Digital agriculture based on big data analytics: a focus on predictive irrigation for smart farming in Morocco

Rabhi, Loubna; Falih, Noureddine; Afraites, Lekbir; Bouikhalene, Belaid

doi:10.11591/ijeecs.v24.i1.pp581-589

Cited by 17 publications

(10 citation statements)

References 18 publications

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“…In recent years, several effective studies have shown that there is a rapid and high demand for water [2], [3]. We have also noticed that there has been an intense shift from traditional irrigation to smart irrigation based on Internet of Things technology to fully automate and remotely examine water requirements [4], [5]. In this axis, an interesting study based on the internet of things and image processing was developed, in this study, the researchers hoped that this intelligent system will improve the method of breeding Phalaenopsis in the future [6].…”

Section: Related Workmentioning

confidence: 99%

Monitoring of submersible pumps using ESP32 microcontroller and photovoltaic panels

Didi

Azami

2023

IJEECS

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In this article, the Moroccan climate is currently undergoing several changes that have negatively affected agricultural activities, especially in the field of irrigation. That's why we relied on internet of things (IoT) technology to overcome these problems. In this article, we present the design and realization of an intelligent irrigation system via a solar submersible pump. We used the ESP32 microcontroller which reads the temperature and humidity values measured by the soil moisture sensor. The communication between the blocks is ensured by the radiofrequency module. We have implemented a Web server to monitor the measured quantities. The graphical representation of the data will be ensured using the ThingSpeak platform which makes it possible to store and collect the data coming from the sensors via the hypertext transfer protocol (HTTP). Our achievement was executed and tested without any problems detected, which shows that our smart irrigation study was very successful.

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Section: Related Workmentioning

confidence: 99%

Monitoring of submersible pumps using ESP32 microcontroller and photovoltaic panels

Didi

Azami

2023

IJEECS

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“…Topic Solution [58] Irrigation Management [59], [61]- [66] Irrigation System [60] Irrigation Control [67] Irrigation Timing Decision [68] Irrigation Predictive…”

Section: Referencesmentioning

confidence: 99%

A Systematic Review of Current Trends in Artificial Intelligence for Smart Farming to Enhance Crop Yield

Widianto

Ardimansyah²,

Pohan³

et al. 2022

Journal of Robotics and Control

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Current technology has been widely applied for development, one of which has an Artificial Intelligence (AI) applied to Smart Farming. AI can give special capabilities to be programmed as needed. In cooperation with agricultural systems, AI is part of improving the quality of agriculture. This technology is no stranger to being applied in basic fields such as agriculture. This smart technology is needed to increase crop yields for various regions by utilizing the current trends paper. This is necessary because less land is available for agriculture, and there is a greater need for food sources. Therefore, this systematic review aims to collect the current trends in AI studies for Smart Farming papers using the latest year features from 2018-2022. This paper is handy for researchers and industry in looking for the latest papers on research to enhance crop yields. The authors utilized Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) of 534 articles from IEEE, ACM, MDPI, IAES, and ScienceDirect. After going through a careful process, 67 papers were found that were judged according to the criteria. After the authors got some of the current trends, the author has discussed several factors regarding the results obtained to enhance crop yields, such as Weather, Soil, Irrigation, Unmanned Aerial Vehicle (UAV), Pest Control, Weed Control, and Disease Control.

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“…SVMs were initially intended for linear models but non-linear ones can be made by transforming input X through a non-linear mapping Φ(X) and then applying conventional linear SVMs over features Φ(X). According to the data, the performance of SVM is the same order, or even better, than a neural network (NN) [25].…”

Section: Support Vector Machine (Svm) Algorithmmentioning

confidence: 99%

An intelligent irrigation system based on internet of things (IoT) to minimize water loss

Amassmir

Tkatek

Abdoun

et al. 2022

IJEECS

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This paper proposes a comparison of three machine learning algorithms for a better intelligent irrigation system based on internet of things (IoT) for differents products. This work's major contribution is to specify the most accurate algorithm among the three machine learning algorithms (k-nearest neighbors (KNN), support vector machine (SVM), artificial neural network (ANN)). This is achieved by collecting irrigation data of a specific products and split it into training data and test data then compare the accuracy of the three algorithms. To evaluate the performance of our algorithm we built a system of IoT devices. The temperature and humidity sensors are installed in the field interact with the Arduino microcontroller. The Arduino is connected to Raspberry Pi3, which holds the machine learning algorithm. It turned out to be ANN algorithm is the most accurate for such system of irrigation. The ANN algorithm is the best choice for an intelligent system to minimize water loss for some products.

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Digital agriculture based on big data analytics: a focus on predictive irrigation for smart farming in Morocco

Cited by 17 publications

References 18 publications

Monitoring of submersible pumps using ESP32 microcontroller and photovoltaic panels

Monitoring of submersible pumps using ESP32 microcontroller and photovoltaic panels

A Systematic Review of Current Trends in Artificial Intelligence for Smart Farming to Enhance Crop Yield

An intelligent irrigation system based on internet of things (IoT) to minimize water loss

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