A Systematic Review of Knowledge Representation Techniques in Smart Agriculture (Urban)

Bhuyan, Bikram Pratim; Tomar, Ravi; Ramdane-Chérif, Amar

doi:10.3390/su142215249

Cited by 18 publications

(4 citation statements)

References 97 publications

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“…Digitalization plays a crucial role not only in industrial sectors but also in various fields, such as smart agriculture, which serves as a significant catalyst for the high-quality development of China's foundational industries and the growth of the rural economy (Li and Nanseki, 2023;Bhuyan et al, 2022). Firstly, communication technologies, especially those based on the Internet of Things (IoT), facilitate efficient digital flow within complex ecological New growth in cross-border E-business networks.…”

Section: 2mentioning

confidence: 99%

New growth in cross-border E-business: evidence from gray forecasting to cross-border E-business in China

Huo,

Ouyang,

Tang

et al. 2024

JIDE

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PurposeThis paper delves into cross-border E-business, unraveling its intricate dynamics and forecasting its future trajectory.Design/methodology/approachThis paper projects the prospective market size of cross-border E-business in China for the year 2023 using the GM (1,1) gray forecasting model. Furthermore, to enhance the analysis, the paper attempts to simulate and forecast the size of China’s cross-border E-business sector using the GM (1,3) gray model. This extended model considers not only the historical trends of cross-border E-business but also the growth patterns of GDP and the digital economy.FindingsThe forecast indicates a market size of 18,760 to 18,934 billion RMB in 2023, aligning with the consistent growth observed in previous years. This suggests a sustained positive trajectory for cross-border E-business.Originality/valueCross-border e-commerce critically shapes China’s global integration and traditional industry development. The research in this paper provides insights beyond statistical trends, contributing to a nuanced understanding of the pivotal role played by cross-border e-commerce in shaping China’s economic future.

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Section: 2mentioning

confidence: 99%

New growth in cross-border E-business: evidence from gray forecasting to cross-border E-business in China

Huo,

Ouyang,

Tang

et al. 2024

JIDE

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“…It comprises biological, physical, hydrologic, and transportation-related systems. Ontologies include concepts such as plants, soil profiles, soil layers, water content, and nitrogen concentration [3]. Machine learning approaches include support vector machines, fuzzy logic, fuzzy logic, random forests, K-means algorithms, convolutional neural networks, and artificial neural networks [4].…”

Section: Introductionmentioning

confidence: 99%

“…A novel hybrid model for automated illness identification is developed. Using Deep Learning algorithms to make image dimensionality more manageable to shorten the processing time for huge datasets with several dimensions [7]. There are various types of machine learning approaches methods that can be used to recognize and detect plant leaf diseases.…”

Section: Introductionmentioning

confidence: 99%

Ontology-based classification method using statistical and symbolic approaches for plant diseases detection in Agriculture

Ahmed

2023

IJCDS

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Agriculturalists spend the majority of their financial resources managing and diagnosing plant diseases. Manually identifying plant diseases requires a lot of time and effort. This study recommends deep neural networks to automatically detect and identify plant diseases. The datasets are trained using a Random Forest technique to classify images of diseased and healthy leaves. The suggested ontology is modelled based on the Web Ontology Language (OWL). Support vector machines, convolution neural networks, and artificial neural networks are just a few of the classifiers utilized to extract features. A dataset that was self-gathered during the augmentation phase is used to validate the augmented method as part of the validation process. The majority of machine-learning methods for identifying plant diseases rely on custom criteria and rarely handle enormous amounts of data. To address this problem, this paper offers an ontology-based method for modelling plant diseases. According to the suggested method, pre-trained architectures include Alex Net and VGG19 CNNs. In order to extract the most useful features from a dataset, the algorithm modifies the details. Based on the correlation coefficient for each characteristic, the right subset of features is selected Convolutional neural networks, artificial neural networks, deep learning, and other artificial intelligence techniques have made it possible to detect diseases in crops like rice, wheat, maize, cotton, tomatoes, peas, potatoes, cucumbers, cassava, berries, peaches, grapes, apples, sweet paper, tea, and others. This study established that training large, publicly available data sets with machine learning algorithms is an effective method for diagnosing plant diseases.

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“…Artificial Intelligence has played a major role in agriculture [14,[20][21][22][23][24][25]. Crop yield is one of the major domains where the tools of AI can be molded for estimation of the same [15,[26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Crop Type Prediction: A Statistical and Machine Learning Approach

et al. 2022

Self Cite

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Farmers’ ability to accurately anticipate crop type is critical to global food production and sustainable smart cities since timely decisions on imports and exports, based on precise forecasts, are crucial to the country’s food security. In India, agriculture and allied sectors constitute the country’s primary source of revenue. Seventy percent of the country’s rural residents are small or marginal agriculture producers. Cereal crops such as rice, wheat, and other pulses make up the bulk of India’s food supply. Regarding cultivation, climate and soil conditions play a vital role. Information is of utmost need in predicting which crop is best suited given the soil and climate. This paper provides a statistical look at the features and indicates the best crop type on the given features in an Indian smart city context. Machine learning algorithms like k-NN, SVM, RF, and GB trees are examined for crop-type prediction. Building an accurate crop forecast system required high accuracy, and the GB tree technique provided that. It outperforms all the classification algorithms with an accuracy of 99.11% and an F1-score of 99.20%.

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A Systematic Review of Knowledge Representation Techniques in Smart Agriculture (Urban)

Cited by 18 publications

References 97 publications

New growth in cross-border E-business: evidence from gray forecasting to cross-border E-business in China

New growth in cross-border E-business: evidence from gray forecasting to cross-border E-business in China

Ontology-based classification method using statistical and symbolic approaches for plant diseases detection in Agriculture

Crop Type Prediction: A Statistical and Machine Learning Approach

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