Precision agriculture is understood as the set of techniques aimed at optimizing agricultural inputs according to the spatial and temporal variability of production, which is achieved with the correct allocation of inputs depending on the needs and potential of each management area (Chartuni et al., 2007; Hedley, 2015; Köksal & Tekinerdogan, 2019; Bhakta et al., 2019). In order to study, evaluate and understand spatial and temporal variations, it is necessary to use technologies such as Global Positioning System (GPS), satellites, remote sensors and images of the area, linked to Geographic Information Systems (GIS) (Croner et al., 1996; Xue et al., 2002; Seelan et al., 2003; Sonti, 2015; Banu, 2015; Thakur et al., 2017). In this sense precision agriculture provides georeferenced information through maps of soil fertility or yield, which using remote sensors is capable to generate immediate information, as well as distributing agricultural inputs in an optimal way, in order to satisfy food needs (Roberson, 2000; Fulton et al., 2018; Lund et al., 1999; Abdullahi et al., 2015).
Hereby, nanotechnology focuses on the development, characterization and use of materials with very small dimensions such as < 100 nm (Weiss et al., 2006; Pal et al., 2011; Sawhney et al., 2008). In this group of materials are nanoparticles, which have great potential in agriculture since they are able to be used as nanofertilizers (Raliya et al., 2017; Arguello, 2016; Zulfiqar et al., 2019; Liu and Lal, 2015). Nanofertilizers focuses on the synthesis of macro and micro nutritive elements for crops, but mainly on micronutrients (Zn, Cu, Mn and Fe) (Manjunatha et al., 2016; Dimkpa & Bindraban, 2017; Guha et al., 2020). Plants need mineral nutrients to complete their life cycle, since they are involved in metabolic functions and are part of the organic structure. These nutrients are classified according to their concentration, being macronutrients which are required in large quantities (N, P, K) and micronutrients in small amounts (Ca, Mg, S, Fe, Mn, B, Zn, Co, Mo), while they have the same importance as macronutrients (Sinfield et al., 2010; Ejaz et al., 2011; Singh and Mishra, 2012; Toor et al., 2021). Iron plays an important role as components of the enzymes involved in the transfer of electrons in the process of photosynthesis (Rout & Sahoo, 2015; Imsande, 1998). The lack of this micronutrient is observed in young leaves as it presents interveinal chlorosis, which reduces the synthesis of complexes protein-chlorophyll in chloroplasts, while the lack of zinc causes the reduction of the growth of internodes (Das, 2014; Magri et al., 2020). Therefore, the leaves may be small, deformed and present chlorosis between the nerves of the old leaves (Zeiger & Taiz, 2006; Rajendran et al., 2009; Zhao et al., 2016).
Lupine or chocho (Lupinus mutabilis Sweet) is an Andean legume, which has a high protein content (approximately 46% in dry grain), compared to the rest of legumes and other Andean cereals (Muñoz et al., 2018; Mikić et al., 2013; Jacobsen & Mujica, 2008; Miano et al., 2015; Mora Villacís et al., 2020; Murgueitio-Herrera et al., 2022). This legume provides phosphorus, iron and zinc, which are the main minerals in the human body, therefore, it is considered a product that contributes to the food sovereignty of many countries. The lupine is part of the variety of agricultural products that have an important nutritional content, because it has a high percentage of protein, and other nutrients that are essential for human health and for food sovereignty in Ecuador (Villacrés et al., 2006; Giunta, 2014; Peña, 2016).
In Ecuador, lupine cultivation is located in the highland region, where it is capable of adapting to different types of soil, especially in dry and sandy agro-ecological zones between 2,600 and 3,400 meters above sea level (Falconí et al., 2013; Carlos et al., 2018; Struelens et al., 2021). It develops in environments where rainfall fluctuates between 300 to 600 mm per year and with a temperature between 7 and 14 ° C (Blackmore et al., 2021; Caicedo & Peralta, 2000).
Anthracnose is a disease that affects stems, leaves, pods and seeds, in all stages of lupine plant development, therefore, this problem alters the spectral response and therefore the value of the NDVI vegetation index, which allows evaluating the health of the vegetation (Falconí, 2012; Horning, 2008).
Remote sensing is an useful technology for precision agriculture, as with the use of satellite platforms for agricultural management they are very important to monitor agricultural production (Brisco et al., 1998; Segarra et al., 2020; Khanal et al., 2017; Torres et al., 2022). However, within the agricultural-scale applications they have not been adopted as expected due to a variety of challenges such as pixel resolutions, infrequent coverage, clouds and slow delivery of information to users (Saiz-Rubio & Rovira-Más, 2020). Nonetheless, unmanned aerial vehicles (UAV) or drones provide a remote sensing platform with the ideal characteristics for data acquisition, such as high spatial resolution, on-demand coverage, and fast information delivery (Nebiker et al., 2008; Feng et al., 2015; Hunt & Daughtry, 2017)
Remote sensing, being a discipline that obtains information from an element by detecting and analyzing its radiated energy, lays its foundations in the study of spectroscopy (Gholizadeh & Kopačková, 2019). This refers to the observation and study of the electromagnetic spectrum, and is based on the interaction of radiant energy with matter, that is, it measures the intensity of radiation as a function of the wavelength of an object (Pu, 2017). That implies the measurement of energy in many parts of the electromagnetic spectrum, which allows monitoring the vegetation in a spatial and temporal way, make predictions of crop production, monitoring of agricultural land use, as well as early detection and control of diseases of the crop (Weiss & Jacob, 2020; Yang, 2020; Viera-Torres et al., 2020).
There are several remote sensing studies focused on analyzing the spectral response across the phenological stages of crops, such as those by Psomas et al. (2005) on grasslands and Manevski et al. (2011) on Mediterranean plants. They are also notable a variety of research developed in the cultivation of lupine, where the spectral behavior of the crop was analyzed, after a seed disinfection application, implemented in greenhouse and field (Tapia-Silva et al., 2011; Kosewska et al., 2016; Sinde-González et al., 2021).
Based on the aforementioned, the present study aims predominantly to determine the spectral variability in the phenological states of the lupine crop in two controlled trials (greenhouse and field). This shall be reached in order to analyze the effects of addition of chelates and nanofertilizers, for purposes of control and monitoring of this focused crop.