Prediction of Frost Events Using Machine Learning and IoT Sensing Devices

Diedrichs, Ana; Bromberg, Facundo; Dujovne, Diego; Brun‐Laguna, Keoma; Watteyne, Thomas

doi:10.1109/jiot.2018.2867333

Cited by 72 publications

(43 citation statements)

References 18 publications

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“…The similarity is calculated based on distance function [19]. There are many distance metrics that can be used to calculate the distance, but Euclidean distance is mostly preferred [20]. We use distance metric to calculate the similarity between new sample and training set cases to find the closest values to the new value considered.…”

Section: Iiiresults and Discussionmentioning

confidence: 99%

Heuristic Prediction of Crop Yield using Machine Learning Technique

Sunkara,

Beulet P.

2019

IJEAT

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Vast research has been done and several attempts are made for application of Machine learning in agricultural field. Major challenge in agriculture is to increase the production in the farm and deliver it to the end customers with best possible price and good quality. It is found that at least 50 percent of the farm produce never reach the end consumer due to wastage and high-end prices. Machine learning based solutions developed to solve the difficulties faced by the farmers are being discussed in this work. The real time environmental parameters of Telangana District like soil moisture, temperature, rainfall, humidity are collected and crop yield is being predicted using KNN Algorithm.

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Section: Iiiresults and Discussionmentioning

confidence: 99%

Heuristic Prediction of Crop Yield using Machine Learning Technique

Sunkara,

Beulet P.

2019

IJEAT

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“…With farm management agricultural practices are made informative by evaluation and comparison with the other developed approaches and methods. Ana Laura Diedrichs et al [71], with the aid of machine learning and IoT sensing devices, predicted the occurrence of frost events. Authors designed their system based on three layers i.e., a group of internet-enabled devices for water data collection.…”

Section: A Iot In Farm Managementmentioning

confidence: 99%

Recent Developments of the Internet of Things in Agriculture: A Survey

2020

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A rise in the population has immensely increased the pressure on the agriculture sector. With the advent of technology, this decade is witnessing a shift from conventional approaches to the most advanced ones. The Internet of Things (IoT) has transformed both the quality and quantity of the agriculture sector. Hybridization of species along with the real-time monitoring of the farms paved a way for resource optimization. Scientists, research institutions, academicians, and most nations across the globe are moving towards the practice and execution of collaborative projects to explore the horizon of this field for serving mankind. The tech industry is racing to provide more optimal solutions. Inclusion of IoT, along with cloud computing, big data analytics, and wireless sensor networks can provide sufficient scope to predict, process, and analyze the situations and improve the activities in the real-time scenario. The concept of heterogeneity and interoperability of the devices by providing flexible, scalable, and durable methods, models are also opening new domains in this field. Therefore, this paper contributes towards the recent IoT technologies in the agriculture sector, along with the development of hardware and software systems. The public and private sector projects and startup's started all over the globe to provide smart and sustainable solutions in precision agriculture are also discussed. The current scenario, applications, research potential, limitations, and future aspects are briefly discussed. Based on the concepts of IoT a precision farming framework is also proposed in this article.

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“…The authors have proposed a prediction of frost events using machine learning techniques and IoT devices in [16], [17]. The authors have used Bayesian networks and Logistic Regression that predict the minimum temperature for the next day.…”

Section: Literature Reviewmentioning

confidence: 99%

Integration of Google Play Content and Frost Prediction Using CNN: Scalable IoT Framework for Big Data

et al. 2020

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The forecast of frost occurrence requires complex decision analysis that uses conditional probabilities. Due to frost events, the production of crops and flowers gets reduced, and we must predict this event to minimize the damages. If the frost prediction results are accurate, then the damage caused by frost can be reduced. In this paper, an ensemble learning approach is used to detect frost events with Convolutional Neural Network (CNN). We have used this to get more efficient and accurate results. Frost events need to be predicted earlier so that the farmer can take on-time precautionary measures. So, for measurement and analysis of Google Play, we have scrapped a dataset of the Agricultural category from different genres and collected the top 550 application of each category of Agricultural applications with 70 attributes for each category. The prediction of frost events prior few days of an actual frost event with an accuracy of 98.86%.

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Prediction of Frost Events Using Machine Learning and IoT Sensing Devices

Cited by 72 publications

References 18 publications

Heuristic Prediction of Crop Yield using Machine Learning Technique

Heuristic Prediction of Crop Yield using Machine Learning Technique

Recent Developments of the Internet of Things in Agriculture: A Survey

Integration of Google Play Content and Frost Prediction Using CNN: Scalable IoT Framework for Big Data

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