Journal ArticleDOI
Deep feature based rice leaf disease identification using support vector machine
TLDR
The simulation results show the deep feature plus SVM perform better classification compared to transfer learning counterpart, and the F1 score of CNN classification models was compared with other traditional image classification models.About:
This article is published in Computers and Electronics in Agriculture.The article was published on 2020-08-01. It has received 208 citations till now. The article focuses on the topics: Local binary patterns & F1 score.read more
Citations
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Journal ArticleDOI
Machine Learning Applications for Precision Agriculture: A Comprehensive Review
TL;DR: In this paper, the authors present a systematic review of ML applications in the field of agriculture, focusing on prediction of soil parameters such as organic carbon and moisture content, crop yield prediction, disease and weed detection in crops and species detection.
Journal ArticleDOI
Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach
TL;DR: The accuracy results in the identification of diseases showed that the deep CNN model is promising and can greatly impact the efficient identification of the diseases, and may have potential in the detection of diseases in real-time agricultural systems.
Journal ArticleDOI
Do we really need deep CNN for plant diseases identification
TL;DR: The results show that the SCNN-KSVM andSCNN-RF outperform other pretrained deep models on the indicators of precision, recall, and F1-score, with fewer parameters.
Journal ArticleDOI
Meta-learning baselines and database for few-shot classification in agriculture
Yang Li,Yang Li,Jiachen Yang +2 more
TL;DR: In this paper, the first work of task-driven meta-learning few-shot classification in the field of agriculture to the best of knowledge is presented, in which the authors collected samples from publicly available resources to assemble a comprehensive dataset for the fewshot classification, covering both pests and plants to analyze the single domain or cross-domain.
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.
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Posted Content
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: Faster R-CNN as discussed by the authors proposes a Region Proposal Network (RPN) to generate high-quality region proposals, which are used by Fast R-NN for detection.
Proceedings ArticleDOI
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
TL;DR: RCNN as discussed by the authors combines CNNs with bottom-up region proposals to localize and segment objects, and when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost.