Table 1.

A brief overview of the literature.

Research Areas	Technique(s)/References	Research Focus	Finding(s)	Application(s)	Limitation(s)	Author(s)
Image monitoring	Thermal imaging [1]	Nondestructive plant physiology monitoring	Stress at an early stage was alleviated. Irreversible damage and yield loss were prevented.	Scales	A few square centimeters can be studied	Chaerle L. et al., 2000.
	RGB imaging [2,28,29,30]	Identification, quantification, and monitoring of plant diseases	Diseases in Cotton, Apple, Grapefruit, and Canadian goldenrod were identified.	Precision in agriculture, plant phenotyping	Low image quality, low detection accuracy	Mahlein, 2016 Camargo and Smith, 2009; Bock et al., 2008; Wijekoon et al., 2008
	Fluorescent imaging [3]	Leaf damage without visible signs, photosynthesis analysis, and fluorimeter comparison of PSM, MFMS, PAM101	A significant difference between same-population leaves and photosynthesis changes was observed. Tree and branch damage patterns were identified.	Natural vegetation, ecological research	Application-dependent	Bolhar-Nordenjampf et al., 1989
	Hyperspectral imaging [4]	Ground-based hyperspectral reflectance of yellow rust disease inoculation. Nutrient-stressed treatment to detect and discriminate yellow rust disease from nutrients.	At major growth stages, four vegetation indices clearly responded to disease. Disease and nutrient stress affected most spectral features. The physiological reflectance index was disease-sensitive.	Disease monitoring and mapping	Cost and complexity	Zhang J et al., 2012
Spectroscopy	X-ray fluorescence [5]	Benchtop XRF to evaluate the elemental distribution change in living plant tissue exposed to X-rays	Higher Zn content than Mn in stems was found. The latter micronutrient presented a higher concentration in leaf veins.	Plant tissue analyses under in vivo conditions	X-rays injure biological tissue	Montanha et al., 2020
	Mass [6]	Monitoring the auxin-regulated nicotine biosynthesis in tobacco and evaluating possible biological effects	Rupture of trichomes and cell damage were observed on spots exposed to Low-Temperature Plasma.	Biosynthesis of plant surface in vivo measurement	Destructive to live cell structure	Martínez-Jarquín et al., 2018
	Raman [7]	High-throughput stress phenotyping of plant measurement	Unique negative correlation between concentration levels of anthocyanins and carotenoids was observed.	Plant stress in vivo	Destructive method	Altangerel et al., 2017
Electrical-based sensor approaches	Microneedle electrodes [8,9]	Measure the xylem sap flow to understand plant physiology	Good adaptation of the microneedle probe in the plant tissue was possible.	Plant physiology (tomato)	Not accurate	Baek et al., 2018; Daskalakis et al., 2018
	Organic electrochemical transistor [10]	Real-time monitoring of the electrolyte of tomato plant’s physiological state	A circadian pattern of variation was revealed, which shows the possibility to detect signs of abiotic stress.	Precision farming, plant physiology	Slow response to plant change, low accuracy	Coppedè et al., 2017
EIS	ZARC-cole and CPE model; CNLS using LEVM7 [14]	Develop EIS for nondestructively evaluating plant root growth of willows	Sum of R1 and R2 in the distributed electric model decreased with an increase in root mass.	Root growth assessment	Minor damage due to insertion of needle’s electrode	Repo et al., 2005
	Single-DCE model; CNLS using LEVM v.6 program [15]	Cold acclimation and measurement of frost hardening.	Both quantitative and qualitative changes in cell membranes and water-status were observed. EIS results indicate weaker hardiness than other tests.	Frost hardening capability measurement	System dependent with limited functionality	Väinölä and Repo, 2000
	Different models; nlmin function in S-PLUS [16,17]	Track the electrical change response of fruit physiology and analyze their ripening	Cell wall and vacuole resistance decreased by 60% and 26%, respectively, and membrane capacitance decreased by 9%.	Fruit and vegetable quality measurement	System-dependent, which may limit functionality	Bera et al., 2017; Harker and maindonald, 1994
	Electrical parameter (Z, R, Y, G), with simple linear regression analysis [19]	Determining the effect of Total Soluble Solids (TSS) on the electrical conductivity of reconstituted apple juice.	EIS parameters are good for determining TSS content. Rapid determination of the TSS content in different fruit juices, detection of their adulteration	Food quality measurement	Lack of changes in physicochemical qualities	Żywica and Banach, 2015.
	Sensor based on four electrodes and three indexes as indicators of leaf water content [20]	Develop water-saving agriculture and increase water-use efficiency	Negative correlation with all three parameters was observed. Relative water content showed the best correlation with the leaf property.	Crop production (leaf water content)	Existence of a severe fringe effect	Zheng et al., 2014.
	Four different models; CNLS method [24]	Determine the best electrical model for plant tissues analysis	Plant tissue conformed better to a double-shell model than others.	Plant tissue analysis	-	Zhang and Willison, 1991.
	* Finding R-C with DSM to understand plant physiology	Monitor and understand leaf physiology for 16 h using double-shell model parameters and a comparative analysis.	$\mathrm{Ratios} \mathrm{of} R_1/R_4 = 10.66, R_1/R_2=3.34, \mathrm{and} R_2/R_4=3.34$ were obtained. The results confirmed previous studies in the literature of. Rapid changes in R1, R2, C3, and C5 were noticeable after water uptake. Possibility of detecting a plant with slow growth status.	Plant physiology for crop production and precision in agriculture.	System-dependent	Proposed

* The outcome of this study is presented in this table (in the last row) and can be used as a comparison with previous studies.