Crop Disease Prediction using Deep Convolution Neural Network
نویسندگان
چکیده
منابع مشابه
Learning Traffic as Images: A Deep Convolution Neural Network for Large-scale Transportation Network Speed Prediction
This paper proposes a convolution neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with high accuracy. Spatiotemporal traffic dynamics is converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. CNN is applied to the image following two consecutive steps: abstract traf...
متن کاملA Survey on Crop Prediction using Back Propagation Neural Network
The aim of this research is to develop a farmer prediction system to identify crop suitable for particular soil. To achieve this Neural Network should be trained to perform correct prediction for farmers. After the network has been properly trained, it can be used to identify the crop suitable for particular type of soil. The Artificial neural networks are relatively crude electronic networks o...
متن کاملCrop Prediction System Using Enhanced Back Propagation Neural Network
The intend of this research is to develop and enhance a prediction system. This prediction system is useful to predict the crop that is suitable for a particular kind of soil. To realize it Neural Network is trained first in order to make accurate predictions. After training, network is capable of identifying the proper crop for particular soil. An Artificial neural networks is a simple electro...
متن کاملMEC: Memory-efficient Convolution for Deep Neural Network
Convolution is a critical component in modern deep neural networks, thus several algorithms for convolution have been developed. Direct convolution is simple but suffers from poor performance. As an alternative, multiple indirect methods have been proposed including im2colbased convolution, FFT-based convolution, or Winograd-based algorithm. However, all these indirect methods have high memory-...
متن کاملNovel Deep Convolution Neural Network Applied to MRI Cardiac Segmentation
In this paper, we propose a fully automatic MRI cardiac segmentation method based on a novel deep convolutional neural network (CNN) designed for the 2017 ACDC MICCAI challenge. The novelty of our network comes with its embedded shape prior and its loss function tailored to the cardiac anatomy. Our model includes a cardiac centerof-mass regression module which allows for an automatic shape prio...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Psychology and Education Journal
سال: 2021
ISSN: 0033-3077
DOI: 10.17762/pae.v58i1.1494