Prediction of frost episodes based in agrometeorological information and machine learning techniques

Möller‐Acuña, Patricia; Ahumada-Garcia, Roberto; Reyes-Suárez, José Antonio

doi:10.1109/ica-acca.2016.7778386

Cited by 2 publications

(1 citation statement)

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“…Plant physiology, seed germination, pollination, growth, photosynthesis, and material transport within the plant are sensitive to climate conditions in growth stages. One of the meteorological issues for many agricultural systems is frost occurrence [6]. Frost also causes a significant impact on electrical lines, engine turbine blades, heat exchangers, and wind turbines.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%