Table 2.

Different imaging techniques, plant assessment studies conducted using the imaging technique, and their advantages and limitations

Imaging technique	Studies conducted using the technique	Advantages	Limitations
Satellite Imagery	Heat stress (Cârlan et al. 2020), wheat yield (Fieuzal et al. 2020), cotton yield (He and Mostovoy 2019), dry bean (Sankaran et al. 2019), drought assessment (Babaeian et al. 2019), hessian Fly infestation wheat (Bhattarai et al. 2019), crop water stress (Omran 2018), alien invasive species (Royimani et al. 2018), purple spot diseases asparagus (Navrozidis et al. 2018), phytophthora root rot in avocado (Salgadoe et al. 2018), heavy metal induced stress rice (Liu et al. 2018), wheat yellow rust (Zheng et al. 2018), cotton root rot (Song et al. 2017), red palm weevil attack (Bannari et al. 2017), wheat biomass (Dong et al. 2016), powdery mildew (Yuan et al. 2016), orange rust sugarcane (Apan et al. 2004)	Easily covers very large areas	High cost of satellite and their launch
		The data can easily help predict droughts and epiphytotics as very large areas can be covered at the same time	RGB imaging is hindered by clouds and inclement weather
			Temporal cycle of satellite limits use at any time
Mobile Cameras	Cercospora leaf spot(Hallau et al. 2017), iron deficiency chlorosis severity in soybean(Naik et al. 2017), salinity stress tolerance (Awlia et al. 2016)	Convenient and portable	No practical use in research as limited to only a handful of samples
		Rapid
		No operational costs
UAV/Drone imaging	Target spot and Bacterial spot Tomato (Abdulridha et al. 2020), iron deficiency soybean (Dobbels and Lorenz 2019), early stress detection (Sagan et al. 2019), dry bean (Sankaran et al. 2019), grain yield wheat (Hassan et al. 2019), plant Nitrogen content (Camino et al. 2018), wheat biomass (Yue et al. 2017), maize yield (Maresma et al. 2016), grapevine leaf stripe (Gennaro et al. 2016), bacterial Leaf Blight in rice (Das et al. 2015), low-nitrogen (low-N) stress tolerance (Zaman-Allah et al. 2015)	Very economical	Cannot cover very large areas like a satellite
		Can cover large areas	Creating orthomosaics out of a myriad of sectional images
		High resolution data	Limited battery capacity
		Easy to operate with low learning curve
Imaging using robots	Nitrogen content maize (Chen et al. 2021), vegetation indices (Bai et al. 2019), Xylella fastidiosa infection Olive (Rey et al. 2019), leaf traits (Atefi et al. 2019), plant architecture (Qiu et al. 2019), heat stress and stripe rust resistance wheat (Zhang et al. 2019a), plant architecture (Young et al. 2018)	Most advanced technique	Still an evolving technique
		Highly efficient as it provides human-like manual phenotyping results	Much work required for workflow and data management
		Test images are run through models autonomously to assess plant health, no need of separate evaluation with spectral indices	High initial cost of equipment
		Programmable to act like a cron job, increases efficiency	Programming skills are required