The use of satellites to monitor crops and support their management is gathering increasing attention. The improved temporal, spatial, and spectral resolution of the European Space Agency (ESA) launched Sentinel-2 A + B twin platform is paving the way to their popularization in precision agriculture. Besides the Sentinel-2 A + B constellation technical features the open-access nature of the information they generate, and the available support software are a significant improvement for agricultural monitoring. This paper was motivated by the challenges faced by researchers and agrarian institutions entering this field; it aims to frame remote sensing principles and Sentinel-2 applications in agriculture. Thus, we reviewed the features and uses of Sentinel-2 in precision agriculture, including abiotic and biotic stress detection, and agricultural management. We also compared the panoply of satellites currently in use for land remote sensing that are relevant for agriculture to the Sentinel-2 A + B constellation features. Contrasted with previous satellite image systems, the Sentinel-2 A + B twin platform has dramatically increased the capabilities for agricultural monitoring and crop management worldwide. Regarding crop stress monitoring, Sentinel-2 capacities for abiotic and biotic stresses detection represent a great step forward in many ways though not without its limitations; therefore, combinations of field data and different remote sensing techniques may still be needed. We conclude that Sentinel-2 has a wide range of useful applications in agriculture, yet still with room for further improvements. Current and future ways that Sentinel-2 can be utilized are also discussed.

Maize is the most cultivated cereal in Africa in terms of land area and production, but low soil nitrogen availability often constrains yields. Developing new maize varieties with high and reliable yields using traditional crop breeding techniques in field conditions can be slow and costly. Remote sensing has become an important tool in the modernization of field-based high-throughput plant phenotyping (HTPP), providing faster gains towards the improvement of yield potential and adaptation to abiotic and biotic limiting conditions. We evaluated the performance of a set of remote sensing indices derived from red–green–blue (RGB) images along with field-based multispectral normalized difference vegetation index (NDVI) and leaf chlorophyll content (SPAD values) as phenotypic traits for assessing maize performance under managed low-nitrogen conditions. HTPP measurements were conducted from the ground and from an unmanned aerial vehicle (UAV). For the ground-level RGB indices, the strongest correlations to yield were observed with hue, greener green area (GGA), and a newly developed RGB HTPP index, NDLab (normalized difference Commission Internationale de I´Edairage (CIE)Lab index), while GGA and crop senescence index (CSI) correlated better with grain yield from the UAV. Regarding ground sensors, SPAD exhibited the closest correlation with grain yield, notably increasing in its correlation when measured in the vegetative stage. Additionally, we evaluated how different HTPP indices contributed to the explanation of yield in combination with agronomic data, such as anthesis silking interval (ASI), anthesis date (AD), and plant height (PH). Multivariate regression models, including RGB indices (R² > 0.60), outperformed other models using only agronomic parameters or field sensors (R² > 0.50), reinforcing RGB HTPP’s potential to improve yield assessments. Finally, we compared the low-N results to the same panel of 64 maize genotypes grown under optimal conditions, noting that only 11% of the total genotypes appeared in the highest yield producing quartile for both trials. Furthermore, we calculated the grain yield loss index (GYLI) for each genotype, which showed a large range of variability, suggesting that low-N performance is not necessarily exclusive of high productivity in optimal conditions.

Ear density is one of the most important agronomical yield components in wheat. Ear counting is time-consuming and tedious as it is most often conducted manually in field conditions. Moreover, different sampling techniques are often used resulting in a lack of standard protocol, which may eventually affect inter-comparability of results. Thermal sensors capture crop canopy features with more contrast than RGB sensors for image segmentation and classification tasks. An automatic thermal ear counting system is proposed to count the number of ears using zenithal/nadir thermal images acquired from a moderately high resolution handheld thermal camera. Three experimental sites under different growing conditions in Spain were used on a set of 24 varieties of durum wheat for this study. The automatic pipeline system developed uses contrast enhancement and filter techniques to segment image regions detected as ears. The approach is based on the temperature differential between the ears and the rest of the canopy, given that ears usually have higher temperatures due to their lower transpiration rates. Thermal images were acquired, together with RGB images and in situ (i.e., directly in the plot) visual ear counting from the same plot segment for validation purposes. The relationship between the thermal counting values and the in situ visual counting was fairly weak (R² = 0.40), which highlights the difficulties in estimating ear density from one single image-perspective. However, the results show that the automatic thermal ear counting system performed quite well in counting the ears that do appear in the thermal images, exhibiting high correlations with the manual image-based counts from both thermal and RGB images in the sub-plot validation ring (R² = 0.75–0.84). Automatic ear counting also exhibited high correlation with the manual counting from thermal images when considering the complete image (R² = 0.80). The results also show a high correlation between the thermal and the RGB manual counting using the validation ring (R² = 0.83). Methodological requirements and potential limitations of the technique are discussed.

Main conclusion: By combining hyperspectral signatures of peanut and soybean, we predicted V_cmax and J_max with 70 and 50% accuracy. The PLS was the model that better predicted these photosynthetic parameters. One proposed key strategy for increasing potential crop stability and yield centers on exploitation of genotypic variability in photosynthetic capacity through precise high-throughput phenotyping techniques. Photosynthetic parameters, such as the maximum rate of Rubisco catalyzed carboxylation (V_c,max) and maximum electron transport rate supporting RuBP regeneration (J_max), have been identified as key targets for improvement. The primary techniques for measuring these physiological parameters are very time-consuming. However, these parameters could be estimated using rapid and non-destructive leaf spectroscopy techniques. This study compared four different advanced regression models (PLS, BR, ARDR, and LASSO) to estimate V_c,max and J_max based on leaf reflectance spectra measured with an ASD FieldSpec4. Two leguminous species were tested under different controlled environmental conditions: (1) peanut under different water regimes at normal atmospheric conditions and (2) soybean under high [CO₂] and high night temperature. Model sensitivities were assessed for each crop and treatment separately and in combination to identify strengths and weaknesses of each modeling approach. Regardless of regression model, robust predictions were achieved for V_c,max (R² = 0.70) and J_max (R² = 0.50). Field spectroscopy shows promising results for estimating spatial and temporal variations in photosynthetic capacity based on leaf and canopy spectral properties.

Proximal remote sensing devices are novel tools that enable the study of plant health status through the measurement of specific characteristics, including the color or spectrum of light reflected or transmitted by the leaves or the canopy. The aim of this study is to compare the RGB and multispectral data collected during five years (2016–2020) of four fruiting vegetables (melon, tomato, eggplant, and peppers) with trial treatments of non-grafted and grafted onto resistant rootstocks cultivated in a Meloidogyne incognita (a root-knot nematode) infested soil in a greenhouse. The proximal remote sensing of plant health status data collected was divided into three levels. Firstly, leaf level pigments were measured using two different handheld sensors (SPAD and Dualex). Secondly, canopy vigor and biomass were assessed using vegetation indices derived from RGB images and the Normalized Difference Vegetation Index (NDVI) measured with a portable spectroradiometer (Greenseeker). Third, we assessed plant level water stress, as a consequence of the root damage by nematodes, using stomatal conductance measured with a porometer and indirectly using plant temperature with an infrared thermometer, and also the stable carbon isotope composition of leaf dry matter.. It was found that the interaction between treatments and crops (ANOVA) was statistically different for only four of seventeen parameters: flavonoid (p < 0.05), NBI (p < 0.05), NDVI (p < 0.05) and the RGB CSI (Crop Senescence Index) (p < 0.05). Concerning the effect of treatments across all crops, differences existed only in two parameters, which were flavonoid (p < 0.05) and CSI (p < 0.001). Grafted plants contained fewer flavonoids ($\overline{\mathrm{x}}$ = 1.37) and showed lower CSI ($\overline{\mathrm{x}}$ = 11.65) than non-grafted plants ($\overline{\mathrm{x}}$ = 1.98 and $\overline{\mathrm{x}}$ = 17.28, respectively, p < 0.05 and p < 0.05) when combining all five years and four crops. We conclude that the grafted plants were less stressed and more protected against nematode attack. Leaf flavonoids content and the CSI index were robust indicators of root-knot nematode impacts across multiple crop types.

The systematic use of plant protection products is now being called into question with the growing awareness of the risks they can represent for the environment and human health. The application of precision agriculture technologies helps to improve agricultural production but also to rationalize input costs and improve ecological footprints. Here we present a study on fungicide application efficiency and its impact on the grass quality of a golf course green using the free open-source image analysis software FIJI (Image J) to analyze ground RGB (high-resolution digital cameras) and multispectral aerial imagery in combination with experimental data of spray pressure and hydraulic slot nozzle size of a boom sprayer machine. The multivariate regression model best explained variance in the normalized green-red difference index (NGRDI) as a relevant indicator of healthy turfgrass fields from the aerial, ground, and machine data set.