Enabling smart agriculture by implementing artificial intelligence and embedded sensing

Sharma, Ashutosh; Georgi, Mikhail; Tregubenko, Maxim D.; Tselykh, Alexey; Tselykh, Alexander

doi:10.1016/j.cie.2022.107936

Cited by 63 publications

(32 citation statements)

References 38 publications

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“…Classification accuracy is the percentage of the sum of true positive and true negative on the total number of plant images, as expressed in (1). The precision metric indicates the number of correct predictions made by the model, as expressed in (2).…”

Section: Resultsmentioning

confidence: 99%

“…This issue increases the number of labourers and decreases the crop productivity. Recently, artificial intelligence (AI) tools represent a good and appropriate solution to avoid the traditional techniques used in the agricultural sector by offering the intelligence proprieties such as learning, self-adapting, classification capability, and more [1]- [5]. These benefits enable us to create intelligent agricultural systems that will help farmers in improving their agricultural productivity, which is our study's goal.…”

Section: Introductionmentioning

confidence: 99%

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Performance analysis of the application of convolutional neural networks architectures in the agricultural diagnosis

Belattar

Abdoun

Haimoudi

2022

IJEECS

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Agriculture is an important sector for developing countries and farmers. Recently, numerous techniques for increasing agricultural productivity have been utilized. However, different issues are still encountered by farmers including various plant diseases. Plant diseases diagnoses are challenging research, and they should be analyzed and treated by detecting the diseased plant leaves. For that reason, in this paper, we develop our proposed architecture using convolutional neural networks (OP-CNN) as a computer-aided to detect and diagnose plant diseases. The proposed architecture can assist farmers in increasing both the quantity and quality of their agricultural productivity. Besides this, the OP-CNN helps to reduce disease prevalence through early detection. The performance of our proposed model is compared with other convolutional neural networks (CNN) architectures in order to validate its capability. The strawberry dataset was employed to train and test the models since the strawberry is one of the main crops in the Larache Province (Morocco). The experimental tests demonstrate that our proposed OP-CNN reaches the highest values versus DenseNet121, VGG19, and ResNet50 with 100%, 99%, 97%, and 63% respectively for classification accuracy, 100%, 100%, 98% and, 79% respectively for precision, 100%, 99%, 97%, and 63% respectively for recall, and 100%, 99%, 97%, and 58% respectively for "F" _1Score.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance analysis of the application of convolutional neural networks architectures in the agricultural diagnosis

Belattar

Abdoun

Haimoudi

2022

IJEECS

View full text Add to dashboard Cite

show abstract

“…Support Vector Machine [38][39][40] is a machine learning approach proposed by Vapnik that has been widely used to analyze and identify patterns. Optimal Separate Hyperplane (Optimum Separate Hyperplane, OSH) is obtained by using the training set to split the data into two categories to obtain the data categories.…”

Section: Iaefa-svm Modelmentioning

confidence: 99%

Improved artificial electric field algorithm based on multi-strategy and its application

Tian

Liu

Wang

et al. 2022

IJCAI

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Artificial electric field algorithm is a new swarm bionic optimization algorithm, which uses the interaction force of charged particles to create a mathematical model to solve the problem. To improve the global exploration ability and local development ability of artificial electric field algorithms, an artificial electric field algorithm based on opposition learning is proposed. The chaos strategy is used to strengthen the quality of the initial population, and the opposition learning strategy is used to increase the diversity of the population and the development ability of the algorithm. The excellent performance of the algorithm is proved by simulation experiments. The improved artificial electric field algorithm is combined with SVM to construct the sand liquefaction identification model by selecting seven measured indexes, including intensity, underground water level, overlying effective pressure, standard penetration hit number, average particle size, non-uniformity coefficient, and shear stress ratio. Compared with traditional methods such as the standard method and seed simplification method, the results show that the IAEFA-SVM model has high prediction accuracy and provides an effective method for sand liquefaction identification.Povzetek: Predstavljen je izboljšan algoritem umetnega električnega polja na osnovi mnogoterih strategij.

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“…There are also some different strategies, such as BP algorithm combined with other techniques such as fuzzy theory or genetic algorithm [39][40][41][42][43] .…”

Section: Lm and Fuzzy Theorymentioning

confidence: 99%

Big Data Intelligent Collection and Network Failure Analysis Based on Artificial Intelligence

Ding

Alroobaea

Baqasah

et al. 2022

IJCAI

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To study smart data collection and network error analysis, this paper proposes intelligent data collection and network error analysis based on artificial intelligence. It examines the establishment of an enterprise-level information security situation awareness system and proposes specific information security models, architectures, and implementation methods. By designing and deploying the system, businesses can effectively detect information security threats, receive threats, filter risks, control threats, and comprehensively improve businesses' ability to detect security threats and security attacks. Test results: Through this platform, it is possible to manually intervene in the unknown threat of large data analysis in the system, and professionals can perform a detailed analysis to determine the means, goals and objectives of the attack and restore the complete picture. Intruder through artificial intelligence combined with big data knowledge and intrusion. Dimensional human characteristics. Including similar Trojans and malicious servers with different application forms, encodings, and attack principles, they "track" intruders by their general characteristics, constantly detect unknown threats, and ultimately ensure the accuracy of unknown threat detection, creating a local threat intelligence analytics platform. Practice has shown that the intelligent acquisition of large data by artificial intelligence can effectively analyze network failures.Povzetek: S pomočjo umetne inteligence je narejena analiza napak v omrežjih in zbiranje podatkov.

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Enabling smart agriculture by implementing artificial intelligence and embedded sensing

Cited by 63 publications

References 38 publications

Performance analysis of the application of convolutional neural networks architectures in the agricultural diagnosis

Performance analysis of the application of convolutional neural networks architectures in the agricultural diagnosis

Improved artificial electric field algorithm based on multi-strategy and its application

Big Data Intelligent Collection and Network Failure Analysis Based on Artificial Intelligence

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