Authors:
Rima Grati
1
;
Myriam Aloulou
2
and
Khouloud Boukadi
3
Affiliations:
1
Zayed University, College of Technological Innovation, Abu Dhabi, U.A.E.
;
2
Liwa College of Technology, Abu Dhabi, U.A.E.
;
3
Miracl Laboratory, Faculty of Economics and Management, University of Sfax, Tunisia
Keyword(s):
IoT, Smart Agriculture, Machine Learning, Self Organization Map, Machine Learning, Deep Learning.
Abstract:
The practice of growing crops and raising cattle is the traditional method of agriculture, a primary source of livelihood. The introduction of advanced technologies and tools provides solutions to predict and avoid soil erosion, over-irrigation, and bacterial infection for crops. Machine learning and Deep learning solutions are hitting high results in terms of precise farming. The most challenging factors for research society are identifying the water need, analyzing soil conditions and suggesting the best crops to cultivate, and predicting fertilizer amounts to prevent bacteria. Grouping similar features helps with accurate prediction and classification. Considering this, we introduce an integrated model Group Organize Forecast (GOF), using Machine Learning (ML) and Deep learning (DL) techniques to balance the requirements and improve automatic irrigation. GOF analyzes the irrigation requirement of a field using the sensed ground parameters such as soil moisture, temperature, weathe
r forecast, radiation levels, the humidity of the crop field, and other environmental conditions. We use a real-time unsupervised dataset to analyze and test the model. GOP clusters the data using Self Organizing Map (SOM) organizes the classes using Cascading Forward Back Propagation (CFBP), and finally predicts the requirement for water and solution to control bacteria in the near future.
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