SLICE: Multifunction IOT-Based Soil Contaminants and Macronutrients Analyzer/ Aloria, John Aziel M. and five others

By:

Aloria, John Aziel M

Contributor(s):

Material type: Text

TextManila, Philipines: Technological University of the Philippines, 2020Description: 184 pages: illustrations ; 30 cm. + CD 3/4 inchContent type:

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Carrier type:

Subject(s):

Soil pollution -- Measurement

LOC classification:

BTH TK 7816 A46 2020

Dissertation note: College of Engineering-- Bachelor of Science in Electronics Engineering, Technological University of the Philippines, 2019. Abstract: Soil nutrients (Nitrogen, Phosphorus, and Potassium) of a certain agricultural aren have their selectivity or specificity for the highest potential crop yield. On the other hand, common heavy metals (Arsenie, Cadmium, Lead, and Mercury) found on soil sediments especially beneath polluted bodies of water also became part of our local farmers' struggles. These correspond to laboratory methods used to solve or lessen the amount of the heavy metals content and produce the desired amount of nutrients by applying appropriate fertilizers. The said problems can be managed through efficient soil analysis. The main objective of this study is to develop a multifunction loT-based device for soil contaminants and macronutrient analysis through Raspberry Pi using NIR spectroscopy to lessen unnecessary efforts by farmers on going to regional soil test laboratories and to provide economical soil analysis. The device used TCD1304AP driven by Arduino MEGA 2560. With Internet of Things (loT), configured to Raspberry Pi through Django and pgAdmin, anyone connected to the provided WiFi may access the webpage for the results including the soil contents, pH level, crop recommendations, and potential solutions and treatments for the soil. The processed data showed 95% accuracy in measuring soil macronutrient concentration and 44.44% accuracy in measuring soil contaminants concentration. The low percentage for contaminants was due to very low numerical values and the limited number of samples so to further describe the performance of the device, percent error and difference were calculated. The device measured the contaminant concentrations with a 25.56% error and 28.57% difference. Author's Abstract

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Holdings
Item type	Current library	Shelving location	Call number	Status	Notes	Date due	Barcode
Bachelor's Thesis COE	TUP Manila Library	Thesis Section-2nd floor	BTH TK 7816 A46 2020 (Browse shelf(Opens below))	Not for loan	For room use only		BTH0003207

Thesis (Undergraduate)

College of Engineering-- Bachelor of Science in Electronics Engineering, Technological University of the Philippines, 2019.

Soil nutrients (Nitrogen, Phosphorus, and Potassium) of a certain agricultural aren have their selectivity or specificity for the highest potential crop yield. On the other hand, common heavy metals (Arsenie, Cadmium, Lead, and Mercury) found on soil sediments especially beneath polluted bodies of water also became part of our local farmers' struggles. These correspond to laboratory methods used to solve or lessen the amount of the heavy metals content and produce the desired amount of nutrients by applying appropriate fertilizers. The said problems can be managed through efficient soil analysis.
The main objective of this study is to develop a multifunction loT-based device for soil contaminants and macronutrient analysis through Raspberry Pi using NIR spectroscopy to
lessen unnecessary efforts by farmers on going to regional soil test laboratories and to provide economical soil analysis. The device used TCD1304AP driven by Arduino MEGA
2560. With Internet of Things (loT), configured to Raspberry Pi through Django and pgAdmin, anyone connected to the provided WiFi may access the webpage for the results including the soil contents, pH level, crop recommendations, and potential solutions and treatments for the soil. The processed data showed 95% accuracy in measuring soil macronutrient concentration and 44.44% accuracy in measuring soil contaminants
concentration. The low percentage for contaminants was due to very low numerical values and the limited number of samples so to further describe the performance of the device, percent error and difference were calculated. The device measured the contaminant concentrations with a 25.56% error and 28.57% difference. Author's Abstract

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