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Potato Plant Disease Detection and Classification for Improved Agriculture

Potato Plant Disease Detection and Classification for Improv...

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Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： Sarthak Chaudary Khushi Bakshi S. Iniyan Department of Computer Science and Engineering SRM Institute of Science and Technolgy Kaatankulathur Chennai Tamilnadu India Department of Computing Technologies School of Computing SRM Institute of Science and Technolgy Kaatankulathur Chennai Tamilnadu India

Agriculture is evolving, and deep learning techniques, especially convolutional neural networks, play a pivotal role. They streamline disease detection, providing farmers with timely information to deploy effective disease control strategies and amplify crop yields. Imagine a world where diseases in plants are detected with unparalleled speed and precision. In the age of technological advancement, agriculture stands at the brink of transformation. The proposed work delves into the world of innovation, by harnessing the power of cutting-edge technologies to safeguard crops. Embarking on a journey where image processing, artificial intelligence, and machine learning join forces to become the guardians of the farmlands. This is the vision that drives the project. The magic of image processing techniques, enabling to swiftly identify and categorize leaf diseases. As a result, farmers can now take immediate, well-informed actions, preventing their crops from getting plagued by diseases and ensuring a healthy yield of crops. The project introduces artificial intelligence and machine learning as formidable allies in the quest to diagnose crop diseases at an early stage. These intelligent algorithms detect disease symptoms even before the human eye can perceive them. These models, powered by convolutional neural networks, meticulously analyze leaf images, extract essential features, and classify diseases with remarkable accuracy. By fusing image processing and deep learning, the approach provides a holistic solution to the complex task of plant disease detection in agriculture.

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A feature extraction with pose-auxiliary method for Person Reidentification 2

A feature extraction with pose-auxiliary method for Person R...

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2nd international conference on Advanced Algorithms and Signal image processing, AASIP 2022

作者： Ma, ZePing Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu610054 China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu610054 China

ISBN: (纸本)9781510660571

Person re-identification is widely considered as a sub problem of image retrieval. It is a technology that uses computer vision technology to determine whether there is a specific pedestrian in an image or video, given a monitored pedestrian image, the pedestrian image under the cross device is retrieved. Many researches have been made to study how to match one people from two picture, but how to locating proper body parts has become a problem which could lead to misalign and wrongly divided sample. To solve this problem, we introduce a pose-auxiliary network to extract the position feature of pedestrian. We use a post estimation network to estimate the person's body location, then we decide whether it's a body part or a static obstacle by using a separated trained classifier. Then we compare these features from two picture and calculate their loss. Meanwhile, we use a graph-matching strategy to compare two pedestrians pose structure, which is also used into jugging the match problem. By using a multi-verification strategy, our method has some improvement than some previous study using Market-1501 and DukeMTMC-Reid datasets, but still remains room for improvement. © 2022 SPIE.

关键词： image retrieval

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Target detection and recognition of radar spectrum image based on deep learning 2

Target detection and recognition of radar spectrum image bas...

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2nd international conference on computer vision, image, and Deep Learning

作者： Zou, Hanyu Chen, Xin Wang, Chenxi He, Song Tang, Xiao School of Air Force Early Warning Academy Wuhan430014 China

ISBN: (数字)9781510646827

ISBN: (纸本)9781510646810

The vehicle-mounted millimeter-wave radar has been widely used in modern vehicles. The radar needs accurate feedback on the information about the surrounding environment, and the driverless cars also need to have extremely high ***, it is of great significance to enhance the ability of the vehicle-mounted millimeter-wave radar to detection of the surrounding targets. Traditional vehicle-mounted millimeter-wave radar usually signals the target echo signal and finally get the target information. In the signal processing process, it is greatly affected by the weather environment, so it is difficult to ensure a sustainable high detection rate. Therefore, a deep learning-based radar spectrum target detection method is proposed to quickly identify the objects present in the echo spectrum. First, the vehiclemounted millimeter radar spectrum mat format data is extracted, make the dataset for training, then adjust and optimize the SSD (Single Shot Multibox Detector) model according to the situation of the data set, and identify the spectral image using the optimized a training model. In the experiment, 11635 pictures were divided into training set and test set at 9:1, and the average mAP of various targets reached 94.35%, thus detecting the radar echo spectrum based on deep learning and achieving good results. The proposed spectral target detection method can be applied to vehicle-mounted millimeterwave radar. © 2021 SPIE.

关键词： Vehicles

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Implementation of Customized Convolutional Neural Networks for Handwritten Marathi Character Recognition

Implementation of Customized Convolutional Neural Networks f...

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international conference on Signal processing, Communication, Power and Embedded System (SCOPES)

作者： Shazid Wahid Khandakhani Sachikanta Dash Sasmita Padhy Rabinarayan Panda Department of CSE GIET University Odisha India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

In the field of Generative AI there are different models that have been applied to train and test the systems to perform the task such as pattern recognition, computer vision, and speech recognition out of which recognizing character is one of the major areas. In this research we have collected our own dataset and published at Mendely Data like MHCD_GIETV1 and MHCD_GIETV2. Initially we have collected samples of Bara-kadhi and implemented deep learning approach like InceptionV3 and Resnet. Comparing both the methods we found InceptionV3 is better among them. Further, we have implemented Customized Convolutional neural networks model with Convolutional Layers. which combines the operations of customizing the model. We have increased the number of hidden layer and extended our epochs and obtained accuracy of 96%. Our research contribution was to provides the valuable resources for future research on other languages, sentiments analysis as well as image recognition.

关键词： Deep learning Sentiment analysis Accuracy image recognition Embedded systems Generative AI Speech recognition Convolutional neural networks Character recognition Optimization

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Classification of Satellite images and Predicting Field Areas after Fine- Tuning Using Sequential CNN Model

Classification of Satellite Images and Predicting Field Area...

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international conference on Futuristic Technologies (INCOFT)

作者： Kanwarpartap Singh Gill Vatsala Anand Rahul Chauhan Ashish Garg Rupesh Gupta Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science and Engineering Graphic Era Hill University Punjab India Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

The categorization of satellite images is a discipline within the domains of remote sensing and computer vision. It is the process of assigning pixels or areas within satellite imagery to predetermined classifications or categories related to land cover or land use. The aforementioned procedure is of utmost importance in the extraction of meaningful information from international Earth observation data for the research development. The categorization of satellite images is dependent upon the use of data obtained from Earth-observing satellites. The satellites in question are designed to acquire electromagnetic radiation, including various wavelengths such as visible, infrared, and microwave, which is either reflected or emitted by the Earth's surface. Multispectral or hyperspectral sensors provide the acquisition of pictures including several spectral bands, hence enabling the retrieval of intricate data pertaining to the Earth's surface. The present research employs a suggested Sequential Convolutional Neural Network (CNN) model to classify the Satellite image. The graphics processing unit (GPU) is used for the purpose of doing data pre-processing. The Deep Learning model is then visualised via a study of the Loss and Accuracy curves. The suggested model is used to forecast the geographical coverage in four distinct categories, namely cloudy, desert, green area, and water respectively which is essential for ecosystem management.

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A Leaf Disease Classification Model in Betel Vine Using Machine Learning Techniques 2

A Leaf Disease Classification Model in Betel Vine Using Mach...

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2nd international conference on Robotics, Electrical and Signal processing Techniques, ICREST 2021

作者： Hasan, Md Zahid Zeba, Nahid Malek, Abdul Reya, Sanjida Sultana Daffodil International University Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665415767

Betel vine leaves diseases caused by regular endangerment to bacteria which causes a huge yield loss globally. Machine learning, the latest breakthrough in computer vision, is encouraging for fine-grained disease classification, as the method uses SVM classifier and Gaussian mixture model for image segmentation. Disease detection and classifications are considered as the two hardest works to the recognition of Betel vine disease. Two types of betel vine diseases are focused on the paper, Bacterial Leaf Spot and Stem Leaf. Pictures are taken using a phone camera or any kind of portable device and the dataset consists of almost 1275 images where each class contains 636 images. The proposed system reaches 83.69% accuracy in classification which appears to be good and promising in comparison to other relevant papers. © 2021 IEEE.

关键词： computer vision

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Diabetic Retinopathy Detection using Squeezenet

Diabetic Retinopathy Detection using Squeezenet

引用

Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： K. Nanthini D. Sivabalaselvamani K.S. Sharvanthika Shubham Kumar Ojha M. Siva Department of Computer Applications Kongu Engineering College Perundurai Erode India

Diabetic retinopathy (DR), which causes lesions on the retina and impairs vision, is a common complication of diabetes mellitus. It can cause blindness if it is not caught in time. With early identification and treatment of DR, the risk of vision loss can be considerably decreased. Ophthalmologists must manually diagnose DR retina fundus images, which is more expensive, time-consuming, and error-prone than computer-aided diagnosis technologies. Recently, deep learning has become one of the most well-liked techniques for enhancing performance, especially in classifying and interpreting medical pictures. Deep learning models can detect specific lesions or abnormalities associated with diabetic retinopathy such as microaneurysms, hemorrhages, exudates, and neovascularization. By training CNNs on annotated retinal images with labeled lesions, these models can learn to identify and localize the presence of these abnormalities, aiding in early detection and diagnosis. The images have been converted to grayscale using an image batch processor and the images are resized are the pre-processing steps. Using deep learning algorithms like Squeezenet, diabetic retinopathy images are classified into No DR indications, mild NPDR, moderate NPDR, Severe NPDR, extremely severe NPDR, PDR, and Advanced PDR cases. Squeezenet produced an accuracy of 94.7% without augmentation and had an accuracy of 97 percent. Then the pre-processed images are undergone.

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High-Speed Data Transfer using AXI Protocol for image Detection

High-Speed Data Transfer using AXI Protocol for Image Detect...

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Innovation in Technology (INOCON), IEEE international conference for

作者： Rajat Raj Sharat Kumar Miskin Naman Bhatia Electronics and Communication Engineering RV College of Engineering Bengaluru India

Convolutional neural networks (CNN) due to their excellent accuracy, have emerged as the leading machine learning method. However, it becomes difficult to deploy CNN algorithms on hardware platforms because of their high memory bandwidth, computational complexity, and power consumption. Hardware accelerators such as Field Programmable Gate Arrays (FPGA), Graphics processing Unit (GPU), etc. are excellent platforms for modelling CNN algorithms. The purpose of the paper is to run a standard software process in hardware and exploit natural parallelism in hardware to speed up neural network prediction. The paper presents a rule-based synthesizable Advanced Microcontroller Bus Architecture (AMBA) Advanced eXtensible Interface (AXI) protocol for high-speed data transmission. The paper develops a computer vision system on an FPGA that could recognize the numbers 0 through 9 written by hand for the capstone design project. The findings show that 91% accuracy is achieved through Rectified linear activation unit (ReLU) which can be increased to 98% by implementing the Sigmoid activation function with a depth of 10 bits. The method provides good accuracy and clock performance while using few resources.

关键词： Protocols Machine learning algorithms Computational modeling Software algorithms Neural networks Graphics processing units Parallel processing

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An image processing Method: Edge-guided Directional Walk Mean Smoothing 2

An Image Processing Method: Edge-guided Directional Walk Mea...

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2020 2nd international conference on computer, Communications and Mechatronics Engineering, CCME 2020

作者： Wang, Chengxue Qian, Wenhua Xu, Dan Li, Ke Pu, Yuanyuan School of Information Science and Engineering Yunnan University Kunming China

image denoising is an important computer vision task, it is also the basis of many other high-level vision tasks. Recently, various image denoising algorithms have proposed, such as BM3D, sparse representation and deep learning, that have achieved impressive performance in AWGN denoising or blind denoising tasks. However, when dealing with Dongba painting (A traditional painting from China's ethnic minorities), there are problems that Dongba painting have blurred edges and faded colors, it is important to retain its original style when denoising. In this paper, we propose a novel image post-processing technique termed edge-guided directional walk mean smoothing (EDWMS), it can effectively calculate the mean of pixels along different directions then obtain denoised image with sharp edge. Experiments show that the proposed EDWMS outperforms compared algorithms in both edge sharpness and color saturation. © Published under licence by IOP Publishing Ltd.

关键词： image denoising

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High precision autocollimation measurement technology based on image recognition 2

High precision autocollimation measurement technology based ...

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2nd international conference on Computing and Data Science, CDS 2021

作者： Zhongtang, Li Mingyao, Liu Jianfeng, Lu Shanghai Normal University TIANHUA College Shanghai China Wuhan University of Technology Wuhan China Tongji University Shanghai China

ISBN: (纸本)9781665404280

Autocollimator is an important optical detection instrument, which is mainly used to measure small angle, and plays an important role in the field of optical equipment manufacturing and debugging. For many years, the autocollimation accuracy of collimator has been one of the factors affecting its performance. In order to improve the autocollimation accuracy of collimator optical axis, an auxiliary calibration method based on image recognition is proposed in this paper. And the concrete realization idea of this method is given. The accuracy of this method to detect and identify the optical axis eccentricity error can reach 0.1 pixel. Taking the detection data as reference to adjust the optical axis, the autocollimation accuracy of collimator optical axis can be greatly improved. © 2021 IEEE.

关键词： computer vision

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