A Deep Learning Approach for Determining Effects of Tuta Absoluta in Tomato Plants

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   Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification

   Sabbir Ahmed, Md. Bakhtiar Hasan, Tasnim Ahmed et al. · 2021 · Computer Vision and Pattern Recognition · Not summarized yet

   Abstract

   To ensure global food security and the overall profit of stakeholders, the importance of correctly detecting and classifying plant diseases is paramount. In this connection, the emergence of deep learning-based image classification has introduced a substantial number of solutions. However, the applicability of these solutions in low-end devices requires fast, accurate, and computationally inexpensive systems. This work proposes a lightweight transfer learning-based approach for detecting diseases from tomato leaves. It utilizes an effective preprocessing method to enhance the leaf images with illumination correction for improved classification. Our system extracts features using a combined m…

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   Explainable vision transformer enabled convolutional neural network for plant disease identification: PlantXViT

   Poornima Singh Thakur, Pritee Khanna, Tanuja Sheorey et al. · 2022 · Computer Vision and Pattern Recognition · Not summarized yet

   Abstract

   Plant diseases are the primary cause of crop losses globally, with an impact on the world economy. To deal with these issues, smart agriculture solutions are evolving that combine the Internet of Things and machine learning for early disease detection and control. Many such systems use vision-based machine learning methods for real-time disease detection and diagnosis. With the advancement in deep learning techniques, new methods have emerged that employ convolutional neural networks for plant disease detection and identification. Another trend in vision-based deep learning is the use of vision transformers, which have proved to be powerful models for classification and other problems. Howev…

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   Detection of healthy and diseased crops in drone captured images using Deep Learning

   Jai Vardhan, Kothapalli Sai Swetha · 2023 · Computer Vision and Pattern Recognition · AI summary available

   Abstract

   Monitoring plant health is crucial for maintaining agricultural productivity and food safety. Disruptions in the plant's normal state, caused by diseases, often interfere with essential plant activities, and timely detection of these diseases can significantly mitigate crop loss. In this study, we propose a deep learning-based approach for efficient detection of plant diseases using drone-captured imagery. A comprehensive database of various plant species, exhibiting numerous diseases, was compiled from the Internet and utilized as the training and test dataset. A Convolutional Neural Network (CNN), renowned for its performance in image classification tasks, was employed as our primary predi…

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   A comprehensive review on Plant Leaf Disease detection using Deep learning

   Sumaya Mustofa, Md Mehedi Hasan Munna, Yousuf Rayhan Emon et al. · 2023 · Computer Vision and Pattern Recognition · AI summary available

   Abstract

   Leaf disease is a common fatal disease for plants. Early diagnosis and detection is necessary in order to improve the prognosis of leaf diseases affecting plant. For predicting leaf disease, several automated systems have already been developed using different plant pathology imaging modalities. This paper provides a systematic review of the literature on leaf disease-based models for the diagnosis of various plant leaf diseases via deep learning. The advantages and limitations of different deep learning models including Vision Transformer (ViT), Deep convolutional neural network (DCNN), Convolutional neural network (CNN), Residual Skip Network-based Super-Resolution for Leaf Disease Detecti…

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   Detecting Volunteer Cotton Plants in a Corn Field with Deep Learning on UAV Remote-Sensing Imagery

   Pappu Kumar Yadav, J. Alex Thomasson, Robert Hardin et al. · 2022 · Computer Vision and Pattern Recognition · AI summary available

   Abstract

   The cotton boll weevil, Anthonomus grandis Boheman is a serious pest to the U.S. cotton industry that has cost more than 16 billion USD in damages since it entered the United States from Mexico in the late 1800s. This pest has been nearly eradicated; however, southern part of Texas still faces this issue and is always prone to the pest reinfestation each year due to its sub-tropical climate where cotton plants can grow year-round. Volunteer cotton (VC) plants growing in the fields of inter-seasonal crops, like corn, can serve as hosts to these pests once they reach pin-head square stage (5-6 leaf stage) and therefore need to be detected, located, and destroyed or sprayed . In this paper, we …

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   Masked autoencoders are scalable learners of cellular morphology

   Oren Kraus, Kian Kenyon-Dean, Saber Saberian et al. · 2023 · Computer Vision and Pattern Recognition · Not summarized yet

   Abstract

   Inferring biological relationships from cellular phenotypes in high-content microscopy screens provides significant opportunity and challenge in biological research. Prior results have shown that deep vision models can capture biological signal better than hand-crafted features. This work explores how weakly supervised and self-supervised deep learning approaches scale when training larger models on larger datasets. Our results show that both CNN- and ViT-based masked autoencoders significantly outperform weakly supervised models. At the high-end of our scale, a ViT-L/8 trained on over 3.5-billion unique crops sampled from 95-million microscopy images achieves relative improvements as high a…

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   Do Vision Transformers See Like Convolutional Neural Networks?

   Maithra Raghu, Thomas Unterthiner, Simon Kornblith et al. · 2021 · Computer Vision and Pattern Recognition · AI summary available

   Abstract

   Convolutional neural networks (CNNs) have so far been the de-facto model for visual data. Recent work has shown that (Vision) Transformer models (ViT) can achieve comparable or even superior performance on image classification tasks. This raises a central question: how are Vision Transformers solving these tasks? Are they acting like convolutional networks, or learning entirely different visual representations? Analyzing the internal representation structure of ViTs and CNNs on image classification benchmarks, we find striking differences between the two architectures, such as ViT having more uniform representations across all layers. We explore how these differences arise, finding crucial r…

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   An Ensemble of Convolutional Neural Networks to Detect Foliar Diseases in Apple Plants

   Kush Vora, Dishant Padalia · 2022 · Computer Vision and Pattern Recognition · AI summary available

   Abstract

   Apple diseases, if not diagnosed early, can lead to massive resource loss and pose a serious threat to humans and animals who consume the infected apples. Hence, it is critical to diagnose these diseases early in order to manage plant health and minimize the risks associated with them. However, the conventional approach of monitoring plant diseases entails manual scouting and analyzing the features, texture, color, and shape of the plant leaves, resulting in delayed diagnosis and misjudgments. Our work proposes an ensembled system of Xception, InceptionResNet, and MobileNet architectures to detect 5 different types of apple plant diseases. The model has been trained on the publicly available…