Research on Crop Disaster Stress Risk Mapping System Based on Agriculture Big Data

Mi, Chunqiao; Peng, Xiaoning; Peng, Ling; Zhao, Chunjiang; Deng, Xiaowu

doi:10.1109/iceitsa54226.2021.00105

Cited by 6 publications

(4 citation statements)

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“…Before you can use site-specific weed boards efficiently, you will first need to collect data on the frequency of weed infection on a per-field basis. Every harvest requires careful consideration of a different group of weed animal kinds, and the goal is to increase revenues while minimising losses as much as possible [5].…”

Section: Resultsmentioning

confidence: 99%

“…The purpose of the new computer programme Drip Irrigation Design and Scheduling (DIDAS) is to aid irrigators in the planning and implementation of drip irrigation systems. This programme recreates a wide range of annual harvests and tree types in various stages of decomposition [6]. It examines plant water content but ignores plant development and yield.…”

Section: Resultsmentioning

confidence: 99%

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Smart Farming based on Machine Learning and IOT

Pendyala¹

2023

IJSMEM

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The quantity of food that must be provided is being affected by the progressively deteriorating state of India’s agricultural industry. Many Indian farmers have shifted their focus away from farming and into other industries. This research is useful because it evaluates alternative approaches to selecting crops, planting them, spotting weeds, and keeping tabs on the system. All of these factors add up to a productive output, but they are hampered by things like a lack of workers and unfavorable environmental conditions. This research looked at the system from multiple angles, primarily focusing on image processing, artificial intelligence,machine learning and the internet of things. The research includes a comparison of the current framework with its most up-to-date counterpart.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Smart Farming based on Machine Learning and IOT

Pendyala¹

2023

IJSMEM

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“…Opportunity for data-driven approaches concerning agricultural optimization have been neglected. In fact, big data analytics can play a vital role in transforming, processing, and visualizing complex agricultural datasets into informative formats [41]. The big data concept generally follows the 5v dimensional model of velocity, volume, value, veracity, and variety.…”

Section: Cloud Computing and Big Datamentioning

confidence: 99%

Artificial Intelligence in Smart Agriculture: Applications and Challenges

Nitin,

Bal Gupta

2023

Curr. Appl. Sci. Technol.

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Artificial intelligence has been categorized as a subfield of computer science wherein machines perform smart learning tasks with the help of data and statical methods. Agriculture is one of the oldest social activities performed by humans. It provides many crucial things like raw materials, food, and employment. Due to the increasing population, it is the need of the hour that the agriculture sector should increase production of resources to match actual demand. Many agronomic factors such as weeds, pests, water condition and availability, and climate conditions impact overall yield. At present, methods used by farmers for management are traditional and insufficient to meet increased demand. To match future demand, new innovative agriculture methos need to be adopted. Artificial intelligence techniques in smart farm monitoring can enhance the quality and quantity of yield. This paper surveys different areas in agriculture where artificial intelligence is applicable. Artificial intelligence enables farmers to access farm-related data and analytical methods that will foster better agronomy, reduce waste, and improve efficiencies with minimum environmental impact. Various artificial intelligence techniques that make agriculture smarter than its previous forms are discussed. In this paper, the implementation of various artificial intelligence techniques in smart agriculture is studied. The aim of this study is to present different key applications and associated challenges to open up new future opportunities.

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“…This is particularly evident in areas where data collection is challenging, and where geology and computer systems professionals are in short supply. Moreover, the current research orientation of the system is not aligned with societal needs, highlighting the importance of addressing this gap in talent acquisition and research direction [14].…”

Section: The Realistic Dilemma Of Safety and Integrated Disaster Prev...mentioning

confidence: 99%

Research on Safety and Integrated Disaster Prevention System Based on Big Data Technology

Sun

2023

IJIIS Int. J. Informatics Inf. Syst.

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Safety integrated disaster prevention systems, as a guarantee of national safety, especially to reduce the serious consequences of disasters, promote the steady development of the economic and social level, have important practical value for the comprehensive study of safety and disaster prevention systems. However, the current application and update of such systems by relevant government agencies and the social level cannot effectively follow the development needs of the society and the industry, and there is an urgent need for effective reform. Based on this, this paper first analyzes the problems existing in the research and construction system of big data technology in the security and integrated disaster prevention system, and then gives the construction strategy of the research system of safety and disaster prevention in view of these problems.

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Research on Crop Disaster Stress Risk Mapping System Based on Agriculture Big Data

Cited by 6 publications

References 0 publications

Smart Farming based on Machine Learning and IOT

Smart Farming based on Machine Learning and IOT

Artificial Intelligence in Smart Agriculture: Applications and Challenges

Research on Safety and Integrated Disaster Prevention System Based on Big Data Technology

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