Sensing Methodologies in Agriculture for Monitoring Biotic Stress in Plants Due to Pathogens and Pests

Nanofibrous Conductive Sensor for Limonene: One-Step Synthesis via Electrospinning and Molecular Imprinting

Detecting volatile organic compounds (VOCs) emitted from different plant species and their organs can provide valuable information about plant health and environmental factors that affect them. For example, limonene emission can be a biomarker to monitor plant health and detect stress. Traditional methods for VOC detection encounter challenges, prompting the proposal of novel approaches. In this study, we proposed integrating electrospinning, molecular imprinting, and conductive nanofibers to fabricate limonene sensors. In detail, polyvinylpyrrolidone (PVP) and polyacrylic acid (PAA) served here as fiber and cavity formers, respectively, with multiwalled carbon nanotubes (MWCNT) enhancing conductivity. We developed one-step monolithic molecularly imprinted fibers, where S(-)-limonene was the target molecule, using an electrospinning technique. The functional cavities were fixed using the UV curing method, followed by a target molecule washing. This procedure enabled the creation of recognition sites for limonene within the nanofiber matrix, enhancing sensor performance and streamlining manufacturing. Humidity was crucial for sensor working, with optimal conditions at about 50% RH. The sensors rapidly responded to S(-)-limonene, reaching a plateau within 200 s. Enhancing fiber density improved sensor performance, resulting in a lower limit of detection (LOD) of 137 ppb. However, excessive fiber density decreased accessibility to active sites, thus reducing sensitivity. Remarkably, the thinnest mat on the fibrous sensors created provided the highest selectivity to limonene (Selectivity Index: 72%) compared with other VOCs, such as EtOH (used as a solvent in nanofiber development), aromatic compounds (toluene), and two other monoterpenes (α-pinene and linalool) with similar structures. These findings underscored the potential of the proposed integrated approach for selective VOC detection in applications such as precision agriculture and environmental monitoring.

Agriculture and environmental management through nanotechnology: Eco-friendly nanomaterial synthesis for soil-plant systems, food safety, and sustainability

Through the advancement of nanotechnology, agricultural and food systems are undergoing strategic enhancements, offering innovative solutions to complex problems. This scholarly essay thoroughly examines nanotechnological innovations and their implications within these critical industries. Traditional practices are undergoing radical transformation as nanomaterials emerge as novel agents in roles traditionally filled by fertilizers, pesticides, and biosensors. Micronutrient management and preservation techniques are further enhanced, indicating a shift towards more nutrient-dense and longevity-oriented food production. Nanoparticles (NPs), with their unique physicochemical properties, such as an extraordinary surface-to-volume ratio, find applications in healthcare, diagnostics, agriculture, and other fields. However, concerns about their potential overuse and bioaccumulation raise unanswered questions about their health effects. Molecule-to-molecule interactions and physicochemical dynamics create pathways through which nanoparticles cause toxicity. The combination of nanotechnology and environmental sustainability principles leads to the examination of green nanoparticle synthesis. The discourse extends to how nanomaterials penetrate biological systems, their applications, toxicological effects, and dissemination routes. Additionally, this examination delves into the ecological consequences of nanomaterial contamination in natural ecosystems. Employing robust risk assessment methodologies, including the risk allocation framework, is recommended to address potential dangers associated with nanotechnology integration. Establishing standardized, universally accepted guidelines for evaluating nanomaterial toxicity and protocols for nano-waste disposal is urged to ensure responsible stewardship of this transformative technology. In conclusion, the article summarizes global trends, persistent challenges, and emerging regulatory strategies shaping nanotechnology in agriculture and food science. Sustained, in-depth research is crucial to fully benefit from nanotechnology prospects for sustainable agriculture and food systems.

Assessment of Black Rot in Oilseed Rape Grown under Climate Change Conditions Using Biochemical Methods and Computer Vision

Global warming is a challenge for plants and pathogens, involving profound changes in the physiology of both contenders to adapt to the new environmental conditions and to succeed in their interaction. Studies have been conducted on the behavior of oilseed rape plants and two races (1 and 4) of the bacterium Xanthomonas campestris pv. campestris (Xcc) and their interaction to anticipate our response in the possible future climate. Symptoms caused by both races of Xcc were very similar to each other under any climatic condition assayed, although the bacterial count from infected leaves differed for each race. Climate change caused an earlier onset of Xcc symptoms by at least 3 days, linked to oxidative stress and a change in pigment composition. Xcc infection aggravated the leaf senescence already induced by climate change. To identify Xcc-infected plants early under any climatic condition, four classifying algorithms were trained with parameters obtained from the images of green fluorescence, two vegetation indices and thermography recorded on Xcc-symptomless leaves. Classification accuracies were above 0.85 out of 1.0 in all cases, with k-nearest neighbor analysis and support vector machines performing best under the tested climatic conditions.

The rising threat of geminiviruses: molecular insights into the disease mechanism and mitigation strategies

BACKGROUND

Geminiviruses are among the most threatening emerging plant viruses, accountable for a huge loss to agricultural production worldwide. These viruses have been responsible for some serious outbreaks during the last few decades across different parts of the world. Sincere efforts have been made to regulate the disease incidence by incorporating a multi-dimensional approach, and this process has been facilitated greatly by the advent of molecular techniques. But, the mixed infection due to the polyphagous nature of vectors results in viral recombination followed by the emergence of novel viral strains which thus renders the existing mitigation strategies ineffective. Hence, a multifaceted insight into the molecular mechanism of the disease is really needed to understand the regulatory points; much has been done in this direction during the last few years. The present review aims to explore all the latest developments made so far and to organize the information in a comprehensive manner so that some novel hypotheses for controlling the disease may be generated.

METHODS AND RESULTS

Starting with the background information, diverse genera of geminiviruses are listed along with their pathological and economic impacts. A comprehensive and detailed mechanism of infection is elaborated to study the interactions between vector, host, and virus at different stages in the life cycle of geminiviruses. Finally, an effort isalso made to analyze the progress made at the molecular level for the development of various mitigation strategies and suggest more effective and better approaches for controlling the disease.

CONCLUSION

The study has provided a thorough understanding of molecular mechanism of geminivirus infection.

Optical Sensing Technologies to Elucidate the Interplay between Plant and Microbes

Plant-microbe interactions are critical for ecosystem functioning and driving rhizosphere processes. To fully understand the communication pathways between plants and rhizosphere microbes, it is crucial to measure the numerous processes that occur in the plant and the rhizosphere. The present review first provides an overview of how plants interact with their surrounding microbial communities, and in turn, are affected by them. Next, different optical biosensing technologies that elucidate the plant-microbe interactions and provide pathogenic detection are summarized. Currently, most of the biosensors used for detecting plant parameters or microbial communities in soil are centered around genetically encoded optical and electrochemical biosensors that are often not suitable for field applications. Such sensors require substantial effort and cost to develop and have their limitations. With a particular focus on the detection of root exudates and phytohormones under biotic and abiotic stress conditions, novel low-cost and in-situ biosensors must become available to plant scientists.

Novel Vegetation Indices to Identify Broccoli Plants Infected With Xanthomonas campestris pv. campestris

A rapid diagnosis of black rot in brassicas, a devastating disease caused by Xanthomonas campestris pv. campestris (Xcc), would be desirable to avoid significant crop yield losses. The main aim of this work was to develop a method of detection of Xcc infection on broccoli leaves. Such method is based on the use of imaging sensors that capture information about the optical properties of leaves and provide data that can be implemented on machine learning algorithms capable of learning patterns. Based on this knowledge, the algorithms are able to classify plants into categories (healthy and infected). To ensure the robustness of the detection method upon future alterations in climate conditions, the response of broccoli plants to Xcc infection was analyzed under a range of growing environments, taking current climate conditions as reference. Two projections for years 2081-2100 were selected, according to the Assessment Report of Intergovernmental Panel on Climate Change. Thus, the response of broccoli plants to Xcc infection and climate conditions has been monitored using leaf temperature and five conventional vegetation indices (VIs) derived from hyperspectral reflectance. In addition, three novel VIs, named diseased broccoli indices (DBI₁-DBI₃), were defined based on the spectral reflectance signature of broccoli leaves upon Xcc infection. Finally, the nine parameters were implemented on several classifying algorithms. The detection method offering the best performance of classification was a multilayer perceptron-based artificial neural network. This model identified infected plants with accuracies of 88.1, 76.9, and 83.3%, depending on the growing conditions. In this model, the three Vis described in this work proved to be very informative parameters for the disease detection. To our best knowledge, this is the first time that future climate conditions have been taken into account to develop a robust detection model using classifying algorithms.