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World population will grow to 9.6 billion by 2050 and global food production needs to be increased by 70% to feed the increased population. Hence, better insight into plant physiology can impart better quality in fruits, vegetables, and crops, and eventually contribute to food security and sustainability. In this direction, this thesis utilizes electrical sensing technology, electrical impedance spectroscopy (EIS) and tomography (EIT), for better understanding and characterization of a number of physiological and structural aspects of the plant. It investigates the dehydration process in onion and ripening process in avocado by EIS, and perform 3D structural imaging of root by EIT. The thesis tracks and analyzes the dynamics of natural dehydration in onion and also assesses its moisture content using EIS. The work develops an equivalent electrical circuit that simulates the response of the onion undergoing natural drying for a duration of three weeks. The developed electrical model shows better congruence with the experimental data when compared to other conventional models for plant tissue with a mean absolute error of 0.42% and root mean squared error of 0.55%. Moreover, the study attempts to find a correlation between the measured impedance data and the actual moisture content of the onions under test (measured by weighing) and develops a simple mathematical model. This model provides an alternative tool for assessing the moisture content of onion nondestructively. The model shows excellent correlation with the ground truth data with a deterministic coefficient of 0.977, root mean square error of 0.030 and sum of squared error of 0.013. Next, the thesis presents an approach that will integrate EIS and machine learning technique that allows us to monitor ripening degree of avocado. It is evident from this study that the impedance absolute magnitude of avocado gradually decreases as the ripening stages (firm, breaking, ripe and overripe) proceed at a particular frequency. In addition, Principal component analysis shows that impedance magnitude (two principal components combined explain 99.95% variation) has better discrimination capabilities for ripening degrees compared to impedance phase angle, impedance real part, and impedance imaginary part. The developed classifier utilizes two principal component features over 100 EIS responses and demonstrate classification over firm, breaking, ripe and overripe stages with an accuracy of 90%, precision of 93%, recall of 90%, f1-score of 90% and an area under ROC curve (AUC) of 88%. Later on, this thesis presents the design, development, and implementation of a low-cost EIT system and analyzes root imaging as well. The designed prototype consists of an electrode array system, an Impedance analyzer board, 2 multiplexer units, and an Arduino. The Eval-Ad5933-EBZ is used for measuring the bio-impedance of the root, and two CD74HC4067 Multiplexers are used as electrode switching unit. Measuring and data collecting are controlled by the Arduino, and data storage is performed in a PC. By performing Finite Element Analysis and solving forward and inverse problem, the tomographic image of the root is reconstructed. The system is able to localize and build 2D and 3D tomographic image of root in a liquid medium. This proposed low-cost and easy-to-access system enables the users to capture the repetitive, noninvasive and non-destructive image of a plant root. Furthermore, the study proposes a simple mathematical model, based on ridge regression, which can predict root biomass from EIT data nondestructively with an accuracy of more than 93%. Thus, this study offers plant scientists and crop consultants the ability to better understand plant physiology nondestructively and noninvasively.



Electrical Impedance Spectroscopy, Electrical Impedance Tomography, Onion, Avocado, Root imaging, Moisture content, Ripening



Master of Science (M.Sc.)


Electrical and Computer Engineering


Electrical Engineering


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