Developing a Comprehensive Relational Database for Optimizing Land Utilization in Sustainable Farming

Hosny, Ossama; Eslamboly, Ahmed El; Dorra, Elkhayam M.; Tarabieh, Khaled; Abotaleb, Ibrahim S.; Amer, Mahmoud; Farouk, Mostafa; Gad, Heba; Hassan, M.; Mansour, S.; Abdelgelil, A.; Sameh, M.

doi:10.1007/978-981-19-0503-2_32

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…A system used to maintain relational databases is a relational database management system (RDBMS). Many relational database systems are equipped with the option of using the SQL (Structured Query Language) for querying and maintaining the database (Ambler, 2009;Khosiin and Umam, 2023;Hosny et al, 2023;Nguyen, 2022;Lifschitz et al, 2022). Each attribute is assigned a letter designation (Table 1, Column 3).…”

Section: Database Of Histological Sectionsmentioning

confidence: 99%

Application of Machine Learning Technology to Management Methods

Lisitsyn,

Chernukha,

Xiao

et al. 2023

Meat Technology

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Multicomponent meat system -brine injected pork and cooked sausage "Doctorskaya" were was analyzed using neural network technologies and the conditions of uncertainty and risk of human error in a decision-making process in time domain were identified. The formation of a situational classifier (digital image-based histology -meat sample sections with a detailed description) and the system's knowledge base was described. The general steps of a histological section image processing are: 1) preprocessing of section images (noise removal, palette optimization, etc.); 2) color segmentation based on palette minimization; 3) approximation of boundaries of the areas highlighted in the image; 4) area size determination; 5) particle shape determination; 6) particle color determination; 7) identification of the presence of counterfeits; 8) results' output regarding the determination of the presence of counterfeits. The Jupyter Notebook and Colaboratory software environment was used to study and compare the influence of several activation functions (ReLu, tanH, eLu, sigmoid, softPlus, softSign) on the generated DataSet. The best result was obtained when with ReLu (0.9843) activation function, followed by SoftPlus (0.9765) and eLu (0.9687) activation functions. This stage of the study considered a kind of convolutional neural network (CNN) architecture with two layers of convolution (Convolutional, C-Layer) and pooling (Subsampling, S-Layer). An algorithm of the Error Back Propagation gradient was applied to train CNN. This is the first research stage for convolutional neural network applications in solution management.

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Section: Database Of Histological Sectionsmentioning

confidence: 99%

Application of Machine Learning Technology to Management Methods

Lisitsyn,

Chernukha,

Xiao

et al. 2023

Meat Technology

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Section: Eluom Relational Databasementioning

confidence: 99%

“…The next section briefly explains the significance of each metric as well as its impact on crop production and yield. Detailed explanation of the crop selection criteria used in the construction of the database is described in Hosny et al 2021 [9]. In the database, the crops are classified based on their commercial name (the name commonly used in commercial products), the scientific name (the name in literature) and family classification which may vary to be either a fruit, vegetable, field crop, cereal, legumes, oilseeds, medicinal plants, herbs or trees.…”

Section: Eluom Relational Databasementioning

confidence: 99%

Economic Land Utilization Optimization Model

et al. 2023

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Recently, population growth and resource depletion have been matched by a growing demand for self-sustaining communities. Numerous studies promote sustainable solutions to the concerns of climate change and food scarcity. This study aims at creating an automated Economic Land Utilization Optimization Model (ELUOM) that identifies sustainable and cost-effective agricultural practices. Soil, water & climatic characteristics of over 400 crops are gathered in a relational database to build the model. Evolutionary algorithms are utilized to filter the database based on user input. Optimization process is then performed on all possible utilization plans of the filtered crops to maximize the 20-year return while minimizing water consumption. The model is verified on a case study in Giza, Egypt where it shows the potential of increasing the return/m3 of water by 370% versus current practices. This research also studies the application of ELOUM on a vacant plot in the American university in Cairo, Egypt.

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