Advanced Deep Learning Models for Corn Leaf Disease Classification: A Field Study in Bangladesh by Sachi Nandan Mohanty, Hritwik Ghosh, Irfan Sadiq Rahat, Chirra Venkata Rami Reddy

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Attention-enhanced corn disease diagnosis using few-shot learning and VGG16 by Ruchi Rani, Jayakrushna Sahoo, Sivaiah Bellamkonda, Sumit Kumar

“…The proposed work uses a pre-trained convolution neural network, VGG16, as the backbone, fine-tuned on the corn disease dataset. An attention module is integrated with the backbone, and further, prototypical few-shot learning is used for corn disease prediction and classification with an accuracy of 98.25 %. • The proposed model intends to identify the diseases early, so the insights generated would be relevant for farmers, and probable losses can be reduced. • By applying Few-Shot Learning, the system avoids the significant challenges of requiring extensively annotated datasets, making it feasible for real-world agricultural applications. • Incorporating a fine-tuned VGG16 backbone along with an attention mechanism and prototypical Few-Shot Learning results in a robust and scalable solution with high accuracy for classifying corn diseases.…”
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Comparison of MobilenetV2 and EfficiennetB3 Method to Classify Diseases on Corn Leaves by Riyadi Slamet, Mulya Rezka, Nabila Realisti Aulia

“…Classification of types of corn leaf disease is needed so that farmers can distinguish types of corn leaf disease. …”
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Assessing data and sample complexity in unmanned aerial vehicle imagery for agricultural pattern classification by Linara Arslanova, Sören Hese, Marcel Fölsch, Friedemann Scheibler, Christiane Schmullius

“…It has been confirmed that to exploit texture information for classification (at smaller sample patch sizes < 120 pixels), Ground Sampling Distances (GSDs) between 0.027 m and 0.064 m (for RGB and CIR sensors of commercial drones, respectively) are suitable for capturing detailed patterns of Corn and Spring Barley. …”
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Case Series of Canine Myasthenia Gravis: A Classification Approach With Consideration of Seronegative Dogs by Rui Xavier Dos Santos, Jan Waelkens, Abbe H. Crawford, Sam Khan, Sara Sami, Sergio A. Gomes, Anouk Van Ham, Iris Van Soens, Ine Cornelis, Jake Canning, Joe Fenn, Patrick Waters, Sofie F. M. Bhatti, An E. Vanhaesebrouck

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Decision-Level Multi-Sensor Fusion to Improve Limitations of Single-Camera-Based CNN Classification in Precision Farming: Application in Weed Detection by Md. Nazmuzzaman Khan, Adibuzzaman Rahi, Mohammad Al Hasan, Sohel Anwar

“…These weed species are frequently encountered within corn fields. The test results showed that the re-trained VGG16 with a transfer-learning-based classifier exhibited acceptable accuracy (99% training, 97% validation, 94% testing accuracy) and inference time for weed classification from the video feed was suitable for real-time implementation. …”
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The impact of methylome analysis on the diagnosis and treatment of CNS tumours in children and adolescents: A population-based study in Greece by Maria Filippidou, Stavros Glentis, Ilona Binenbaum, Martin Sill, Kleoniki Roka, Antonia Vlachou, Georgia Avgerinou, Jonas Ecker, Florian Selt, Martin Hasselblatt, Mirjam Blattner-Johnson, Kathrin Schramm, Clio Trougkou, Dimitrios Doganis, Nikolaos Katzilakis, Vita Ridola, Evgenia Papakonstantinou, Vassilios Papadakis, Emmanouel Hatzipantelis, Eleftheria Kokkinou, Roser Pons, Christina Kanaka-Gantenbein, Dominik Sturm, Steffen Hirsch, Nicola Dikow, Kristian W. Pajtler, Cornelis M. van Tilburg, Michael C. Frühwald, Till Milde, Olaf Witt, David T.W. Jones, Andreas Von Deimling, Felix Sahm, Kalliopi Stefanaki, Stefan M. Pfister, Antonis Kattamis

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CropsDisNet: An AI-Based Platform for Disease Detection and Advancing On-Farm Privacy Solutions by Mohammad Badhruddouza Khan, Salwa Tamkin, Jinat Ara, Mobashwer Alam, Hanif Bhuiyan

“…To classify corn, potato, and wheat leaf diseases, we used three representative CNN models for image classification (VGG16, Inception Resnet V2, Inception V3) along with our custom model, and the classification accuracy for these three different crops varied from 92.09% to 98.29%. …”
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Determination of agricultural land use: incidence of atmospheric corrections and the implementation in multi-sensor and multi-temporal images by E. Willington, J. P. Clemente, M. Bocco

“…This situation makes that detailed and updated information is needed for many applications. Remote sensing provides data of large areas periodically, so it becomes a useful input to soil classification. …”
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Genotyping by sequencing reveals the genetic diversity and population structure of Peruvian highland maize races by Carlos I. Arbizu, Carlos I. Arbizu, Isamar Bazo-Soto, Joel Flores, Rodomiro Ortiz, Raul Blas, Pedro J. García-Mendoza, Ricardo Sevilla, José Crossa, Alexander Grobman

“…Peruvian maize germplasm needs further investigation with modern technologies to better use them massively in breeding programs that favor agriculture mainly in the South American highlands. …”
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