A novel deep learning based approach for seed image classification and retrieval

Loddo, Andrea; Loddo, M.; Ruberto, Cecilia Di

doi:10.1016/j.compag.2021.106269

Cited by 74 publications

(37 citation statements)

References 28 publications

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“…However, the scenario considerably changes when observing the results of the multiclass classification performance that we evaluated using the F-measure computed for all the classes, as indicated in Equation (6). In particular, Figure 5 shows that the best MFMs are generally lower than the accuracy on both data sets, even though the SVM reaches more than 99% of MFM on the Canada data set with ResNet50 and SeedNet descriptors and 96.07% with SeedNet-extracted features on Cagliari data set.…”

Section: Resultsmentioning

confidence: 99%

“…The works most related to ours belong to this category. In particular, Sarigu et al [5] performed a plum variety identification employing seed's endocarp shape, colour, and texture descriptors followed by a Linear Discriminant Analysis (LDA) to obtain the most representative features, while Przybylo et al [21] and Loddo et al [6] employed CNNs.…”

Section: Seed Detection and Classificationmentioning

confidence: 99%

“…Each original image contains a scale marker as a dimensional reference for the seed. It was removed following the preprocessing procedure proposed in [6]. The basic collection of the Banca del Germoplasma della Sardegna (BG-SAR), University of Cagliari, Italy, was used to create the local data set.…”

Section: Canada Data Setmentioning

confidence: 99%

“…Compared to manual analysis, the use of seed image analysis techniques brings several advantages to the process: In this work, we address the problem of multiclass classification of seed images from two different perspectives. First, we study possible optimisations of five traditional machine learning classifiers as adopted in our previous work [6], trained with seven different categories of handcrafted (HC) features extracted from seed images with our proposed ImageJ tool [19]. Second, we train several well-known convolutional neural networks and a new CNN, namely SeedNet, recently proposed in our previous work [6], in order to determine whether and to what extent a feature extraction performed from them may be advantageous over handcrafted features for training the same traditional machine learning methods depicted before.…”

Section: Introductionmentioning

confidence: 99%

“…This growth is mainly due to the fact that computer vision techniques have been combined with deep learning techniques. The latter has offered promising results in various application fields, such as haematology [ 2 ], biology [ 3 , 4 ], or botany [ 5 , 6 ]. Deep learning algorithms differ from traditional machine learning (ML) methods in that they require little or no preprocessing of images and can infer an optimal representation of data from raw images without the need for prior feature selection, resulting in a more objective and less biased process.…”

Section: Introductionmentioning

confidence: 99%

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On the Efficacy of Handcrafted and Deep Features for Seed Image Classification

Loddo

Ruberto

2021

J. Imaging

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Computer vision techniques have become important in agriculture and plant sciences due to their wide variety of applications. In particular, the analysis of seeds can provide meaningful information on their evolution, the history of agriculture, the domestication of plants, and knowledge of diets in ancient times. This work aims to propose an exhaustive comparison of several different types of features in the context of multiclass seed classification, leveraging two public plant seeds data sets to classify their families or species. In detail, we studied possible optimisations of five traditional machine learning classifiers trained with seven different categories of handcrafted features. We also fine-tuned several well-known convolutional neural networks (CNNs) and the recently proposed SeedNet to determine whether and to what extent using their deep features may be advantageous over handcrafted features. The experimental results demonstrated that CNN features are appropriate to the task and representative of the multiclass scenario. In particular, SeedNet achieved a mean F-measure of 96%, at least. Nevertheless, several cases showed satisfactory performance from the handcrafted features to be considered a valid alternative. In detail, we found that the Ensemble strategy combined with all the handcrafted features can achieve 90.93% of mean F-measure, at least, with a considerably lower amount of times. We consider the obtained results an excellent preliminary step towards realising an automatic seeds recognition and classification framework.

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Section: Resultsmentioning

confidence: 99%

Section: Seed Detection and Classificationmentioning

confidence: 99%

Section: Canada Data Setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the Efficacy of Handcrafted and Deep Features for Seed Image Classification

Loddo

Ruberto

2021

J. Imaging

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Deep Learning‐Based Classification of Image Data Sets Containing 111 Different Seeds

Tugrul,

Eryigit,

2023

Advcd Theory and Sims

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Image analysis plays a crucial role in understanding and protecting biodiversity. A wide variety of images are used in research on identifying and classifying plants, including stems, leaves, flowers, and fruits. In order to increase crop production, more research needs to be done on the image analysis of seeds. This study aims to fill the gap in this field by creating an image data set of 111 different species in 42 families. An improved Convolutional Neural Networks (CNNs) model is developed by adding new layers to the last layers of the well‐known CNNs in the literature. A well‐balanced image data set is used to train the proposed model and calculate its performance. The accuracy of the custom CNNs model for seed classification is between 91% and 94%. The custom model's top‐2 and top‐3 accuracy values are 98.56% and 98.92%, respectively. The proposed CNNs model shows encouraging results in terms of accuracy and computation time for seed classification and recognition.

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Maize seeds forecasting with hybrid directional and bi‐directional long short‐term memory models

Isik,

Tasdemir,

Taspinar

et al. 2023

Food Science & Nutrition

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The purity of the seeds is one of the important factors that increase the yield. For this reason, the classification of maize cultivars constitutes a significant problem. Within the scope of this study, six different classification models were designed to solve this problem. A special dataset was created to be used in the models designed for the study. The dataset contains a total of 14,469 images in four classes. Images belong to four different maize types, BT6470, CALIPOS, ES_ARMANDI, and HIVA, taken from the BIOTEK company. AlexNet and ResNet50 architectures, with the transfer learning method, were used in the models created for the image classification. In order to improve the classification success, LSTM (Directional Long Short‐Term Memory) and BiLSTM (Bi‐directional Long Short‐Term Memory) algorithms and AlexNet and ResNet50 architectures were hybridized. As a result of the classifications, the highest classification success was obtained from the ResNet50+BiLSTM model with 98.10%.

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A novel deep learning based approach for seed image classification and retrieval

Cited by 74 publications

References 28 publications

On the Efficacy of Handcrafted and Deep Features for Seed Image Classification

On the Efficacy of Handcrafted and Deep Features for Seed Image Classification

Deep Learning‐Based Classification of Image Data Sets Containing 111 Different Seeds

Maize seeds forecasting with hybrid directional and bi‐directional long short‐term memory models

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