wheat Grains Dataset Across Different Species

Pervan-Akman¹,

Çolak²,

Özkan³

et al. 2023

Preprint

View full text Add to dashboard Cite

Sunn pest is one of the most crucial noxious species in agriculture. As the sunn pest penetrates and makes damage to the grains, they can no longer be utilized to make bakery goods like bread, pasta, and other baked foods since they become unusable. Therefore, analysis and detection of damaged wheat grains are necessary for production to continue. We recommend that the dataset could be used for analysis and segmentation of the wheat grains, detection of the sunn pest damage condition, and classification of the 6 species of which the dataset consists. As mentioned, in our dataset, there are 6 different species of wheat, including Bezostaja, Müfitbey, Nacibey, Sönmez-2001, Tosunbey, and Ekiz, which are the species that were made in Türkiye. Our dataset differs from the others due to its various species and the condition, whether it is healthy or sunn pest damaged or broken wheat grain, and because the dataset is a multiclass one, it can be used for the classification of the varieties. On the other hand, when the sunn pest damage condition is taken into account, the dataset is suitable for the detection of the sunn pest damage. In addition to that, the dataset includes grains that touch each other which makes it more applicable to problems that arise in real-life. The dataset consists of 83 sunn pest damaged and 87 healthy wheat images. In addition, there are 170 images that contain 3565 wheat grains. As can be observed, the dataset contains a variety of wheat species with sunn pest affected which displays that the promised dataset is suitable for numerous machine learning problems such as classification, segmentation, and detection.

show abstract

Section: Discussionmentioning

confidence: 99%

Sunn Pest Damaged and Healthy Wheat Grains Dataset Across Different Species

Pervan-Akman¹,

Çolak²,

Özkan³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In order to objectively evaluate the performance of the methods, Accuracy (ACC), Sensitivity (TPR), Speci city (TNR), Precision (PRE), F1-Score, and Mathew Correlation Coe cient (MCC) metrics are calculated from the confusion matrix [11,[47][48][49][50]. In Figure 4, a multiclass confusion matrix is shown.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Computer-Aided Multiclass Classification of Corn from Corn Images Integrating Deep Feature Extraction

Bhamidipati

Yaşar

Taşpınar

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Corn has great importance in terms of production in the field of agriculture and animal feed. Obtaining pure corn seeds in corn production is quite significant for seed quality. For this reason, the distinction of corn seeds that have numerous varieties plays an essential role in marketing. This study was conducted with 14,469 images of BT6470, Calipso, Es_Armandi, and Hiva types of corn licensed by BIOTEK. The classification of images was carried out in three stages. At the first stage, deep feature extraction of the four types of corn images was performed with the pretrained CNN model SqueezeNet 1000 deep features were obtained for each image. In the second stage, in order to reduce these features obtained from deep feature extraction with SqueezeNet, separate feature selection processes were performed with the Bat Optimization (BA), Whale Optimization (WOA), and Gray Wolf Optimization (GWO) algorithms among optimization algorithms. Finally, in the last stage, the features obtained from the first and second stages were classified by using the machine learning methods Decision Tree (DT), Naive Bayes (NB), multi-class Support Vector Machine (mSVM), k-Nearest Neighbor (KNN), and Neural Network (NN). In the classification processes of the features obtained in the first stage, the mSVM model has achieved the highest classification success with 89.40%. In the second stage, as a result of the classifications performed through the active features selected by using three types of feature selection algorithms (BA, WOA, GWO), the classification success obtained with the mSVM model was 88.82%, 88.72%, and 88.95%, respectively. The classification accuracies of the tested methods and the classification accuracies obtained in the first stage are close to each other in terms of classification success. However, with the algorithms used in feature selection, successful classification processes have been carried out with fewer features and in a shorter time. The results of the study, in which classification was carried out in the inexpensive, the objective, and the shorter time of processing for the corn types, present a different perspective in terms of classification performance.

show abstract

“…Object detection of pests is one of the main tasks of plant pest detection, and the aim is to obtain accurate location and class information of pests, which can be well solved by CNN-based deep learning feature extractors and integrated models. Sabanci et al proposed a convolutional recurrent hybrid network combining AlexNet and BiLSTM for the detection of pest-damaged wheat [ 12 ]. Gambhir et al developed a CNN-based interactive network robot to diagnose pests and diseases on crops [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Maize-YOLO: A New High-Precision and Real-Time Method for Maize Pest Detection

Yang

Xing

Wang

et al. 2023

Insects

View full text Add to dashboard Cite

The frequent occurrence of crop pests and diseases is one of the important factors leading to the reduction of crop quality and yield. Since pests are characterized by high similarity and fast movement, this poses a challenge for artificial intelligence techniques to identify pests in a timely and accurate manner. Therefore, we propose a new high-precision and real-time method for maize pest detection, Maize-YOLO. The network is based on YOLOv7 with the insertion of the CSPResNeXt-50 module and VoVGSCSP module. It can improve network detection accuracy and detection speed while reducing the computational effort of the model. We evaluated the performance of Maize-YOLO in a typical large-scale pest dataset IP102. We trained and tested against those pest species that are more damaging to maize, including 4533 images and 13 classes. The experimental results show that our method outperforms the current state-of-the-art YOLO family of object detection algorithms and achieves suitable performance at 76.3% mAP and 77.3% recall. The method can provide accurate and real-time pest detection and identification for maize crops, enabling highly accurate end-to-end pest detection.

show abstract

A Novel Convolutional-Recurrent Hybrid Network for Sunn Pest–Damaged Wheat Grain Detection

Cited by 31 publications

References 34 publications

Sunn Pest Damaged and Healthy Wheat Grains Dataset Across Different Species

Sunn Pest Damaged and Healthy Wheat Grains Dataset Across Different Species

Computer-Aided Multiclass Classification of Corn from Corn Images Integrating Deep Feature Extraction

Maize-YOLO: A New High-Precision and Real-Time Method for Maize Pest Detection

Contact Info

Product

Resources

About