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Artificial Neural Networks or Disease Diagnosis and Categorization in Citrus Plants 2

Artificial Neural Networks or Disease Diagnosis and Categori...

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2nd international conference on Electrical, Electronics, Information and Communication technologies, ICEEICT 2023

作者： Ashwini, S. Shanmuga Prabha, P. Krithika, V. Malathi, P. SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Akshaya College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350397635

Plant diseases are the leading cause of lost production and productivity in agriculture, which in turn leads to significant economic expenses and food insecurity. Citrus is a plant genus that may produce fruit that has a high market value and is thus cultivated and harvested in large quantities all over the world. The cultivation of citrus, however, has been significantly hampered by the presence of a number diseases, which has resulted in significant decreases in both productivity and quality. The detection and classification of plant diseases has been greatly aided by the development of machine learning and computer vision in recent years. This improvement came about in recent years. Although machine learning models make use of a wide variety of methods for identification and prediction, it would appear that study into the deep learning (DL) area offers the most potential for boosting accuracy as a result of the confluence of these two fields. This research makes use of a two- stage deep Convolutional Neural Network (CNN) model in order to analyse leaf images for the purpose of identifying plant diseases and classifying citrus diseases. Using a region proposal network, the proposed model will first identify potential target diseased areas, and then it will use a classifier to assign the most likely target area to the right disease class. These two fundamental steps are what make up the model. The model that was proposed has a precision that is averaged at 96% and an accuracy of detection that is 95%. © 2023 IEEE.

关键词： Neural network models

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Blockchain based Security Solution for Internet of Drones 2

Blockchain based Security Solution for Internet of Drones

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2nd international conference on Innovative Sustainable Computational technologies, CISCT 2022

作者： Maurya, Sudhanshu Niveditha, V.R. Anandapriya, B. Verma, Rohan Mirza, Kashish Tiwari, Mohit School of Computing Graphic Era Hill University Bhimtal Campus Nainital India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Patrician College of Arts and Science Department of Computer Application Chennai India Hemvati Nandan Bahuguna Garhwal University Department of Computer Science and Engineering Uttarakhand Srinagar India Graphic Era Hill University Bhimtal Campus Department of Computer Science and Engineering Nainital India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665474160

Nowadays, Drone usage is increasing faster than before. They have frequently been employed in civil services. In a number of sectors, including transportation, agriculture, building, and emergency response, drones have grown in significance. Drones can operate in places that are inaccessible to humans, collect data more effectively than conventional techniques, and carry out tasks more quickly. With the development of technology and laws, their use is anticipated to increase in the future. Therefore, it is imperative that drones must become smarter, more private, and more secure as they offer data transfer secrecy and reliability. In this paper, we have proposed a blockchain-based security solution model for the Internet of Drone environment. The proposed method is evaluated on a limited number of pretend drones. Here we may easily authenticate the administrator, add new drones, authenticate a drone, delete drones, get data from drones, save it to a blockchain-based secure system, and securely retrieve that information. © 2022 IEEE.

关键词： Drones

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Research on agricultural e-commerce public trade platform system

引用

1st international conference on computer and computing technologies in agriculture (CCTA 2007)

作者： Yang, Qifeng Feng, Bin Song, Ping Wuhan Univ Technol Econ Coll Wuhan 430070 Peoples R China

ISBN: (纸本)9780387772523

As we know, the decentralized management of agricultural production is inconsistent with the big market and circulation in our country. Facing this situation, we suggest strong enterprise construct third party agricultural public trade platform under the guide of the government in a way of market-based operation. The drive to build such platform from government, enterprises, peasant and agricultural organization was analyzed. The framework, function module and logic structure of the platform system were designed. In order to realize the sustainable development of the platform, we suggest the government should strengthen the function of the role in the platform construction and operation. Besides, we should improve the functional innovation, technological innovation and management innovation of the platform continuously to give full play to its economic and social benefit, and led the three sides that the government, peasant and enterprise to win together.

关键词： agriculture e-commerce trade platform system structure

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Comparative Analysis of Machine Learning and Deep Learning Algorithms for Plant Disease Classification and Detection 2

Comparative Analysis of Machine Learning and Deep Learning A...

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2nd international conference on Advanced computing and Communication technologies, ICACCTech 2024

作者： Kaur, Harminder Raheja, Neeraj Bindal, Amit Kumar Mullana Department of Computer Science and Engineering Haryana Ambala India

ISBN: (纸本)9798331519056

agriculture, a crucial foundation for human existence, exists at the intersection of conventional methods and modern innovations. Any country's primary issue in agriculture science is effective plant disease control, driven by the growing need for food due to population growth. Moreover, developments in modern technology have greatly improved the precision and effectiveness of plant detection. A critical challenge in the agricultural sector is leaf diseases, significantly impacting crop production and economic profit. Integrating digitalization and technology in agriculture is imperative for augmenting productivity and ensuring food security. This research emphasizes the principal concerns and obstacles associated with classifying leaf diseases. By experimenting with different approaches, researchers have already achieved great strides in the identification and categorization of illnesses. But due to assessments, new information, and discussions, changes are required. The resilience of deep learning (DL) and machine learning (ML) techniques, such as feed-forward neural networks (FFNN), k-means clustering (KMC), fuzzy logic (FL), artificial neural networks (ANN), and genetic algorithms (GA),so forth, has been demonstrated by earlier research. Due to their inherent ability to automatically obtain relevant visual information and understand spatial hierarchies, CNNs are usually the preferred option for image identification and classification among the ML and DL methods addressed in this study. The selection between machine learning and deep learning is dependent upon numerous issues, the data that is available, and the amount of processing power that is available. Because of this, DL - primarily using CNNs are advised for many complicated image detection and classification applications. They also show outstanding results for classification and detection on their datasets but not on others. Lastly, by using a variety of methods for processing images in the field of artif

关键词： Fuzzy neural networks

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Machine and Deep Learning-based Techniques for Precision agriculture with Comparative Analysis 2

Machine and Deep Learning-based Techniques for Precision Agr...

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2nd international conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Bhavana, Munugapati Rao, Koppula Srinivas Department of Computer Science & Engineering MLR Institute of Technology Affiliated to JNTUH Hyderabad India

ISBN: (纸本)9798350340235

Water and soil resources are crucial to agricultural growth and must be utilized more effectively. Precision agriculture (PA), an innovative approach in modern farming, integrates advanced technologies and data-driven methods to enhance agricultural practices and increase crop productivity while minimizing resource inputs and environmental impact. This review explores the many facets of precision agriculture, covering its methods, difficulties, and potential. This study attempts to provide the most recent data on PA technology and techniques for determining the most precise answer to diverse scenarios in agricultural operations. This study discusses research that has been done over the past five years (2018-2023) on approaches based on machine learning (ML) and deep learning (DL) for very accurate crop and weed yield predictions, as well as soil and weather forecasting. The analysis finishes with a discussion of precision agriculture's challenges and potential solutions. In PA, existing authors perform on the limited data and are more time-consuming. Issues with data quality, such as missing or inconsistent data, affect the reliability of analysis and predictions. And it affects the prediction accuracy. Unpredictable weather patterns can impact the effectiveness of specific PA evaluations. To overcome these issues, eliminate missing values and normalize the data using the min-max normalization method. And employ novel deep learning techniques such as Bi -directional convolutional long short-term memory and some optimization algorithms such as Northern Goshawk Optimization algorithm and Crayfish Optimization Algorithm to optimize the prediction parameters to achieve higher prediction accuracy with less computational time. This article describes how knowledge-based agriculture may raise output and product quality over the long run. © 2023 IEEE.

关键词： Comparative Analysis Crop Yield Prediction IoT-Based Techniques Precision agriculture Soil Properties

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Research on the Theory and Methods for Similarity Calculation of Rough Formal Concept in Missing-Value Context

Research on the Theory and Methods for Similarity Calculatio...

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第四届国际计算机与计算技术在农业中的应用研讨会

作者： Wang Kai Li Shao-Wen Zhang You-Hua Liu Chao School of Information and Computer Science Anhui Agricultural University

ISBN: (纸本)9783642183355

In this paper,for the low similarity computation accuracy of concept in the field of agriculture ontology mapping,formal concept analysis theory and rough set theory are introduced to similarity *** considering attribute hierarchies in concept lattice,the semantic hierarchy of the concepts are weighted differently,and the theory and methods of similarity calculation of rough formal concept in missing-value context is ***,similarity computing model is *** results show the model has a high computational accuracy.

关键词： Research on the Theory and Methods for Similarity Calculation of Rough Formal Concept in Missing-Value Context Concept

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Deep Learning Models for Gesture-controlled Drone Operation 16

Deep Learning Models for Gesture-controlled Drone Operation

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16th international conference on Network and Service Management (CNSM) / 2nd international Workshop on Analytics for Service and Application Management (AnServApp) / 1st international Workshop on the Future Evolution of Internet Protocols (IPFuture)

作者： Begum, Tahajjat Hague, Israat Keselj, Vlado Dalhousie Univ Dept Comp Sci Halifax NS Canada

ISBN: (纸本)9783903176317

Recently Unmanned Aerial Vehicles (UAVs) or Drones have gained enormous attention in applications like military, agriculture, industry, etc. One approach of controlling the operation of a drone is using hand gestures, which enables designing a low-cost system. However, the accuracy of such a system highly depends on the gesture recognition models. We can use a neural network-based gesture recognition model, which is a widely accepted image recognition scheme. In this work, we first design three deep neural network-based gesture recognition models: simple Convolutional Neural Networks (CNN), VGG-16, and ResNet-50 to uncover the best model for drone control. We evaluate the proposed models over our generated hand-gesture images in terms of their accuracy, precision, and complexity. The analysis reveals that each of the three models has its advantages and disadvantages while balancing between accuracy and complexity. For example, Simple CNN offers 92% accuracy on the testing set validation with the lowest validation loss compared to VGG-16 and ResNet-50. Thus, users can choose one of the proposed models to match their drone application.

关键词： Convolutional Neural Networks (CNN) Human-computer Interaction (HCI) Unmanned Aerial Vehicles (UAV)

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Soil Image Classification and Crop Recommendation Using Convolutional Neural Networks 2

Soil Image Classification and Crop Recommendation Using Conv...

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2nd international conference on computing and Data Analytics, ICCDA 2024

作者： Jasvanth, S.M. Fredrik, E.J.Thomson Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Karpagam Academy of Higher Education Department of Computer Technology Coimbatore India

ISBN: (纸本)9798331542573

In modern agriculture, leveraging advanced technologies for soil analysis and crop recommendation is imperative for sustainable and efficient farming practices. This research introduces a novel approach based on Convolutional Neural Networks (CNN) for soil image classification and subsequent crop recommendation. The proposed system aims to enhance precision in identifying soil types, thereby facilitating informed decisions on suitable crops. The first phase involves the acquisition of a comprehensive dataset comprising soil images with labeled attributes such as peat, sandy, loamy, chalk, clay, and silty soils. Subsequently, preprocessing techniques, including resizing and normalization, are applied to ensure uniformity in image dimensions and pixel values. This standardized dataset is then divided into training, validation, and testing sets. The CNN model is trained on the soil image dataset to automatically extract pertinent features for accurate soil classification. Upon successful training, the model enters the inference phase, where it can accurately predict soil types when presented with new soil images. Leveraging this soil classification, the system recommends suitable crops based on established correlations between soil types and optimal crops for cultivation. The proposed CNN-based approach offers a robust solution for automating soil analysis and crop recommendation, contributing to precision agriculture practices. The system's efficiency is validated through rigorous evaluation metrics, showcasing its potential to revolutionize decision-making processes for farmers in the agricultural sector. © 2024 IEEE.

关键词： Convolutional neural networks

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computer and computing technologies in agriculture 1

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丛书名： The international Federation for Information Processing 259

2008年

作者： Li, Daoliang 编

ISBN: (数字)9780387772530

ISBN: (纸本)9780387772523

This book constitutes the second volume of the proceedings of the First ifip WG 12.5 CCTA 2007 held in Wuyishan, China. The ifip series publishes state-of-the-art results in the sciences and technologies of information and communication. The scope of the series includes: foundations of computer science; software theory and practice; education; computer applications in technology; communication systems; systems modeling and optimization; information systems; computers and society; computer systems technology; security and protection in information processing systems; artificial intelligence; and human-computer interaction. Proceedings and post-proceedings of referred international conferences in computer science and interdisciplinary fields are featured. These results often precede journal publication and represent the most current research. The principal aim of the ifip series is to encourage education and the dissemination and exchange of information about all aspects of computing.

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Research of Fractal Compression Algorithm Taking Details in Consideration in agriculture Plant Disease and Insect Pests Image 9th

Research of Fractal Compression Algorithm Taking Details in ...

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9th ifip WG 5.14 international conference on computer and computing technologies in agriculture (CCTA)

作者： Deng, Qiao Liu, Chunhong Fu, Liting China Agr Univ Coll Informat & Elect Engn Beijing 10083 Peoples R China Beijing Engn & Technol Res Ctr Internet Things Ag Beijing 10083 Peoples R China

ISBN: (纸本)9783319483542;9783319483535

agriculture plant disease and insect pests is varied and tremendously harmful, so experts are needed to diagnose. But experts can't have the energy and time to the field for the majority of farmers to guide, they can only get the image through remote. However, agriculture plant disease and insect pests image is rich in detail, while transmission bandwidth and storage is limited, it is necessary to compress image to make sure of image quality. This paper proposes an improved method based on Jacquin theory to reduce coding time. Encoded sub block is classified into detailed block and non-detailed block, so we can reduce the encoding time. Experimental results show that, for the agriculture plant disease and insect pests image which is rich in detail, the number of encoded blocks reduces to 31.45 % and the encoding time reduces to 32.9 % of the original one.

关键词： Plant disease and insect pests image Detail Fractal encoding Image compression

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