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检索条件"任意字段=2nd IFIP International Conference on Computer and Computing Technologies in Agriculture"

共 694 条记录，以下是121-130 订阅

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Design of portable instrument for measuring agriculture field size based on GPS

引用

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

作者： Xiang, Xinjian Yang, Chunting Zhejiang Univ Sci & Technol Coll Automat & Elect Engn Hangzhou 310023 Peoples R China

ISBN: (纸本)9780387772523

Global Positioning System (GPS) plays an important role in precision agriculture. Applying positioning function of GPS, this paper researches on portable instrument for measuring field size. Instrument consists of low-cost GPS module, data acquisition board in. microcontroller, large capacity EEPROM storage, RS232C communication interface, keyboard and LCD display. GPS position information acquisition technology, method for calculating polygonal fields geometric. size, software and hardware design principles of instrument, GPS measuring error analysis and accuracy control of instrument are provided. Experiment results prove that measuring instrument can fast measure geometric size of fields in any shape, relative measuring error is less than 2.5%, and the bigger the size of field is, the higher the measuring accuracy is.

关键词： GPS field geometric size measuring instrument precision agriculture

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Disease Detection in C4 Plants using Machine Learning Approaches 2

Disease Detection in C4 Plants using Machine Learning Approa...

引用

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

作者： Singh, Souravdeep Sharma, Geeta Kumar, Ram Kumar, Adesh School of Computer Application Lovely Professional University Punjab Phagwara144411 India School of Computing Science and Engineering VIT Bhopal University Bhopal India School of Agriculture Lovely Professional University Phagwara India

ISBN: (纸本)9798331519056

Disease detection in crops is essential to maintaining food security and agricultural productivity. Plant disease identification is essential to maintaining crop health and increasing agricultural productivity. with the advancement of computational techniques like Machine learning is used for extracting insights from data, enabling automation, and making predictions in various farming domains. Recent assessments reveal that infections that develop on the leaves of plants are causing significant losses for 56% of the agriculture sector. Monitoring the disease's growth and increasing agricultural production is essential. We must first identify the condition early on and take action to stop it from propagating. Therefore, we could potentially able to resolve this challenge by implementing some algorithms for identifying diseases on leaflets. In this study, we investigate the efficacy of Convolutional Neural Networks (CNNs) for detecting diseases in two major crops: maize and sugarcane which lie under C4 plants. Utilizing a dataset located from Kaggle, comprising a diverse range of images depicting various disease symptoms, we trained and evaluated CNN models for both crops. The accuracy of disease detection achieved remarkable results, with 98% accuracy for maize and 100% accuracy for sugarcane. Our comparative analysis highlights the efficacy of CNNs in accurately identifying diseases in these crops, showcasing their potential for aiding farmers in timely disease management and enhancing agricultural yield. © 2024 IEEE.

关键词： C4 Plants Leaf diseases Machine Learning Maize Sugarcane

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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...

引用

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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An optimization genetic algorithm for image databases in agriculture

引用

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

作者： Zhu, Changwu Yan, Guanxiang Liu, Zhi Gao, Li Hua Zhong Normal Univ Dept Comp Sci Wuhan 430079 Peoples R China Wuhan Univ Sch Informat Management Wuhan 430072 Peoples R China Wuhan Junxie Shiguan Sch Wuhan 430079 Peoples R China

ISBN: (纸本)9780387772523

Data Mining is rapidly evolving areas of research that are at the intersection of several disciplines, including statistics, databases, pattern recognition, and high-performance and parallel computing. In this paper, we propose a novel mining algorithm, called ARMAGA (Association rules mining Algorithm based on a novel Genetic Algorithm), to mine the association rules from an image database, where every image is represented by the ARMAGA representation. We first take advantage of the genetic algorithm designed specifically for discovering association rules. Second we propose the Algorithm Compared to the algorithm in, and the ARMAGA algorithm avoids generating impossible candidates, and therefore is more efficient in terms of the execution time.

关键词： Association rules

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Leveraging DenseNet for Multi-Classification of Plant Leaf Diseases 2

Leveraging DenseNet for Multi-Classification of Plant Leaf D...

引用

2nd IEEE international conference on Advances in Information Technology, ICAIT 2024

作者： Bhatt, Chandradeep Thakur, Gesu Ansari, Mohammad Sadique Sawan, Vikash Singh, Teekam Taranath, N.L. Department of CSE Graphic Era Hill University Uttarakhand Dehradun India College of Smart Computing COER University Roorkee India Uttar Pradesh Barabanki India Department of Computer Engineering & Applications GLA University Uttar Pradesh Mathura India Graphic Era Deemed to be University Dehradun India School of CSE Presidency University Rajanukunte Bangalore India

ISBN: (纸本)9798350383867

To address the growing need for sustainable agriculture, it is crucial to explore and implement cutting-edge technologies that can boost crop productivity and minimize losses caused by plant diseases. Using the state of art technology in the deep learning and computer vision it is easy to analyses the leaf disease using the plant leaf images. This research explores the uses of computer vision technology using the DenseNet approach for accurately classify the various plant diseases. The aim of this research to assess the effectiveness of the DensNet approach in comparison with the novel Convolutional Neural Networks (CNN). By integrating DenseNet201 and a CNN classifier, the model employs transfer learning and deep neural networks to recognize plant leaf disease. The performance of the presented model is also measure in the term of accuracy of 98%. © 2024 IEEE.

关键词： CNN Deep Learning DenseNet Plant Leaf

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Crop Disease Prediction Using Classification Techniques 2

Crop Disease Prediction Using Classification Techniques

引用

2nd IEEE international conference on Advances in computing, Communication and Applied Informatics, ACCAI 2023

作者： Asif Jamal, G.A. Kalaiselvi, K. Sathiyavathi, V. Saraswathi, N. Ramesh, L. Sai Jbas College for Women Department of Botany Chennai India Saveetha College of Liberal Arts and Sciences Department of Computer Applications Chennai India Jbas College for Women Department of Computer Science Chennai India Anna University Centre for Distance Education Chennai India Anna University Department of Ist Ceg Chennai India

ISBN: (纸本)9798350315905

agriculture is the backbone of world economy and recent researchers working on improving the crop yield using recent technologies. This article is the one addresses the crop disease prediction using machine learning approaches which detects the disease in crop before it reaches the severity which may result in loss in crop yield. Machine learning and data mining approaches supports the farmers to identify the crop diseases by the features existent in the crop in early stage itself and make them to take necessary steps to reduce the diseases in the crop. This work targets the grass grub insect which creates loss in crop yield. The system uses different existent machine learning approaches and it combines this algorithm to evaluate the efficiency of prediction accuracy. The obtained results shows that the hybrid or ensemble approaches achieve more accuracy than individually used algorithms. © 2023 IEEE.

关键词： Crops

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Soil Image Classification and Crop Recommendation Using Convolutional Neural Networks 2

Soil Image Classification and Crop Recommendation Using Conv...

引用

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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Interactive agriculture relying on Artificial Intelligence and Convolution Neural Network 2

Interactive Agriculture relying on Artificial Intelligence a...

引用

2nd IEEE international conference on Intelligent and Innovative technologies in computing, Electrical and Electronics, ICIITCEE 2024

作者： Nandhini, J. Mohanapriya, D. Kalaiarasi, G. Lakshmipriya, N. Arunprasath, M.M. Jai Shriram Engineering College Department of Electronics and Communication Engineering Tamil Nadu Tiruppur India Vignan's Foundation for Science Technology & Research Department of Advanced Computer Science and Engineering Andrapradesh Guntur India Jai Shriram Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Tiruppur India Ksr Institute for Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Tiruchengode India

ISBN: (纸本)9798350306415

Crop production is more complicated than farming, as it involves a lot of labor and very specific humid conditions in both the soil as well as the atmosphere in which to grow them. Farming has been made easier by technology, such as smart irrigation and precise plant nutrition, but these systems have a drawback: they spray water uniformly on all crops, even if some of them need little. An agricultural smart spraying system minimizes the impact on the environment while targeting chemical spray applications. In smart sprayers, which in most cases also have a targeted detection and spraying system, the targeted sensor forms the basis of precision spraying management. Despite the fact that excess water can be sprayed, it is not recommended. A crop can be protected against being affected by possible rots if moisture sensors and float sensors are used as part of the Intelligent Spraying System. Using this technology, it is possible to stop crops from being destroyed by climate change. The crop won't be harmed by pest sickness because of using Convolutional Neural Network (CNN) algorithm. This paper examines the conventional techniques for identifying plant diseases and pests. agriculture productivity is seriously threatened by agricultural illnesses and insect pests. If pests are not found and identified quickly, they might result in considerable economic losses. So by using CNN those disease are classified in earlier stage and crops are being protected. © 2024 IEEE.

关键词： Artificial Intelligence CNN Image Processing Smart Irrigation

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Mobile WSN Testbed for agriculture: Plant Monitoring System 2

Mobile WSN Testbed for Agriculture: Plant Monitoring System

引用

2nd international conference On Emerging Computation and Information technologies, ICECIT 2017

作者： Meda, Nidhi S. Sadashiva, Thejas Gubbi Ramani, Sanjeev Kaushik Iyengar, S.S. Computer Science and Engineering Siddaganga Institute of Technology Tumkur India School of Computing and Information Sciences Florida International University United States

ISBN: (纸本)9781538610947

Wireless Sensor Network is having a potential to change the scope of new generation computing. Wireless Sensor Network (WSN) provides powerful means to gather the different kind of data and for data acquisition. It has a wide array of applications ranging from health monitoring to military. Our work focuses mainly on plant monitoring. In this paper, we propose the novel idea of designing mobile testbeds which are capable of moving randomly all along the agricultural field. We then concentrate on the technique to collect the data with the help of mobile testbeds at different locations in the agricultural field. Mobile testbeds collect and transmit the data from many locations at different time intervals. The combined data is analysed in the base station. Forecasting with regression is useful in predicting or forecasting the variable. Prediction about the desired variable is made by the technique called Regression model. Plant health monitoring also includes prevention against bacterial and fungal diseases. This paper also discusses the detection of disease and its name for a particular plant by using image processing techniques. Index terms: Mobile testbed, Forecasting with regression, Bilateral filtering, Canny edge detection, Contour method. © 2017 IEEE.

关键词： Testbeds

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Yield mapping in precision farming

引用

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

作者： Zhang, M. Li, M. Z. Liu, G. Wang, M. H. China Agr Univ Minist Educ Key Lab Modern Precis Agr Syst Integrat Res Beijing 100083 Peoples R China

ISBN: (纸本)9780387772523

Spatial variability of yield is very important in precision farming. With a yield monitor system installed on combine harvester, the yield data can be collected automatically while harvesting. In this paper, the component and the working theory of the yield monitor system were introduced. Performance of main sensors and the method improving GPS position accuracy were analyzed. Based on the analysis, a new grain yield monitor is developed and performance tested. The crop harvested was wheat, and the harvesting combine used to equip the monitor was JL 1603, a typical machine in northern China with 4 m of header width. And. The yield map was created to support decision making in precision agriculture.

关键词： precision agriculture yield monitor system grain flow sensor yield map

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