Comparison of Proximal Remote Sensing Devices of Vegetable Crops to Determine the Role of Grafting in Plant Resistance to Meloidogyne incognita

Abstract

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 (x¯ = 1.37) and showed lower CSI (x¯ = 11.65) than non-grafted plants (x¯ = 1.98 and 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.

Characterization of Some Aromatic and Medicinal Plants Species Based on Uav Images

“…Techniques based on imaging analysis (remote sensing, UAV) were used to analyze, characterize and discriminate some species of medicinal plants, in relation to temporal and spatial variability, or plant health (Kumar, 2010;Kandpal et al, 2021;Hamdane et al, 2022;Ding et al, 2023).…”

Artificial intelligence applications in precision agriculture to predict the effect of Root-Knot nematodes and grafting on vegetable crop health from proximal remote sensing machines

“…Therefore, it is necessary to take over that high populations of the nematode beget a meaningful reduction of the crop biomass, which allows the employment of vegetation indexes that offer a good correlation with the biomass to indicate places with a lesser probability of circumstance of nematodes. In this sense, the use of hyperspectral sensors associated with novel techniques of data analysis can allow the differentiation of the causes of variability and isolate the sites with nematode problems with greater accuracy (Hunt et al 2013;Hamdane et al 2022). In fact, with variable rate application, deductions of 40 in the volume of nematicide related have been observed, substantially in the areas of a field where the nematodes aren't yet extant and the product doesn't require to be applied.…”

Advances and Sustainable Practices for the Rapidly Changing Field of Agronomy

“…In addition, the authors highlighted that when soil indicators such as soil texture and organic matter were included in the remote sensing-based machine learning models, the spatial predictions of N, P, and K were improved, thus leading to the creation of digital agrochemical maps on a single-field scale. In the same context of proximal remote sensing, Hamdane et al [10] and coworkers used rapid and non-destructive assessment techniques such as proximal remote sensing and field sensors to evaluate the interaction between grafting and nematode presence on the physiological status of diverse crops. Within this scope, the authors compared proximal remote sensing devices to depict the roles of pepper, eggplant, tomato, and melon grafting on a resistant rootstock against Meloidogyne incognita in a greenhouse experiment using infested soil.…”