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Voluminous and variety of disparate information is generated and consumed in agriculture domain at a higher velocity. In agriculture, information is available in the form of weather and soil conditions reports, GPS mapping, water resources, fertilizer/ pesticide use, field characteristics, and commodity market conditions. Big data technology has a...
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... Because legumes such as peanuts, lentils, soybeans and mungbeans-are essential plant-based protein sources, their production is essential to human nutrition (Kumar et al., 2020;Rawal, 2017). But because of their extremely delicate water needs, conventional farming methods often lead to less-than-ideal water management, risking not just the sustainability of legume cultivation but also food security (Shah et al., 2017;El-Nakhlawy et al., 2017). Artificial Intelligence (AI) offers a novel solution for addressing these issues. ...
Background: Cultivating legumes, a significant facet of sustainable agriculture, consistently faces challenges in managing water resources. The present study aimed to explore the integration of artificial intelligence (AI) to enhance water use efficiency in legume farming with the potential to reduce the water shortage problem. In this work, Peas as a specific legume is chosen. In Uttar Pradesh, India, precision irrigation was combined with artificial intelligence (AI) to maximize crop productivity, support sustainable farming methods and solve the problem of water constraints. AI-enabled precision irrigation offers significant advantages like precise allocation of water resources, enhanced crop yield, optimal water consumption, cost-effectiveness and a reduction of greenhouse gas emissions. Methods: By employing a systematic methodology, including data collection, AI modeling and thorough data analysis, this work reveals useful findings. The comparison between traditional and AI-driven precision irrigation shows that artificial intelligence delivers enhanced real-time decision-making capabilities. It optimally tailors’ irrigation schedules and water distribution, considering weather, soil conditions and crop requirements. The achieved water savings, combined with improved legume yields, have significant implications for agricultural techniques with limited resources. Result: Because of a changing climate and decreasing water supplies, farmers, legislators and other stakeholders can greatly benefit from the suggestions that were obtained from the findings, which provide practical direction. This research serves as a milestone in the integration of AI for precision agriculture, creating a way for a more sustainable and productive future in legume farming.
... The regional references are subsets of global references. The global references are protocols applicable to all inherited regional references [47]. ...
In today’s world, Internet of Things (IoT) is an emerging advancement that has proven its application in various fields ranging from aviation to enhancing the food supply. IoT aims to integrate numerous sensing devices to derive actionable knowledge. IoT uses standardized protocols at various levels to connect the smart devices to a large-scale decision-making system. In this chapter, we present a survey on the recent application of IoT techniques and future challenges and open issues. We have gone through many case-studies presented by leading IoT solution provider and correlated its implementation through research papers. The most influential IoT implementation is identified, and the applications are presented. We majorly focus on the IoT impact on developing smart transportation, smart cities, and agriculture.
... In recent years, a lot of studies have been focused on developing big data platforms that can handle massive volumes of agriculture data to support decision-making systems [26]. Shah et al. [17] proposed a spark-based agricultural information system built upon big data open sources. The author developed various web based analytical and visualized services for cotton crop. ...
... The regional references are subsets of global references. The global references are protocols applicable to all inherited regional references [47]. ...
... This method of data analytics can be used for crop yield prediction, current weather trends and performing insights on Agricultural market data. [10]. In Section IV, the challenges that are faced in the analysis of big data in agriculture are discussed. ...
This paper aims at collecting and analysing temperature, rainfall, soil, seed, crop production, humidity and wind speed data (in a few regions), which will help the farmers improve the produce of their crops. Firstly, we pre-process the data in a Python environment and then apply the MapReduce framework, which further analyses and processes the large volume of data. Secondly, k-means clustering is employed on results gained from MapReduce and provides a mean result on the data in terms of accuracy. After that we use bar graphs and scatter plots to study the relationship between the crop, rainfall, temperature, soil and seed type of two regions (Ahmednagar, Maharashtra and, Andaman and Nicobar Islands). Further, a self-designed recommender system has been used to predict the crops and display them on a Graphic User Interface designed in a Flask environment. The system design is scalable and can be used to find the recommended crops of other states in a similar manner in the future.
... This method of data analytics can be used for current weather trend, crop yield prediction and perform insights on Agricultural market data [17]. ...
In the sector of agriculture which is very vast and requires a lot of planning, decision making, security andvarious other intricate factors influencing it. Though, the field of agriculture is less impacted by the recent technologicaladvancements. However, agriculturalists are rapidly moving towards working with modern tools and technologies. Onesuch up to the minute technology is Big Data analytics. Big data has been introduced to almost every other sectoreven agriculture is not outdistanced from it [1]. Agriculturists, Agribusinesses, institutions and researchers have beendependent on various techniques to collect related data. Henceforth, the collected data is further modified or turnedinto quality from quantity type. The sole focus is to extract acumens from it which can be utilized by the farmers orthe end users and can be implemented to gain and achieve assured outcomes. Such as Apt crop forecasting, precisionfarming, smart agriculture, achieving high quality seeds, climate predictions and much more [2]. However, In-order toattain these niches a lot of big data analytic techniques have to be understood such as Predictive analytics, Machinelearning, Classification and clustering, Recommendation system, Time series analytics, Regression analytics and Datamining. These are just a few that have been addressed here. Furthermore, a reviewed assimilation of various big dataanalytic techniques and its implementation in the field of agriculture has been obtained. Nevertheless, every technologyhas its drawbacks. Hence, the challenges of big data analytics in agriculture has been discussed with further augmentingto its future scope of work in the area of agriculture.
... In addition to the modernization of agricultural procedures and farming tools, it is needed a management information system (MIS) and intended to reduce the technological gap between agro users and information [15]. Precision agriculture supported by the MIS of the farm activities represents a viable and effective solution for its modernization [16]. ...
... P. Shah, [13] Spark based on agricultural information system domain at a higher velocity. Information is available in the form of weather and soil condition reports, water resources, soil condition reports, field characteristics, and market conditions. ...
Agriculture is the backbone of Indian economy. Big data are emerging précised and viable analytical tool in agricultural research field. This review paper facilitates the farmers in selecting the right crops and appropriate cropping pattern for a particular locality. A modern trend in the Agriculture domain has made people realize the importance of big data. It provides a methodology for facing challenges in agricultural production, by applying this Algorithm, using machine learning techniques. The different machine learning techniques survey is presented in this paper to realize enhanced monitory benefits in a particular area. A study of machine learning algorithms for big data Analytic is also done and presented in this paper.
... Here are the statistically approaches we adopted to • Naive Bayes classifier is a simple probabilistic classifier based on applying Bayes theorem with strong naive independence assumptions [4]. • Support vector machines re supervised learning models with associated learning algorithms that analyze data and recognize patterns, are used for classification and regression analysis [5]. • Regression analysis Estimating the relationships among variables [4]. ...
Voluminous data is generated from complex data sources with varied characteristics in agriculture. The agricultural data is made available in a variety of formats, is generated and collected using diverse techniques, and is exposed through numerous data sources. Data management in agriculture presents more difficult problems for emerging nations than for industrialized nations. The research scope is formulated by evaluating the challenges and opportunities related to advanced application development in agriculture for developing countries. The paper discusses the research evolution of data management, applications, and analytics algorithms development in agriculture. The advanced data systems, applications, and architectures with big data characteristics in agriculture are reviewed. It highlights the significant constraints in providing data management-related solutions for application development in agriculture. The paper poses research questions; and discusses the possible answers for future endeavors.
