Linking physiological parameters with visible/near-infrared leaf reflectance in the incubation period of vascular wilt disease

Graphene-Cu Nanocomposites Induce Tolerance against Fusarium oxysporum, Increase Antioxidant Activity, and Decrease Stress in Tomato Plants

The tomato crop is susceptible to various types of stress, both biotic and abiotic, which affect the morphology, physiology, biochemistry, and genetic regulation of plants. Among the biotic factors, is the phytopathogen Fusarium oxysporum f. sp. lycopersici (Fol), which can cause losses of up to 100%. Graphene-Cu nanocomposites have emerged as a potential alternative for pathogen control, thanks to their antimicrobial activity and their ability to induce the activation of the antioxidant defense system in plants. In the present study, the effect of the Graphene-Cu nanocomposites and the functionalization of graphene in the tomato crop inoculated with Fol was evaluated, analyzing their impacts on the antioxidant defense system, the foliar water potential (Ψ_h), and the efficiency of photosystem II (PSII). The results demonstrated multiple positive effects; in particular, the Graphene-Cu nanocomposite managed to delay the incidence of the "vascular wilt" disease and reduce the severity by 29.0%. This translated into an increase in the content of photosynthetic pigments and an increase in fruit production compared with Fol. In addition, the antioxidant system of the plants was improved, increasing the content of glutathione, flavonoids, and anthocyanins, and the activity of the GPX, PAL, and CAT enzymes. Regarding the impact on the water potential and the efficiency of the PSII, the plants inoculated with Fol and treated with the Graphene-Cu nanocomposite responded better to biotic stress compared with Fol, reducing water potential by up to 31.7% and Fv/Fm levels by 32.0%.

The impact of tritium phytoremediation on plant health as measured by fluorescence

Phytoremediation, using plants for soil, sediment, or water contaminant clean-up, is an established technology dependent on plant health. Tritium (3H), a radioactive isotope of hydrogen that is generally found in the environment as tritiated water (HTO), is a low-level beta emitter with a half-life of 12.32 years. Chlorophyll fluorescence (CF) for monitoring risk assessment of tritium to plant health was conducted at the Tritium Irrigation Facility (TIF) located on the US Department of Energy's Savannah River Site (SRS) near Aiken, SC. Two fluorometers were evaluated in conjunction with phytoremediation at the 25 -acre TIF where tritiated groundwater is being spray-irrigated on a mixed coniferous/deciduous forested watershed as a means of reducing tritium release to a nearby stream that serves as a tributary to the Savannah River. Tritium activity in irrigated water averaged 104 + 42 pCi mL-1 during the 2003 project. Fluorescence parameters measured by the two fluorometers were well correlated with each other (p < 0.0001). Tritium in water respired from oak leaves ranged up to 1845.13 pCi ml-1 and 2138.22 pCi ml-1 in pine needles. Trees in both the test and control sites were approximately 15 years old. Here we demonstrated that fluorescence parameters provide an effective way to estimate the impact of HTO on plant health in a noninvasive, extremely rapid, and cost-effective manner. In the current study applying fluorometry, plants within the TIF phytoremediation site exposed to the site tritiated water were not significantly impacted by the tritium phytoremediation based on CF parameters as compared to the control, a nascent non-irrigated site.

Prediction of South American Leaf Blight and Disease-Induced Photosynthetic Changes in Rubber Tree, Using Machine Learning Techniques on Leaf Hyperspectral Reflectance

The efficiency of visible and near-infrared (VIS/NIR) sensors and predictive modeling for detecting and classifying South American Leaf Blight (SALB) (Pseudocercospora ulei) in rubber trees (Hevea brasiliensis) has been poorly explored. Furthermore, the performance of VIS/NIR analysis combined with machine learning (ML) algorithms for predicting photosynthetic alterations caused by SALB is unknown. Therefore, this study aimed to detect and classify the SALB levels, as well as to predict, for the first time, disease-induced photosynthetic changes in rubber trees. Leaf hyperspectral reflectance combined with five ML techniques (random forest (RF), boosted regression tree (BRT), bagged classification and regression trees (BCART), artificial neural network (ANN), and support vector machine (SVM)) were used. The RF, ANN, and BCART models achieved the best performance for classifying the SALB levels on the training dataset (accuracies of 98.0 to 99.8%), with 10-fold cross-validation repeated five times, and test dataset (accuracies of 97.1 to 100%). The ANN and RF models were better at predicting leaf gas exchange-related traits such as net CO₂ assimilation rate (A) and extrinsic water use efficiency (WUEe) in the training (R² ranged from 0.97 to 0.99) and testing (R² ranged from 0.96 to 0.99) phases. In comparison, lower performances (R² ranged from 0.24 to 0.52) were evidenced for the photochemical traits. This research provides a basis for future designs of a remote monitoring system based on early detection and accurate diagnosis of biotic stress caused by SALB, which is fundamental for more effective rubber crop protection.

Comparative Effect of Inoculation of Phosphorus-Solubilizing Bacteria and Phosphorus as Sustainable Fertilizer on Yield and Quality of Mung Bean (Vigna radiata L.)

Globally, the availability of phosphorus (P) to crops remains limited in two-thirds of the soils, which makes it less accessible to plants and ultimately associated with low crop yields. The present study investigated the effect of phosphorus-solubilizing bacteria (PSB; Pseudomonas spp.) for the improvement of phosphorus in mung bean (Vigna radiata) varieties and growth of net grain and biological yields. Results showed that inoculation of mung bean varieties with PSB at the rate of 100 g/kg seed significantly improved the root and shoot dry weight of about 1.13 and 12.66 g, root and shoot length of 14.49 and 50.63 cm, root and shoot phosphorus content of 2629.39 and 4138.91 mg/kg, a biological yield of 9844.41 kg/ha, number of pods of 17 per plant, number of grains of 9 per pod, grain yield of 882.23 kg/ha, and 1000-grain weight of 46.18 g after 60 days of observation. It was also observed that PSB-treated varieties of mung bean showed the maximum photosynthetic yield, photosynthetic active radiation, electron transport rate, and momentary fluorescent rate of 0.75, 364.32, 96.12, and 365.33 μmol/m² s, respectively. The highest harvest index of 13.28% was recorded by P-treated mung beans. Results disclosed that inoculation of seeds of mung bean with PSB exhibited different effects in measured parameters. It is concluded that PSB possessed remarkable results in measured parameters compared to the control and highlighted that PSB could be an effective natural sustainable fertilizer for mung bean cultivation in sandy soil.

Precision Agriculture Digital Technologies for Sustainable Fungal Disease Management of Ornamental Plants

Ornamental plant production constitutes an important sector of the horticultural industry worldwide and fungal infections, that dramatically affect the aesthetic quality of plants, can cause serious economic and crop losses. The need to reduce the use of pesticides for controlling fungal outbreaks requires the development of new sustainable strategies for pathogen control. In particular, early and accurate large-scale detection of occurring symptoms is critical to face the ambitious challenge of an effective, energy-saving, and precise disease management. Here, the new trends in digital-based detection and available tools to treat fungal infections are presented in comparison with conventional practices. Recent advances in molecular biology tools, spectroscopic and imaging technologies and fungal risk models based on microclimate trends are examined. The revised spectroscopic and imaging technologies were tested through a case study on rose plants showing important fungal diseases (i.e., spot spectroscopy, hyperspectral, multispectral, and thermal imaging, fluorescence sensors). The final aim was the examination of conventional practices and current e-tools to gain the early detection of plant diseases, the identification of timing and spacing for their proper management, reduction in crop losses through environmentally friendly and sustainable production systems. Moreover, future perspectives for enhancing the integration of all these approaches are discussed.

Investigation on the effects of water loss on the solar spectrum reflectance and transmittance of Osmanthus fragrans leaves based on optical experiment and PROSPECT model

Water is the main determinant of the leaf spectral characteristics in the shortwave infrared region, whereas only changing the water content in the PROSPECT model cannot accurately describe the solar spectrum reflectance and transmittance of the dehydrated leaf. To elucidate the effects of water loss, the solar spectrum reflectances and transmittances of the Osmanthus fragrans leaves in the fresh state, natural air-dry state and oven-dry state were measured, and the leaf parameters were predicted by the PROSPECT model inversion. The results revealed that the first effect was to increase the brown pigment content, which led to an increase in leaf absorption and change of the leaf absorption characteristics, and correspondingly, in the visible region, both the reflected and transmitted radiations were decreased and the reflection peak shifted towards a long wavelength. The other two effects were to increase the leaf structure index and refractive index, which resulted in an enhancement of the reflected radiation and an attenuation of the transmitted radiation over the range from 400 to 2500 nm. These findings suggest that if people consider the changes of leaf pigment content, structure and refractive index when water is lost from an actual leaf, it will be expected to improve the monitoring accuracy of the leaf water content based on leaf spectral remote sensing technology.

In addition to reducing water content, leaf water loss also exerted three effects on the leaf reflectance and leaf transmittance, i.e., the increases of brown pigment content, leaf refractive index, and leaf internal structure index.

Functional Hyperspectral Imaging by High-Related Vegetation Indices to Track the Wide-Spectrum Trichoderma Biocontrol Activity Against Soil-Borne Diseases of Baby-Leaf Vegetables

Research has been increasingly focusing on the selection of novel and effective biological control agents (BCAs) against soil-borne plant pathogens. The large-scale application of BCAs requires fast and robust screening methods for the evaluation of the efficacy of high numbers of candidates. In this context, the digital technologies can be applied not only for early disease detection but also for rapid performance analyses of BCAs. The present study investigates the ability of different Trichoderma spp. to contain the development of main baby-leaf vegetable pathogens and applies functional plant imaging to select the best performing antagonists against multiple pathosystems. Specifically, sixteen different Trichoderma spp. strains were characterized both in vivo and in vitro for their ability to contain R. solani, S. sclerotiorum and S. rolfsii development. All Trichoderma spp. showed, in vitro significant radial growth inhibition of the target phytopathogens. Furthermore, biocontrol trials were performed on wild rocket, green and red baby lettuces infected, respectively, with R. solani, S. sclerotiorum and S. rolfsii. The plant status was monitored by using hyperspectral imaging. Two strains, Tl35 and Ta56, belonging to T. longibrachiatum and T. atroviride species, significantly reduced disease incidence and severity (DI and DSI) in the three pathosystems. Vegetation indices, calculated on the hyperspectral data extracted from the images of plant-Trichoderma-pathogen interaction, proved to be suitable to refer about the plant health status. Four of them (OSAVI, SAVI, TSAVI and TVI) were found informative for all the pathosystems analyzed, resulting closely correlated to DSI according to significant changes in the spectral signatures among health, infected and bio-protected plants. Findings clearly indicate the possibility to promote sustainable disease management of crops by applying digital plant imaging as large-scale screening method of BCAs' effectiveness and precision biological control support.