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Smart Farming based on Deep Learning Approaches 2

Smart Farming based on Deep Learning Approaches

引用

2nd Odisha international conference on Electrical Power Engineering, Communication and computing Technology, ODICON 2022

作者： Tripathy, Subhranshu Sekhar Tripathy, Niva Rath, Mamata Pati, Abhilash Panigrahi, Amrutanshu Dash, Manoranjan Driems Autonomous College Dept. of Computer Science and Engg. Odisha Cuttack India Siksha 'o' Anusandhan University Dept. of Computer Science and Engg. Odisha Bhubaneswar India Siksha 'o' Anusandhan University Faculty of Management Sciences Odisha Bhubaneswar India

ISBN: (纸本)9781665478397

A novel idea called 'smart farming' aims to increase the productivity and effectiveness of agriculture by utilizing cutting-edge information technologies. Using the most recent developments in automation, artificial intelligence, and networking, farmers are better able to keep an eye on every step of the process and apply exact treatments selected by machines with superhuman accuracy. With an expanding global population comes a rise in the need for both employment and food. The farmers' traditional practices fell short of meeting these demands. New automated methods were consequently proposed. These innovative techniques satisfied the world's food needs while also creating jobs for billions of people. For effective farm management, a variety of techniques are utilized, including IoT, cloud computing, AI, machine learning, deep learning, big data, etc. Deep learning is one of these, and it is a developing field of study for crop yield forecasting. Engineers, data scientists, and farmers are still developing methods to optimize the amount of human labor needed in agriculture. As key information sources improve, smart farming transforms into a learning system that grows smarter every day. Artificial neural network principles are used in a machine learning method known as deep learning. The depth of deep learning networks, which also allows them to detect latent structures in unlabeled, unstructured data, is what sets them apart from neural networks. Compared to ML techniques, deep learning networks that automatically extract features without human involvement have a considerable advantage. In this research, we suggest a recommendation model and an algorithm to assess crop yield in the upcoming year. We have contrasted the deep learning algorithm with the random forest machine learning algorithm. These algorithms have been the subject of a brief comparison. Python has been used to implement the suggested model. © 2022 IEEE.

关键词： Cloud computing

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Machine Learning for Climate Change Impact Assessment and Adaptation Planning 2

Machine Learning for Climate Change Impact Assessment and Ad...

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2nd IEEE international conference on Trends in Quantum computing and Emerging Business technologies, TQCEBT 2024

作者： Balakrishnan, T. Suresh Krishnan, Prabhakar Ebenezar, U. Samson Mohammed Nizarudeen, M. Kamal, N. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-Campus India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India

ISBN: (纸本)9798350384277

The presented research introduces a new advanced data analytics methodology for climate change impact assessment and adaptive planning that utilizes machine learning and deep-learning techniques. Quantitative analysis was performed over several databases such as GHCN, ERA5, National Centers for Environmental Prediction (NCEP) and JRA-55 in addition to CRU for a thorough assessment of the proposed methodology. In climate modeling, the Convolutional Neural Networks (CNN) showed better accuracy than ERA5 and JRA-55 which outperformed Global Historical Climatology Network (GHCN) with lower Mean Absolute Error (MAE), Root Mean Squared Error values. The Early Warning System, which was built using the LSTM network algorithm, had a higher accuracy of 0.90 and recall rate of 0.88 especially with JRA-55 proving its effectiveness in early detection for extreme weather events before they occur. The RL-based adaptive planning component performed well across all datasets, with ERA5 reaching the best feedback loop performance score of 94%. The precision agriculture optimization, which was enabled by Random Forest revealed different accuracy and AUC-ROC values across various datasets showing flexibility. Against international climate agreements, the overall alignment score was 0.78 with ERA5 being slightly better at 0.80 using Support Vector Machines (SVM). These quantitative assessments accentuate the practicality, efficacy, and applicability of this methodology in various datasets making it an apt tool for evidence-based decisions on climate resilience strategies at the global level. © 2024 IEEE.

关键词： Adversarial machine learning

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THE RESEARCH OF VERTICAL SEARCH ENGINE FOR agriculture

THE RESEARCH OF VERTICAL SEARCH ENGINE FOR AGRICULTURE

引用

The Second ifip international conference on computer and computing technologies in agriculture (CCTA2008)

作者： Weiying Li Yan Zhao~* Bo Liu Qiang Li College of Information Science and Technology agriculture University of HeBei Baoding 071001 China

Following rapid expansion of huge agriculture information body on the Web, the efficient agriculture information gathering on specified top becomes more and more important in search engine research

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RESEARCH ON agriculture DOMAIN META-SEARCH ENGINE SYSTEM

RESEARCH ON AGRICULTURE DOMAIN META-SEARCH ENGINE SYSTEM

引用

The Second ifip international conference on computer and computing technologies in agriculture (CCTA2008)

作者： Nengfu Xie~* Wensheng Wang Agricultural Information Institute The Chinese Academy of Agricultural Sciences Beijing China 100081

The rapid growth of agriculture web information brings a fact that search engine can not return a satisfied result for users’ *** this paper,we propose an agriculture domain search engine sy

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Segmentation of small animal computed tomography images using original CT values and converted grayscale values 7

Segmentation of small animal computed tomography images usin...

引用

7th ifip WG 5.14 international conference on computer and computing technologies in agriculture, CCTA 2013

作者： Ma, Guoqiang Li, Naixiang Wang, Xiaojuan Dept. of Computer Science & Information Engineering Tianjin Agriculture University Tianjin300384 China Tianjin Agriculture University Tianjin300384 China

ISBN: (纸本)9783642543432

Medical image segmentation is the foundation of normal and diseased tissue 3D visualization, operation simulation and visual operation. In this paper, we comprehensively use two values that represent the same object (body tissue), to segment by the same algorithm implementation. The original CT images are downloaded from the web source, the dog rib tumor CT scan image by GE medical systems, all the experiment of dataset of 312 thoracic CT scans. The core of the segmentation is k-means clustering algorithm. The segmentation process consisted of two phases: (1) convert CT value to JPG gray value or not use the original CT value as the data sets for clustering;(2) segmentation bone tissue using the new k-means clustering algorithm program which is implemented with MATLAB 2012a programming language and for two-dimensional data matrix directly. The experiment produced strikingly different results. These results may be indicating that not only the segmentation algorithm of CT image is important, but also the data for segmentation is important too. © ifip international Federation for Information Processing 2014.

关键词： computerized tomography

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A new navigation line extraction method for agriculture implements guidance system

A new navigation line extraction method for agriculture impl...

引用

6th international conference on computer and computing technologies in agriculture, CCTA 2012

作者： Li, Mingxuan Zhang, Man Huan, Haiyan Liu, Gang Key Laboratory of Modern Precision Agriculture System Integration Research Ministry of Education China Agricultural University Beijing 100083 China

ISBN: (纸本)9783642361364

To make up the shortages of the existing vision navigation algorithms such as the noise interference and the processing speed, a new approach for navigation line detection is presented in this article. This new method was based on image boundary detection. The color images were transformed into binary ones after pre-processing. Then, three stages in this method were presented. At first, the image was divided into several sectors and the boundary of the crops was detected. Then a number of points indicating the centers of the rows were determined. At last, the navigation line was estimated by an improved linear regression algorithm. Compared to other navigation line detection algorithms, this new method could find the navigation lines more accurately. It need only about 89ms to process a picture with 640x320. So it can meet the need of agricultural real-time visual navigation when the speed is 1~2m/s. © 2013 ifip international Federation for Information Processing.

关键词： computer vision

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Monitoring crop growth status based on optical sensor

引用

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

作者： Cui, Di Li, Minzan Zhu, Yan Cao, Weixing Zhang, Xijie China Agr Univ Minist Educ Key Lab Modern Precis Agr Syst Integrat Res Beijing 100083 Peoples R China Nanjing Agr Univ Minist Agr key Lab Crop Growth Regulat Nanjing 210095 Jiangsu Peoples R China

ISBN: (纸本)9780387772523

In order to detect the growth status and predict the yield of the crop, crop growth monitor measuring nitrogen content in the plant is developed based on optical principle. The monitor measures the spectral reflectance of the plant canopy at the 610 nm and 1220 nm wavebands, and then calculates the nitrogen content in the plant with the measured data. The field test was carried out to evaluate performance of the monitor. A portable multi-spectral radiometer named Crop Scan was used to measure the reflectance as a reference instrument. The result shows that the leaf reflectance measured by the monitor has a close linear correlation with that measured by Crop Scan at the 610 nm waveband (R2 = 0.7604), but the correlation between them is needed to be improved at the 1220 nm waveband. The hardware and the software of the monitor are also explained in detail. It is still need to be improved to satisfy the demand of ground-based remote sensing in precision farming.

关键词： nitrogen content crop growth ground-based remote sensing precision agriculture

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Greenhouse irrigation optimization decision support system 7

Greenhouse irrigation optimization decision support system

引用

7th ifip WG 5.14 international conference on computer and computing technologies in agriculture, CCTA 2013

作者： Zhang, Dongmei Guo, Ping Liu, Xiao Chen, Jinliang Jiang, Chong College of Water Resources & Civil Engineering China Agriculture University Beijing100083 China School of Computer Science and Technology Harbin Institute of Technology Harbin Heilongjiang150001 China

ISBN: (纸本)9783642543401

Greenhouse irrigation optimization decision support system (GDSS) is developed for the irrigation management of greenhouse crops in the northwest arid area of China. GDSS forecasts on relative yield and aids to develop irrigation schedule in terms of growth periods, comprehensively considering soil, crops and water supply conditions. The system consists of three modules. The database module stores all kinds of data using the Access database. The model module includes optimization models of greenhouse crops under insufficient irrigation based on uncertainty. These models are programmed by lingo, which can be invoked through internal interface. The man-machine dialogue module is designed with the principal of user control, user-friendly, visuality, usability, conciseness and uniformity. The GDSS can provide the decision makers the alternative decision making under uncertainty. © ifip international Federation for Information Processing 2014.

关键词： Decision support systems

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SwasthPhasal: An E-farming Web Portal 2

SwasthPhasal: An E-farming Web Portal

引用

2nd IEEE international conference on Advanced technologies in Intelligent Control, Environment, computing and Communication Engineering, ICATIECE 2022

作者： Saindane, Pallavi Bugtani, Siddarth Hiranandani, Yashvi Jagtap, Vishwesh Rajpal, Kabir VESIT Department Computer Engineering Mumbai India

ISBN: (纸本)9781665493963

agriculture has been India's main profession for centuries. According to the Indian Agricultural Research Institute, the need for edible grains will increase to 345 million tones in the next decade, however its role in India's GDP has recently declined. The current situation of farmers is disappointing as most of them live in severe poverty. They have to take loans to make ends meet and at times it becomes difficult to repay the loan. Our portal aims at providing crop seeds and fertilizers at cheap prices. The option of renting farming equipment would also be available. The cutting-edge technology which has been used over the past few years, has been lagging when it comes to farming. The main reason for this is both the disregard of these technologies and also the inaccessibility. Though farmers strive hard, even in this 21st century, they are bamboozled by third party retailers, which increases their poverty. In this covid pandemic, there was a huge demand for the agricultural products but it was difficult to get all the materials as well as information online. The solution to all the problems is Agro marketing which would make everything easy and secure. E-Farming provides a way for farmers to buy produce with only a basic knowledge of how to use the internet. This website is a centralized approach to guide farmers in all aspects and display current market prices of various products without brokers' cost. It will also include various government agricultural programs and access to new cultivation techniques. [2] The farmer will also be helped in the harvesting process by giving them a 6-day weather forecast and a schedule via SMS provided by the system. Along with this, they will be able to connect with agro health advisors to raise their queries regarding crop health and efficient farming techniques. © 2022 IEEE.

关键词： Portals

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STUDYED ON THE EXTERNALDEFECTS SEGMENTATION BASED ON THE COLOR CHARACTER OF POTATOES

STUDYED ON THE EXTERNALDEFECTS SEGMENTATION BASED ON THE COL...

引用

The Second ifip international conference on computer and computing technologies in agriculture (CCTA2008)

作者： Min Hao~1 Shuoshi Ma~(1 *) 1 College of Electrical and Mechanical Engineering Inner Mogolia agriculture University Huhot 010018 China

Potato quality detection in China remains at the stage of dependent on human sense organ to identify and *** to the characteristics and requests of potatoes’ detection,The original imag

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