Towards a Novel Approach for Smart Agriculture Predictability

Rima Grati, Myriam Aloulou, Khouloud Boukadi

2023

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, weather 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.

Paper Citation

in Harvard Style

Grati R., Aloulou M. and Boukadi K. (2023). Towards a Novel Approach for Smart Agriculture Predictability. In Proceedings of the 18th International Conference on Software Technologies - Volume 1: ICSOFT; ISBN 978-989-758-665-1, SciTePress, pages 96-105. DOI: 10.5220/0012082400003538

in Bibtex Style

@conference{icsoft23,
author={Rima Grati and Myriam Aloulou and Khouloud Boukadi},
title={Towards a Novel Approach for Smart Agriculture Predictability},
booktitle={Proceedings of the 18th International Conference on Software Technologies - Volume 1: ICSOFT},
year={2023},
pages={96-105},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012082400003538},
isbn={978-989-758-665-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Software Technologies - Volume 1: ICSOFT
TI - Towards a Novel Approach for Smart Agriculture Predictability
SN - 978-989-758-665-1
AU - Grati R.
AU - Aloulou M.
AU - Boukadi K.
PY - 2023
SP - 96
EP - 105
DO - 10.5220/0012082400003538
PB - SciTePress