Journal ArticleDOI
Performance of deep learning vs machine learning in plant leaf disease detection
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This article is comparing the performance of ML (Support Vector Machine, Random Forest), Random Forest, Stochastic Gradient Descent (SGD), & DL (Inception-v3, V GG-16, VGG-19) in terms of citrus plant disease detection as DL methods perform better than that of ML methods in case of disease detection.About:
This article is published in Microprocessors and Microsystems.The article was published on 2021-02-01. It has received 223 citations till now. The article focuses on the topics: Plant disease.read more
Citations
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Journal ArticleDOI
Machine learning in agriculture domain: A state-of-art survey
TL;DR: An extensive survey of latest machine learning application in agriculture to alleviate the problems in the three areas of pre-harvesting, harvesting and post-Harvesting is presented.
Journal ArticleDOI
Dense convolutional neural networks based multiclass plant disease detection and classification using leaf images
TL;DR: Experimental results proved that the proposed deep learning-based system can efficiently classify various types of plant leaves with good accuracy and signify its real-time performance.
Journal ArticleDOI
Artificial Intelligence to Improve the Food and Agriculture Sector
Rayda Ben Ayed,Mohsen Hanana +1 more
TL;DR: In this article, the authors report the importance of artificial intelligence and machine learning as a predictive multidisciplinary approach integration to improve the food and agriculture sector, yet with some limitations that should be considered by stakeholders.
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Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming
TL;DR: In this paper , the potential of ICT technologies in traditional agriculture, as well as the challenges that may arise when they are used in farming techniques are discussed, and a thorough review of the most recent literature in each area of expertise is presented.
Journal ArticleDOI
Tomato Leaf Diseases Classification Based on Leaf Images: A Comparison between Classical Machine Learning and Deep Learning Methods
TL;DR: This study generally aimed to identify the most suitable ML/DL models for the PlantVillage tomato dataset and the tomato disease classification problem and found that, for the dataset and classification task, the ResNet34 network obtained the best results.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
Posted Content
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
TL;DR: This work proposes a small DNN architecture called SqueezeNet, which achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters and is able to compress to less than 0.5MB (510x smaller than AlexNet).
Journal ArticleDOI
Using Deep Learning for Image-Based Plant Disease Detection
TL;DR: In this article, a deep convolutional neural network was used to identify 14 crop species and 26 diseases (or absence thereof) using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions.