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<!DOCTYPE html>
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<title>Corn Seeds Dataset</title>
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<a class="navbar-brand" href="#page-top">Corn Seeds Dataset</a>
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<h2>About</h2>
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<h3>News and updates</h3>
<h3>Overview</h3>
<p>Welcome to the CornSeeds project! This is an ongoing research effort to provide agricultural researchers around the world with corn seed image data for training seeds <a>classification/detection </a>models. The data is available for free to researchers for non-commercial use.</p>
<h3>Can I download the images?</h3>
<p>Yes. For details, visit the link given below.
<br/>github.com/Naagar/Seeds_Classification </p>
<h3>Research Team</h3>
<p> <a href="https://naagar.github.io">Sandeep Nagar,</a> ML Lab, IIIT-Hyderabad</p>
<p> <a href="https://girishvarma.in">Prateek Pani,</a> ML Lab, IIIT-Hyderabad</p>
<p> <a href="https://adtechcorp.io/Leadership_raj.html">Raj Nair,</a> AdTech Corp.</p>
<p> <a href="https://girishvarma.in">Prof. Girish Varma,</a> CSTAR Lab and ML Lab, IIIT-Hyderabad</p>
<p>For students, advisors, and other contributors to the project, please see the list of publications below.</p>
<h3>Publications</h3>
<p>Nagar, S., Pani, P., Nair, R., Varma, G.,: Automated Seed Quality Testing System using GAN & Active Learning, In: PReMI 2021, ISI Kolkata</p>
<h3>Inside dataset folder</h3>
<p> Dataset (folder_name)</p>
<p>
<br/>Train
<br/> -broken
<br/> -discolored
<br/> -pure
<br/> -silkcut
<br/>Test
<br/> -broken
<br/> -discolored
<br/> -pure
<br/> -silkcut </p>
<h3>Sponsers</h3>
<p>We are grateful for support from Adtech and the International Institute of Information Technology (IIIT)-Hyderabad, which enabled this project.</p>
</div>
<div class="col-lg-4">
<h3>What is Corn Seed Dataset?</h3>
<p>This dataset is the images of corn seeds considering the top and bottom view independently (two images for one corn seed: top and bottom). There are four classes of the corn seed (<a>Broken-B</a>, <a>Discolored-D</a>, <a>Silkcut-S</a>, and <a>Pure-P</a>) 17802 images are labled by the experts at the AdTech Corp. and 26K images were unlablled out of which 9k images were labled using the Active Learning (BatchBALD)</p>
<p>We have created three different datasets: (1). Primary dataset: contains the 17802 images labeled by the experts. Top-view(8901) and Bottom-view(8901).
<br/>
<br/>(2). Dataset with fake images: We generated fake images using Conditional GAN (BigGAN) as follows: broken-2937, discolored-5823, pure-2937, and silk cut-5823 instances and added them into the train set to balance the data set.
<br/>
<br/>(3). Balanced dataset: In this case of adding newly captured images labeled using the Batch Active Learning method, new 9000 labeled images are added to the primary dataset. This new dataset contains 26,802 images split into train and validation set 80: 20, respectively. Contains the 17802 images and the 9K images labeled by the Active Learning (BatchBALD).</p>
<h3>Why Corn Seed Dataset?</h3>
<p>Machine vision for precision agriculture has attracted research interest in recent years. Plant health monitoring approaches are addressed, including weed, insect, and disease detection. With the success of DNNs, different methods have been proposed to tackle problems of corn seed classification. Fine-grained objects (seeds) are visually similar by a rough glimpse, and details can correctly recognize them in discriminative local regions. We hope that this dataset set focus of computer vision and machine learning researchers on the problems related to automation and agriculture.</p>
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<span class="name">download the dataset</span>
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<span class="skills">(1). Primary dataset: 17801 labeled images (unbalanced) <a href="https://india-data.org/dataset-details/53a0923f-02f2-4fa5-a933-de04907e0ed6">click here</a>
<br/>(2.) Dataset with fake images: primary dataset + fake images generated using BigGAN (Cond. GAN), 5K images for each class <a href="https://india-data.org/dataset-details/53a0923f-02f2-4fa5-a933-de04907e0ed6">click here</a>
<br/>(3). Balanced dataset(26802): 26802:- balanced and 9K labeled using Active learning (BatchBALD) <a href="https://india-data.org/dataset-details/53a0923f-02f2-4fa5-a933-de04907e0ed6">click here</a> </span>
<p>Image pre-processing python's code <a href="https://github.com/Naagar/Seeds_Classification/tree/master/seed_dataset">click here</a></p>
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<h3>Location</h3>
<p><a = href="http://mll.iiit.ac.in/">Machine Learning Lab</a>,
<br><a href="https://www.iiit.ac.in/">International Institute of Information Techonology (IIIT) Hyderabad</a>
<br/> and <a href="https://adtechcorp.io/"> AdTech Corp. </a></p>
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<p><a href="https://naagar.github.io">For any query, write a mail </a>[email protected]</p>
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Copyright © Sandeep Nagar 2021
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<h2>Sample images from the Primary dataset</h2>
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<img src="img/portfolio/sample_real.png" class="img-responsive img-centered" alt="">
<p>These are the sample images from the 17K image dataset labeled by the experts, including the top and bottom view of different seed</p>
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<h2>Fake corn seed mages</h2>
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<p>Overview of our proposal system: Flow model.</p>
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<h2>Real vs Fake</h2>
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<p>Left: Real images from the Primary dataset. Right: Fake images were generated using BigGAN. As we can see, the fake and real images are almost the same, and it is difficult to differentiate.</p>
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<h2>Fake Images Samples</h2>
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<img src="img/portfolio/heatmap_acc_cropped.png" class="img-responsive img-centered" alt="">
<p>Right: graph plotted for the train and test accuracy (DNN: resnet18), Left: Confusion matrix for the classes </p>
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<h2>Bar chart - 1</h2>
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<img src="img/portfolio/bar_chart_1.png" class="img-responsive img-centered" alt="">
<p>Bar graph ploted for the Primary dataset for all the classes, as from the graph we can say that the Primary dataset is imbalance and to Solve this problem we used the Fake image generation using the BigGAN. see the next bar graph for comparison.</p>
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<h2>Bar chart - 2</h2>
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<p>Bar graph plotted for the dataset with fake images for all the classes; from the graph, we can say that this dataset is relatively more balanced compared to the last bar graph for the Primary dataset.</p>
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