Remote Sensing in Precision Agriculture for Irrigation Management

Samreen, Tayyaba; Ahmad, M.; Baig, Muhammad Talha; Kanwal, Sehrish; Nazir, Muhammad Zulqernain; Sidra-Tul-Muntaha,

doi:10.3390/environsciproc2022023031

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…UAVs can be used to monitor the effectiveness of irrigation systems. They can capture images of fields before and after irrigation, which can be used to determine the optimal amount of water to apply [144]. By using these systems, it will help to conserve water resources, which can be used in parallel with the smart water systems.…”

Section: Irrigation Managementmentioning

confidence: 99%

Global Models of Smart Cities and Potential IoT Applications: A Review

Hassebo,

Tealab

2023

IoT

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As the world becomes increasingly urbanized, the development of smart cities and the deployment of IoT applications will play an essential role in addressing urban challenges and shaping sustainable and resilient urban environments. However, there are also challenges to overcome, including privacy and security concerns, and interoperability issues. Addressing these challenges requires collaboration between governments, industry stakeholders, and citizens to ensure the responsible and equitable implementation of IoT technologies in smart cities. The IoT offers a vast array of possibilities for smart city applications, enabling the integration of various devices, sensors, and networks to collect and analyze data in real time. These applications span across different sectors, including transportation, energy management, waste management, public safety, healthcare, and more. By leveraging IoT technologies, cities can optimize their infrastructure, enhance resource allocation, and improve the quality of life for their citizens. In this paper, eight smart city global models have been proposed to guide the development and implementation of IoT applications in smart cities. These models provide frameworks and standards for city planners and stakeholders to design and deploy IoT solutions effectively. We provide a detailed evaluation of these models based on nine smart city evaluation metrics. The challenges to implement smart cities have been mentioned, and recommendations have been stated to overcome these challenges.

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Section: Irrigation Managementmentioning

confidence: 99%

Global Models of Smart Cities and Potential IoT Applications: A Review

Hassebo,

Tealab

2023

IoT

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“…Remote sensing has made it e cient to collect data at intervals (Li et al, 2023), and using image analysis makes it possible to categorize the images into different classes based on their content (Tombe & Viriri, 2023). In the last 20 years, signi cant progress has been made in the use of remote sensing in identifying and monitoring plant growth, investigating plant stress and other biophysical characteristics (Kanda et al, 2022;Conrad et al, 2021;Samreen et al, 2023). These studies were usually done on largescale agriculture using satellite images (Vasconcelos et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the ability to measure morphological structures of plants obtained from tissue culture applying image processing techniques

Aliabad,

aliabad,

Habbab

et al. 2023

Preprint

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Biotechnological approaches, for instance, plant tissue culture, can be used to improve and accelerate the reproduction of plants. A single portion of a plant can produce many plants throughout the year in a relatively short period of laboratory conditions. Monitoring and recording plant morphological characteristics such as root length and shoot length in different conditions and stages are necessary for tissue culture. These features were measured using graph paper in a laboratory environment and sterile conditions. This research investigated the ability to use image processing techniques in determining the morphological features of plants obtained from tissue culture. In this context RGB images were prepared from the plants inside the glass, and different pixel-based and object-based classification methods were applied to an image as a control. The accuracy of these methods was evaluated using the kappa coefficient, and overall accuracy was obtained from Boolean logic. The results showed that among pixel-based classification methods, the maximum likelihood method with a kappa coefficient of 87% and overall accuracy of 89.4 was the most accurate, and the Spectral angle mapper method (SAM) method with a kappa coefficient of 58% and overall accuracy of 54.6 was the least accurate. Also, among object-based classification methods, Support Vector Machine (SVM), Naïve Bayes, and K-nearest neighbors algorithm (KNN) techniques, with a Kappa coefficient of 88% and overall accuracy of 90, can effectively distinguish the cultivation environment, plant, and root. Comparing the values of root length and shoot length estimated in the laboratory culture environment with the values obtained from image processing showed that the use of the SVM image classification method, which is capable of estimating root length and shoot length with RMSE 2.4, MAD 3.01 and R2 0.97, matches the results of manual measurements with even higher accuracy.

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A Scoping Review of the Smart Irrigation Literature Using Scientometric Analysis

Gurmessa,

Assefa

2023

Journal of Engineering

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Background. Smart irrigation is a research field which grows very fast. It facilitates the contribution of technologies on smart agriculture. Smart irrigation is a broad topic with overwhelming literature published and available semantic ambiguity, so covering such a vast topic is not easy without scoping reviews. To enable researchers to gain a deep knowledge of structure of the field, a scientometric-based scoping review was conducted. Methods. The bibliometric data focused on smart irrigation from databases such as Scopus, Web of Science, and Google Scholar were downloaded, thoroughly merged, and cleaned to meet the inclusion criteria. These data were analyzed and clustered using K-means from VOSviewer. VOSviewer is used to create coauthor and coword occurrence network graphs from keywords, titles, and abstracts. Results. The findings highlight the broad scope of the research field, the ambiguity of the terminology, the lack of collaboration, and the absence of research into the impact of smart irrigation on agriculture. The leading institutions and researchers in the field and geographical distribution are from China, Israel, Australia, and Egypt. The leading main topics addressed in the field are IOT, smart irrigation, irrigation, water stress, energy, deep learning, soil moisture, and relations in the network. Conclusion. Smart irrigation (drip irrigation + IoT) in agriculture increases crop yield, increases water use efficiency, and decreases costs. In future work, large studies need to be conducted to establish and investigate the scope of smart irrigation research to reveal the knowledge structure, current state of practice, and key actors in the field.

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Remote Sensing in Precision Agriculture for Irrigation Management

Abstract: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Cited by 5 publications

References 8 publications

Global Models of Smart Cities and Potential IoT Applications: A Review

Global Models of Smart Cities and Potential IoT Applications: A Review

Evaluation of the ability to measure morphological structures of plants obtained from tissue culture applying image processing techniques

A Scoping Review of the Smart Irrigation Literature Using Scientometric Analysis

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