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

Screening of the Biocontrol Efficacy of Potent Trichoderma Strains against Fusarium oxysporum f.sp. ciceri and Scelrotium rolfsii Causing Wilt and Collar Rot in Chickpea

Chickpeas contribute to half of the pulses produced in India and are an excellent source of protein, fibers, carbohydrates, minerals, and vitamins. However, the combination of the wilt and root rot diseases drastically lowers its yield. The use of antagonist microbes that restrict the growth of other phytopathogens is an ecofriendly approach to combat the serious threats raised by the plant pathogens. Trichoderma spp. are well known as biocontrol agents, especially against soil- and seed-borne phytopathogens. In this study, 21 Trichoderma isolates that were collected from different rhizospheric soils were evaluated against two notorious soil-borne pathogens, such as Fusarium oxysproum f.sp. ciceri and Sclerotium rolfsii. The maximum percentage of inhibition against the tested pathogens was observed in Trichoderma isolate PBT13 (72.97%, 61.1%) followed by PBT3 (72.23%, 59.3%). The mycelial extension rate method, dual culture (antagonism), production of cell-wall degrading enzymes (CWDs), and antifungal metabolites (by GC-MS) were used as selection criteria for potent Trichoderma isolates. Among the 21 isolates, PBT3, PBT4, PBT9, and PBT13 exhibited high antagonistic activity, production of antifungal metabolites, and chitinase and β-1,3-glucanase activity. These four species were subjected to molecular characterization using an internal transcribed spacer (ITS 1 and ITS4). The results of molecular characterization identified the four species as T. virnes, T. asperellum, T. lixii, and T. harzianum. Moreover, significant chitinase and β-1,3-glucanase activities of all Trichoderma isolates were recorded in the growth medium. Trichoderma harzianum (isolate PBT13) was found to exhibit the highest chitinase activity in terms of zone formation (4.40 ± 0.17 cm), whereas Trichoderma virens (isolate PBT3) exhibited the highest β-1,3-glucanase activity1.511 μmole/min. A GC-MS analysis of ethyl extracts from two isolates of Trichoderma (PBT9, PBT13) revealed the presence of 28 VOCs. Overall, this study suggests that these four Trichoderma strains are promising biological control agents (BCAs) and could be developed as bio-pesticides after stringent field trials for the management of soil-borne diseases of chickpeas.

Active vs. Passive Thermal Imaging for Helping the Early Detection of Soil-Borne Rot Diseases on Wild Rocket [Diplotaxis tenuifolia (L.) D.C.]

Cultivation of wild rocket [Diplotaxis tenuifolia (L.) D.C.] as a baby-leaf vegetable for the high-convenience food chain is constantly growing due to its nutritional and taste qualities. As is well known, these crops are particularly exposed to soil-borne fungal diseases and need to be effectively protected. At present, wild rocket disease management is performed by using permitted synthetic fungicides or through the application of agro-ecological and biological methods that must be optimized. In this regard, the implementation of innovative digital-based technologies, such as infrared thermography (IT), as supporting systems to decision-making processes is welcome. In this work, leaves belonging to wild rocket plants inoculated with the soil-borne pathogens Rhizoctonia solani Kühn and Sclerotinia sclerotiorum (Lib.) de Bary were analyzed and monitored by both active and passive thermographic methods and compared with visual detection. A comparison between the thermal analysis carried out in both medium (MWIR)- and long (LWIR)-wave infrared was made and discussed. The results achieved highlight how the monitoring based on the use of IT is promising for carrying out an early detection of the rot diseases induced by the investigated pathogens, allowing their detection in 3-6 days before the canopy is completely wilted. Active thermal imaging has the potential to detect early soil-borne rotting diseases.

Trichoderma and its role in biological control of plant fungal and nematode disease

Trichoderma is mainly used to control soil-borne diseases as well as some leaf and panicle diseases of various plants. Trichoderma can not only prevent diseases but also promotes plant growth, improves nutrient utilization efficiency, enhances plant resistance, and improves agrochemical pollution environment. Trichoderma spp. also behaves as a safe, low-cost, effective, eco-friendly biocontrol agent for different crop species. In this study, we introduced the biological control mechanism of Trichoderma in plant fungal and nematode disease, including competition, antibiosis, antagonism, and mycoparasitism, as well as the mechanism of promoting plant growth and inducing plant systemic resistance between Trichoderma and plants, and expounded on the application and control effects of Trichoderma in the control of various plant fungal and nematode diseases. From an applicative point of view, establishing a diversified application technology for Trichoderma is an important development direction for its role in the sustainable development of agriculture.

Presence and future of plant phenotyping approaches in biostimulant research and development

Commercial interest in biostimulants as a tool for sustainable green economics and agriculture concepts is on a steep rise, being followed by increasing demand to employ efficient scientific methods to develop new products and understand their mechanisms of action. Biostimulants represent a highly diverse group of agents derived from various natural sources. Regardless of their nutrition content and composition, they are classified by their ability to improve crop performance through enhanced nutrient use efficiency, abiotic stress tolerance, and quality of crops. Numerous reports have described modern, non-invasive sensor-based phenotyping methods in plant research. This review focuses on applying phenotyping approaches in biostimulant research and development, and maps the evolution of interaction of these two intensively growing domains. How phenotyping served to identify new biostimulants, the description of their biological activity, and the mechanism/mode of action are summarized. Special attention is dedicated to the indoor high-throughput methods using model plants suitable for biostimulant screening and developmental pipelines, and high-precision approaches used to determine biostimulant activity. The need for a complex method of testing biostimulants as multicomponent products through integrating other -omic approaches followed by advanced statistical/mathematical tools is emphasized.

Sorting biotic and abiotic stresses on wild rocket by leaf-image hyperspectral data mining with an artificial intelligence model

BACKGROUND

Wild rocket (Diplotaxis tenuifolia) is prone to soil-borne stresses under intensive cultivation systems devoted to ready-to-eat salad chain, increasing needs for external inputs. Early detection of the abiotic and biotic stresses by using digital reflectance-based probes may allow optimization and enhance performances of the mitigation strategies.

METHODS

Hyperspectral image analysis was applied to D. tenuifolia potted plants subjected, in a greenhouse experiment, to five treatments for one week: a control treatment watered to 100% water holding capacity, two biotic stresses: Fusarium wilting and Rhizoctonia rotting, and two abiotic stresses: water deficit and salinity. Leaf hyperspectral fingerprints were submitted to an artificial intelligence pipeline for training and validating image-based classification models able to work in the stress range. Spectral investigation was corroborated by pertaining physiological parameters.

RESULTS

Water status was mainly affected by water deficit treatment, followed by fungal diseases, while salinity did not change water relations of wild rocket plants compared to control treatment. Biotic stresses triggered discoloration in plants just in a week after application of the treatments, as evidenced by the colour space coordinates and pigment contents values. Some vegetation indices, calculated on the bases of the reflectance data, targeted on plant vitality and chlorophyll content, healthiness, and carotenoid content, agreed with the patterns of variations observed for the physiological parameters. Artificial neural network helped selection of VIS (492-504, 540-568 and 712-720 nm) and NIR (855, 900-908 and 970 nm) bands, whose read reflectance contributed to discriminate stresses by imaging.

CONCLUSIONS

This study provided significative spectral information linked to the assessed stresses, allowing the identification of narrowed spectral regions and single wavelengths due to changes in photosynthetically active pigments and in water status revealing the etiological cause.

Surveying soil-borne disease development on wild rocket salad crop by proximal sensing based on high-resolution hyperspectral features

Wild rocket (Diplotaxis tenuifolia, Brassicaceae) is a baby-leaf vegetable crop of high economic interest, used in ready-to-eat minimally processed salads, with an appreciated taste and nutraceutical features. Disease management is key to achieving the sustainability of the entire production chain in intensive systems, where synthetic fungicides are limited or not permitted. In this context, soil-borne pathologies, much feared by growers, are becoming a real emergency. Digital screening of green beds can be implemented in order to optimize the use of sustainable means. The current study used a high-resolution hyperspectral array (spectroscopy at 350-2500 nm) to attempt to follow the progression of symptoms of Rhizoctonia, Sclerotinia, and Sclerotium disease across four different severity levels. A Random Forest machine learning model reduced dimensions of the training big dataset allowing to compute de novo vegetation indices specifically informative about canopy decay caused by all basal pathogenic attacks. Their transferability was also tested on the canopy dataset, which was useful for assessing the health status of wild rocket plants. Indeed, the progression of symptoms associated with soil-borne pathogens is closely related to the reduction of leaf absorbance of the canopy in certain ranges of visible and shortwave infrared spectral regions sensitive to reduction of chlorophyll and other pigments as well as to modifications of water content and turgor.

Hyperspectral Reflectance Response of Wild Rocket (Diplotaxis tenuifolia) Baby-Leaf to Bio-Based Disease Resistance Inducers Using a Linear Mixed Effect Model

Baby leaf wild rocket cropping systems feeding the high convenience salad chain are prone to a set of disease agents that require management measures compatible with the sustainability-own features of the ready-to-eat food segment. In this light, bio-based disease resistance inducers able to elicit the plant's defense mechanism(s) against a wide-spectrum of pathogens are proposed as safe and effective remedies as alternatives to synthetic fungicides, to be, however, implemented under practical field applications. Hyperspectral-based proximal sensing was applied here to detect plant reflectance response to treatment of wild rocket beds with Trichoderma atroviride strain TA35, laminarin-based Vacciplant^®, and Saccharomyces cerevisiae strain LAS117 cell wall extract-based Romeo^®, compared to a local standard approach including synthetic fungicides (i.e., cyprodinil, fludioxonil, mandipropamid, and metalaxyl-m) and a not-treated control. Variability of the spectral information acquired in VIS-NIR-SWIR regions per treatment was explained by three principal components associated with foliar absorption of water, structural characteristics of the vegetation, and the ecophysiological plant status. Therefore, the following model-based statistical approach returned the interpretation of the inducers' performances at field scale consistent with their putative biological effects. The study stated that compost and laminarin-based treatments were the highest crop impacting ones, resulting in enhanced water intake and in stress-related pigment adjustment, respectively. Whereas plants under the conventional chemical management proved to be in better vigor and health status than the untreated control.

The Often-Overlooked Power of Summary Statistics in Exploratory Data Analysis: Comparison of Pattern Recognition Entropy (PRE) to Other Summary Statistics and Introduction of Divided Spectrum-PRE (DS-PRE)

Unsupervised exploratory data analysis (EDA) is often the first step in understanding complex data sets. While summary statistics are among the most efficient and convenient tools for exploring and describing sets of data, they are often overlooked in EDA. In this paper, we show multiple case studies that compare the performance, including clustering, of a series of summary statistics in EDA. The summary statistics considered here are pattern recognition entropy (PRE), the mean, standard deviation (STD), 1-norm, range, sum of squares (SSQ), and X⁴, which are compared with principal component analysis (PCA), multivariate curve resolution (MCR), and/or cluster analysis. PRE and the other summary statistics are direct methods for analyzing data-they are not factor-based approaches. To quantify the performance of summary statistics, we use the concept of the "critical pair," which is employed in chromatography. The data analyzed here come from different analytical methods. Hyperspectral images, including one of a biological material, are also analyzed. In general, PRE outperforms the other summary statistics, especially in image analysis, although a suite of summary statistics is useful in exploring complex data sets. While PRE results were generally comparable to those from PCA and MCR, PRE is easier to apply. For example, there is no need to determine the number of factors that describe a data set. Finally, we introduce the concept of divided spectrum-PRE (DS-PRE) as a new EDA method. DS-PRE increases the discrimination power of PRE. We also show that DS-PRE can be used to provide the inputs for the k-nearest neighbor (kNN) algorithm. We recommend PRE and DS-PRE as rapid new tools for unsupervised EDA.

Biological Control of Fungal Diseases by Trichoderma aggressivum f. europaeum and Its Compatibility with Fungicides

Our purpose was to evaluate the ability of Trichoderma aggressivum f. europaeum as a biological control agent against diseases from fungal phytopathogens. Twelve isolates of T. aggressivum f. europaeum were obtained from several substrates used for Agaricus bisporus cultivation from farms in Castilla-La Mancha (Spain). Growth rates of the 12 isolates were determined, and their antagonistic activity was analysed in vitro against Botrytis cinerea, Sclerotinia sclerotiorum, Fusarium solani f. cucurbitae, Pythium aphanidermatum, Rhizoctonia solani, and Mycosphaerella melonis, and all isolates had high growth rates. T. aggressivum f. europaeum showed high antagonistic activity for different phytopathogens, greater than 80%, except for P. aphanidermatum at approximately 65%. The most effective isolate, T. aggressivum f. europaeum TAET1, inhibited B. cinerea, S. sclerotiorum, and M. melonis growth by 100% in detached leaves assay and inhibited germination of S. sclerotiorum sclerotia. Disease incidence and severity in plant assays for pathosystems ranged from 22% for F. solani to 80% for M. melonis. This isolate reduced the incidence of Podosphaera xanthii in zucchini leaves by 66.78%. The high compatibility by this isolate with fungicides could allow its use in combination with different pest management strategies. Based on the results, T. aggressivum f. europaeum TAET1 should be considered for studies in commercial greenhouses as a biological control agent.