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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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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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Crop Disease Prediction Using Classification Techniques 2

Crop Disease Prediction Using Classification Techniques

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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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Smart Crop Recommender System Using Machine Learning 2

Smart Crop Recommender System Using Machine Learning

引用

2nd international conference on Recent Trends in Microelectronics, Automation, computing, and Communications Systems, ICMACC 2024

作者： Nokshangthemba Kumar, Saurabh Soloman, Shyam Sunder Jannu Baydeti, Nagaraju National Institute of Technology Nagaland Department of Computer Science and Engineering Nagaland Chumukedima797 103 India

ISBN: (纸本)9798350366570

agriculture is key to any country's economic growth. However, meeting food requirements of the present population due to the ever-rising human population, changing climatic conditions, and limited natural resources becomes a difficult problem. Smart agriculture is an innovative way of supporting farming by recommending the best crops to use and solving the current problems in maintaining farming sustainable. The classification machine learning algorithm is used to build the recommendation model. This paper's concept is a method of choosing appropriate crops that results in several favorable outcomes for agriculture and farmers. Since the crop yield can be increased, it will contribute to boosting our country's economy. Crops recommendation system for agriculture is based on various attributes such as crop type, soil composition, weather condition, soil pH and rainfall. The data have taken from the historic data which was collected from soil and water conservation and district agriculture office Nagaland and use supervised machine learning approaches to apply different ML algorithms. The effected of algorithm are measure on a various scale including Accuracy, Precision, Recall and F1-Score. The random forest outperformed in specifying the suitable crop with an impressive accuracy of 92.59%. © 2024 IEEE.

关键词： Smart agriculture

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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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Mobile WSN Testbed for agriculture: Plant Monitoring System 2

Mobile WSN Testbed for Agriculture: Plant Monitoring System

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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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Research on Quality Inspection Method of Digital Aerial Photography Results

Research on Quality Inspection Method of Digital Aerial Phot...

引用

The 4th ifip international conference on computer and computing technologies in agriculture and the 4th Symposium on Development of Rural Information(CCTA 2010)

作者： Xiaojun Wang~1 Yanling Li~1 Yong Liang~1 and Yanwei Zeng~2 1 School of Information Science & Engineering Shandong Agricultural University Taian Shandong 271018 P.R.China 2 State Supervision and Testing Center of Surveying and Mapping Product 198 2nd Section Renmin Road Chengdu 610081 China

Photography is the main access to obtain geospatial information,the quality of the results will directly affect the follow-up results of the quality of surveying and ***,we need a com

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1st ifip TC 12 international conference on computer and computing technologies in agriculture, CCTA 2007

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1st ifip TC 12 international conference on computer and computing technologies in agriculture, CCTA 2007

ISBN: (纸本)9780387772509

The proceedings contain 80 papers. The special focus in this conference is on computer and computing technologies in agriculture. The topics include: Parameterized computer aided design of stubble cleaner;regional country information service platform based on hybrid network;the short message management system based on J2ME;design and implementation of a mobile management system for campus server;a fast longest common subsequence algorithm for biosequences alignment;research on monitoring technology of digital reservoir;research on low altitude image acquisition system;a threshold-based similarity relation under incomplete information;web-based expert system of wheat and corn growth management;study of corn optimization irrigation model by genetic algorithms;analysis of virtual reality technology applications in agriculture;multicast in mobile ad hoc networks;the design of fruit automated sorting system;reduction in agricultural tax and the income growth of rural residents;study on the application of digital irrigation area system;GIS combined with MCE to evaluate land quality;an efficient new method on accurately estimating Galileo VPL;control systems in our daily life;development for breeding performance management system on pig farms;rice shape parameter detection based on image processing;representation and calculation method of plant root;an improved fast brain learning algorithm;ontology-based agricultural knowledge acquisition and application;role's functions in CMS;research on quality standard of rural information;implementation scheme of agricultural content management system;content share in CMS;an agent-based population model for China;how to develop rural informatization in china;study on suitability evaluation model of new maize varieties;research of sluice monitoring system based on GPRS and PLC;study on real-time video transportation for national grain depot;approach of developing spatial distribution maps of soil nutrients;information technology speed

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Health Monitoring System Using IoT 2

Health Monitoring System Using IoT

引用

2nd IEEE international conference on Device Intelligence, computing and Communication technologies, DICCT 2024

作者： Adhikari, Manoj Singh Ramola, Jyoti Chaudhary, Vimal Sharma, Mahakant Patel, Raju Joshi, Vikalp School of Electronics and Electrical Engineering Lovely Professional University Punjab India Electronics and Communication Engineering Graphic Era Hill University Dehradun India School of Agriculture Lovely Professional University Punjab India School of Computer Science Engineering Lovely Professional University Punjab India School of Electronics Engineering Vellore Institute of Technology Chennai India Sr Instrumentation Enginer Drillmec International Pvt LTD. Hyderabad India

ISBN: (纸本)9798350372847

This paper consists of a healthcare system in an Internet of Things environment, which aims to monitor patient's health signs. The system consists of sensors to capture data from the hospital. The data is gathered with the help of a heart rate sensor, a body temperature sensor, a carbon monoxide sensor, a room temperature sensor, and a carbon dioxide sensor. The integration of all sensors into the IoT, the system enables continuous monitoring of patients' vital signs. This provides the help for the detection of any abnormal health conditions or unfavorable room conditions in early stage. The developed scheme maintains a low error percentage, ensuring the accuracy and reliability of the system. The error percentage is kept within a certain limit, which is specified to be less than 5%. This indicates that the system performs effectively in capturing and transmitting the sensor data. © 2024 IEEE.

关键词： Temperature sensors

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Comparsion of multispectral reflectance with digital color image in assessing the winter wheat nitrogen status 2nd

引用

2nd ifip international conference on computer and computing technologies in agriculture, CCTA2008

作者： Jia, Liangliang Chen, Xinping Li, Minzan Cui, Zhenling Zhang, Fusuo College of Resources and Environmental Sciences China Agricultural University Beijing100094 China Institute of Agricultural Resources & Environment Hebei Academy of Agriculture and Forestry Sciences Shijiazhuang050051 China Key lab of Precision Agriculture China Agricultural University Beijing100083 China

ISBN: (纸本)9781441902108

Previous researches have shown that the digital image color intensity could reflect the crops N status, but there is little information about the comparision of spectrum reflectance in the visible bands with the digital imagery color intensities. A field experiment was conducted to compare the wheat canopy reflectance at visible bands (400-700 nm) at shooting stage with near ground digital image to detect N deficiencies. Single color bands of R, G, B and ratio indices of G/R, G/B, R/B, R/ (R+G+B), G/ (R+G+B) and B/ (R+G+B), which derived from digital image and spectral measurments, were regressed with wheat N status. The R, G, G/B, R/B, R/ (R+G+B) and G/ (R+G+B) all had negative correlations, while the G/R and B/ (R+G+B) indices had positive correlations, with plant N status. For the B band, the digital image analysis data got positive correlations while the spectral measurements got negative correlations. With higher correlation coefficient than other indices, the R/ (R+G+B) was the best index in this research. Considering the easiness of getting digital images and the accurate prediction of crops N status, the digital image analysis method seems to be a better way for in field plant N status evaluation. © 2009 by international Federation for Information Processing.

关键词： Crops

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Optimizing Agricultural Productivity: Developing a Smart Crop Recommendation System Using Machine Learning 2

Optimizing Agricultural Productivity: Developing a Smart Cro...

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2nd international conference on Recent Trends in Microelectronics, Automation, computing, and Communications Systems, ICMACC 2024

ISBN: (纸本)9798350366570

Crop recommendation is the crucial aspect of modern agriculture, aiming to assist farmers in selecting the most suitable crops for their land and maximizing yield. In this study, the effectiveness of various preprocessing techniques, grid search optimization and the pipeline class are applied in conjunction with the different machine learning algorithm for the crop recommendation. The accuracy and efficiency of the recommendation process enhancement is aimed by applying techniques including data standardization, relative density normalization, and grid search optimization. Leveraging Machine Learning algorithms within a unified pipeline framework allows for systematic evaluation and comparison of models, ultimately providing farmers with reliable recommendations for crop selection. Machine Learning algorithms Decision Tree, K-Nearest Neighbor, Random Forest, Support Vector Machine, Logistic Regression and Naïve Bayes were applied to predict and optimize agricultural productivity based on the dataset collected from Nagaland state. Among these algorithms, Random Forest outperformed the others, achieving an impressive accuracy of 99.54% in predicting agricultural productivity. © 2024 IEEE.

关键词： Smart agriculture

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