Broomrape (Orobanche and Phelipanche spp.) parasitism is a severe problem in many crops worldwide, including in the Mediterranean basin. Most of the damage occurs during the sub-soil developmental stage of the parasite, by the time the parasite emerges from the ground, damage to the crop has already been done. One feasible method for sensing early, below-ground parasitism is through physiological measurements, which provide preliminary indications of slight changes in plant vitality and productivity. However, a complete physiological field survey is slow, costly and requires skilled manpower. In recent decades, visible to-shortwave infrared [VIS-SWIR] hyperspectral tools have exhibited great potential for faster, cheaper, simpler and non-destructive tracking of physiological changes. The advantage of VIS-SWIR is even greater when narrow-band signatures are analyzed with an advanced statistical technique, like a partial least squares regression [PLS-R]. The technique can pinpoint the most physiologically sensitive wavebands across an entire spectrum, even in the presence of high levels of noise and collinearity. The current study evaluated a method for early detection of O. cumana parasitism in sunflower that combines plant physiology, hyperspectral readings and PLS-R. Seeds of susceptible and resistant O. cumana sunflower varieties were planted in infested (15 mg kg-1 seeds) and non-infested soil. The plants were examined weekly to detect any physiological or structural changes; the examinations were accompanied by hyperspectral readings. During the early stage of the parasitism, significant differences between infected and non-infected sunflower plants were found in the reflectance of near and shortwave infrared areas. Physiological measurements revealed no differences between treatments until O. cumana inflorescences emerged. However, levels of several macro- and microelements tended to decrease during the early stage of O. cumana parasitism. Analysis of leaf cross-sections revealed differences in range and in mesophyll structure as a result of different levels of nutrients in sunflower plants, manifesting the presence of O. cumana infections. The findings of an advanced PLS-R analysis emphasized the correlation between specific reflectance changes in the SWIR range and levels of various nutrients in sunflower plants. This work demonstrates potential for the early detection of O. cumana parasitism on sunflower roots using hyperspectral tools.

Understanding the spectral characteristics of crops in response to stress caused by weeds is a basic step in improving the precision of agricultural technologies that manage weeds in the field. This research focused on the competition between corn (Zea mays) and redroot pigweed (Amaranthus retroflexus), a common weed that strongly reduces corn yield. The aim of this research was to characterize the physiological changes that occur in corn during early growth because of crop–weed competition and to examine the ability to detect the effect of competition through hyperspectral measurements. A greenhouse experiment was conducted, and corn plants were examined during early growth, with and without weed competition. Hyperspectral measurements were combined with physiological measurements to examine the reflectance and photosynthetic activity of corn. Changes were expected to appear mainly in the short-wave infrared region (SWIR) due to competition for water. Relative water content (RWC), chlorophyll content, photosynthetic rate, and stomatal conductance were reduced in the presence of weeds, and intercellular CO₂ levels increased. Deeper SWIR light absorption occurred in the weed treatment as expected, accompanied by spectral changes in the visible (VIS) and near infrared (NIR) ranges. The results highlight the potential of using spectral measurements as an indicator of competition for water.

The study compared the effects of water stress under ambient (350 μmol mol⁻¹) and elevated (700 μmol mol⁻¹) carbon dioxide concentrations on canopy carbon assimilation rates, soil water use, and water use efficiency (WUE) of cotton plants on a daily basis. This research was carried out in four sun-lit SPAR (Soil Plant Atmosphere Research) units with soil bins located at Beltsville, Maryland, USA. Whole canopy net photosynthetic rates were recorded at 5-min intervals, and soil water content was measured hourly using time-domain reflectometry. Pre-dawn and mid-day leaf water potentials were measured during the water stress and the recovery periods. The soil water use rates of plants grown under elevated CO₂ were significantly lower than those grown under ambient levels for both water-stressed and well-watered plants. The WUE of the ambient treatment did not begin to increase until most of the available water was depleted from the soil. The elevated CO₂ non-irrigated treatment was able to maintain carbon assimilation rates similar to that of the well-watered treatment while decreasing water uptake during the first 10 days of the drying period. Both water use and carbon assimilation rates decreased in the ambient non-irrigated treatment, shortly after withholding water. Plants grown at 350 μmol mol⁻¹ CO₂ concentration depleted more water from the deepest part of the profile (0.35–0.85 m) than did the plants from the elevated CO₂ treatment.

Root hairs are essential for nutrient uptake and plant-microbe interactions, playing a vital role in plant health and agricultural productivity. They extend from the surface of root cells, significantly increasing the root surface area, and constitute roughly 70% of the total root area. Given these advantages, detecting root hairs in scenes with low resolution is challenging. Therefore, we have proposed a study that utilizes unsupervised image super-resolution methods to reconstruct finer details for root hairs using the dataset captured from our novel scanning camera known as RootCam. RootCam is a fully automated tool designed for monitoring and capturing plant root images for different vision tasks for a more accurate representation of their morphology and root trait measurements. Root hair super-resolution proves to be a powerful tool for root biology and its applications in precision agriculture. To the best of the authors' knowledge, this research study is the first that mainly focuses on root hairs and their trait measurement improvement using super-resolution. By visualizing the rhizosphere in high-resolution detail, we are able to notice a significant improvement in bell-pepper plant root hair count from 7 to 12, total root length from 0.32 to 1 mm, and root hair density (number of root hairs/mm) from 2.7 to 4.63, as upscaling factors rise from 2 to 8, respectively, when compared with bicubic and contrastive learning semi-supervised remote sensing image super (CLSR) for super-resolution. Researchers and farmers can make informed decisions about nutrient placement, irrigation management, and crop selection, optimizing resource use efficiency and crop yields.