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p-Index From 2021 - 2026
0.817
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This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Noureddine Falih
Sultan Moulay Slimane University
Computer Science & IT Electrical & Electronics Engineering

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Deep learning-based decision support system for weeds detection in wheat fields Brahim Jabir; Noureddine Falih
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp816-825

Abstract

In precision farming, identifying weeds is an essential first step in planning an integrated pest management program in cereals. By knowing the species present, we can learn about the types of herbicides to use to control them, especially in non-weeding crops where mechanical methods that are not effective (tillage, hand weeding, and hoeing and mowing). Therefore, using the deep learning based on convolutional neural network (CNN) will help to automatically identify weeds and then an intelligent system comes to achieve a localized spraying of the herbicides avoiding their large-scale use, preserving the environment. In this article we propose a smart system based on object detection models, implemented on a Raspberry, seek to identify the presence of relevant objects (weeds) in an area (wheat crop) in real time and classify those objects for decision support including spot spray with a chosen herbicide in accordance to the weed detected.
Digital agriculture based on big data analytics: a focus on predictive irrigation for smart farming in Morocco Loubna Rabhi; Noureddine Falih; Lekbir Afraites; Belaid Bouikhalene
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp581-589

Abstract

Due to the spead of objects connected to the internet and objects connected to each other, agriculture nowadays knows a huge volume of data exchanged called big data. Therefore, this paper discusses connected agriculture or agriculture 4.0 instead of a traditional one. As irrigation is one of the foremost challenges in agriculture, it is also moved from manual watering towards smart watering based on big data analytics where the farmer can water crops regularly and without wastage even remotely. The method used in this paper combines big data, remote sensing and data mining algorithms (neural network and support vector machine). In this paper, we are interfacing the databricks platform based on the apache Spark tool for using machine learning to predict the soil drought based on detecting the soil moisture and temperature.
A functional framework based on big data analytics for smart farming Loubna Rabhi; Noureddine Falih; Lekbir Afraites; Belaid Bouikhalene
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1772-1779

Abstract

Big data in agriculture is defined as massive volumes of data with a wide variety of sources and types which can be captured using internet of things sensors (soil and crops sensors, drones, and meteorological stations), analyzed and used for decision-making. In the era of internet of things (IoT) tools, connected agriculture has appeared. Big data outputs can be exploited by the future connected agriculture in order to reduce cost and time production, improve yield, develop new products, offer optimization and smart decision-making. In this article, we propose a functional framework to model the decision-making process in digital and connected agriculture.
Precipitation forecasting using machine learning in the region of Beni Mellal-Khenifra Hamza Jdi; Noureddine Falih
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp451-458

Abstract

Agriculture in the region of Beni Mellal-Khenifra, Morocco relies on irrigation from rain and dams, but recently there has been a lack of precipitation which may negatively affect crop growth. This has made accurate precipitation forecasts even more important for farmers, as they need this information to make informed decisions about their crops. However, a lack of data-driven research utilizing past data presents a challenge for the development of such research and leaves farmers relying solely on weather forecasts from TV, which cannot relied upon in systems such as irrigation. The objective of this paper is to propose various approaches for forecasting precipitation in the region of Beni Mellal-Khenifra using big data analytics and machine learning techniques. The study made use of Apache Spark, a big data analytics tool, and five machine-learning algorithms: Lasso regression, ridge regression, elastic net, auto regressive integrated moving average, and random forest. These algorithms were applied on dataset of daily rainfall from 2000 to 2015 to forecast the amount of precipitation in the region. The results of the study showed that the random forest algorithm had the lowest mean absolute error, making it the most effective at forecasting precipitation in the region.
Co-Authors Belaid Bouikhalene Brahim Jabir Hamza Jdi Lekbir Afraites Loubna Rabhi