Drones: Innovative Technology for Use in Precision Pest Management.

Arthropod pest outbreaks are unpredictable and not uniformly distributed within fields. Early outbreak detection and treatment application are inherent to effective pest management, allowing management decisions to be implemented before pests are well-established and crop losses accrue. Pest monitoring is time-consuming and may be hampered by lack of reliable or cost-effective sampling techniques. Thus, we argue that an important research challenge associated with enhanced sustainability of pest management in modern agriculture is developing and promoting improved crop monitoring procedures. Biotic stress, such as herbivory by arthropod pests, elicits physiological defense responses in plants, leading to changes in leaf reflectance. Advanced imaging technologies can detect such changes, and can, therefore, be used as noninvasive crop monitoring methods. Furthermore, novel methods of treatment precision application are required. Both sensing and actuation technologies can be mounted on equipment moving through fields (e.g., irrigation equipment), on (un)manned driving vehicles, and on small drones. In this review, we focus specifically on use of small unmanned aerial robots, or small drones, in agricultural systems. Acquired and processed canopy reflectance data obtained with sensing drones could potentially be transmitted as a digital map to guide a second type of drone, actuation drones, to deliver solutions to the identified pest hotspots, such as precision releases of natural enemies and/or precision-sprays of pesticides. We emphasize how sustainable pest management in 21st-century agriculture will depend heavily on novel technologies, and how this trend will lead to a growing need for multi-disciplinary research collaborations between agronomists, ecologists, software programmers, and engineers

Making sense of light: the use of optical spectroscopy techniques in plant sciences and agriculture

As a result of the development of non-invasive optical spectroscopy, the number of prospective technologies of plant monitoring is growing. Being implemented in devices with different functions and hardware, these technologies are increasingly using the most advanced data processing algorithms, including machine learning and more available computing power each time. Optical spectroscopy is widely used to evaluate plant tissues, diagnose crops, and study the response of plants to biotic and abiotic stress. Spectral methods can also assist in remote and non-invasive assessment of the physiology of photosynthetic biofilms and the impact of plant species on biodiversity and ecosystem stability. The emergence of high-throughput technologies for plant phenotyping and the accompanying need for methods for rapid and non-contact assessment of plant productivity has generated renewed interest in the application of optical spectroscopy in fundamental plant sciences and agriculture. In this perspective paper, starting with a brief overview of the scientific and technological backgrounds of optical spectroscopy and current mainstream techniques and applications, we foresee the future development of this family of optical spectroscopic methodologies.info:eu-repo/semantics/publishedVersio

Evaluation of parameters influencing plant response to carbon nanotube contamination

Carbon nanotubes (CNTs) are among the most used nanomaterials (NMs) thanks to their excellent physicochemical properties. All along their lifecycle, they may be spread unintentionallyor intentionally in the environment. It is thus essential to assess their behavior and potential impacts on ecosystems and particularly on crop plants. Overall, behaviour and effects of CNTs in plants are not well understood and still very controversial. In this work, we aimed to assess the influence of several parameters on plant response after exposure in a CNT-contaminated soil. We first focused on the analytical challenge of detecting CNT in biological matrices and tested several spectroscopic techniques. Then, we evaluated the response of tomato plants to two different NMs (CNTs and TiO2-NPs). Our results highlight that despite being different for several parameters (i.e. shape, size, surface chemistry), CNT exposure led to a similar response in tomato plants, in particular on the alteration of plant cell wall components. The study of different plant species (tomato, canola, maize and cucumber) exposed to DWCNT contamination highlighted different responses according to plant species, maize (monocot) being the most sensitive. Different types of CNTs are currently available. Five types of CNTs varying in diameter, functionalization and length were used to investigate their impact on canola. Canola was more sensitive to CNTs with the smallest diameters, but it was also observed that the functionalization greatly modulated the plant response. Finally, we tested the impact of a combined stress: canola plants grown in optimal growth conditions were not impacted by CNT exposure at the tested dose, while we observed that plants were more sensitive to CNTs when submitted to a concomitant heat stress

Multi-sensor and data fusion approach for determining yield limiting factors and for in-situ measurement of yellow rust and fusarium head blight in cereals

The world’s population is increasing and along with it, the demand for food. A novel parametric model (Volterra Non-linear Regressive with eXogenous inputs (VNRX)) is introduced for quantifying influences of individual and multiple soil properties on crop yield and normalised difference vegetation Index. The performance was compared to a random forest method over two consecutive years, with the best results of 55.6% and 52%, respectively. The VNRX was then implemented using high sampling resolution soil data collected with an on-line visible and near infrared (vis-NIR) spectroscopy sensor predicting yield variation of 23.21%. A hyperspectral imager coupled with partial least squares regression was successfully applied in the detection of fusarium head blight and yellow rust infection in winter wheat and barley canopies, under laboratory and on-line measurement conditions. Maps of the two diseases were developed for four fields. Spectral indices of the standard deviation between 500 to 650 nm, and the squared difference between 650 and 700 nm, were found to be useful in differentiating between the two diseases, in the two crops, under variable water stress. The optimisation of the hyperspectral imager for field measurement was based on signal-to-noise ratio, and considered; camera angle and distance, integration time, and light source angle and distance from the crop canopy. The study summarises in the proposal of a new method of disease management through suggested selective harvest and fungicide applications, for winter wheat and barley which theoretically reduced fungicide rate by an average of 24% and offers a combined saving of the two methods of £83 per hectare

Behavior of Copper Oxide Nanoparticles in Soil Pore Waters as Influenced by Soil Characteristics, Bacteria, and Wheat Roots

The goal of this project was to study the behavior of copper oxide nanoparticles in soil environments. Copper oxide nanoparticles have antimicrobial properties and may also be used in agricultural settings to provide a source of copper for plant health, but accidental or misapplication of these nanoparticles to soil may be damaging to the plant and its associated bacteria. Dissolved soil organic matter that is present in soil pore waters dissolved nanoparticles, but did not dissolve the expected amounts from a geochemical model because the geochemical model did not take into account surface chemistry or coating of the nanoparticles by dissolved organic matter. Wheat grown in soil pore water increased the solubility of the nanoparticles. The nanoparticles and dissolved copper were harmful to wheat, but dissolved soil organic matter remediated a portion of the damage. These studies were conducted with Utah soils and wheat, a highly valuable Utah crop. These results suggest that contamination of soils by copper oxide nanoparticles will be partially mitigated by the organic matter content of the soil. Producers of fertilizers and fungicides may use various forms of organic matter to deliver products that are targeted to specific plants or pathogens and avoid damage to non-target organisms

Developing Multimodal Spectroscopic Imaging Techniques to Study Metal Dyshomeostasis and Altered Brain Biochemistry During Ageing

Cognitive decline and neurodegenerative diseases such as Alzheimer’s disease, pose significant concerns for the ageing population. Increased understanding of the biochemical processes that occur during ageing, may help to reveal pathways for prevention or treatment. There has been interest in the potential role that metal dis-homeostasis plays during ageing and cognitive decline, so this thesis has focussed on developing and applying spectroscopic methods to study metal dis-homeostasis and concomitant biochemical changes during ageing

Geochemical and spectroscopic fingerprinting for authentication and geographical traceability of high-quality lemon fruits.

Geochemical (mineral element and Sr isotope ratio) and spectroscopical fingerprinting (Near Infrared Spectroscopy) were proposed to authenticate and track the two high-quality lemon fruits from the Campania region (Limone di Sorrento PGI and Limone Costa d'Amalfi PGI) to protect them from frauds. Considering the geochemical indicators, we built different chemometric discriminant models based on mineral profile and 87Sr/86Sr isotope ratio. These two techniques were applied to discriminate fruits from different territorial scales, small territorial scales (region scale), and large territorial scales. The results of different discriminant models applied on mineral profiles of lemon juices, both on a small and large territorially scale, showed good discrimination according to provenance, especially for non-essential elements as Rb, Ba, Sr, Ti, and Co. These same elements have shown a good correlation with cultivation soils and stability between the two production years. It is worth noting that although, the performance of the whole elemental profile gave a better result than the profile of the non-essential elements, the reliability of the two models, calculated as the ratio between the percentage of correctly validated and classification samples, was similar. In addition, the Sr isotope ratio had shown a clear differentiation among the fruits from the Campania region and extra-regional samples, and by analysis of 86Sr/87Sr of soils, it was clear that the strontium isotope ratio of lemon juices was closely related to that of the bioavailable fractions of the soil. Furthermore, combining both isotopic and mineral profiles in lemon juices by a low-level data fusion approach, the results showed a better clustering according to geographical origins than the two-determination taken separately, although on an explorative level. In addition, the spectroscopical data (NIR) on intact lemon fruits showed the strong influence of environmental growing conditions on the samples. For this, the application of Linear Discriminant Analysis (LDA) models suggested building the discrimination models according to origins (PGI and not PGI productions) based on one production year. In the same way, the application of MLR models, that showed a strong relationship between quality properties of lemon fruits and NIR spectra, suggested the applicability of this technique to build predictive models for the quality properties. In addition, on a part of the total samples collected only in 2019 (intact lemons and juices), have been successfully applied two different chemometrics models i.e., LDA and Partial Least Square Discriminant Analysis (PLS-DA). The results showed better provenance discrimination using the lemon juices than the intact lemons. Comparing the results obtained, of the two approaches used, the results of geochemical fingerprinting have shown more stability for discriminate lemon fruits derived from two different production years, especially for not essential elements. However, considering the various vantages of the application of NIR spectroscopy (non-destructive, rapid, and cheap) and the results obtained, this technique can be used for rapid screening of samples in order to verify the quality and origins of lemon fruits during the year. The study of the pedoclimatic features was fundamental to understand the nature of discriminating variables, in both approaches. Additional research should be conducted to include a greater number of lemon farms (or sampling points) in the PGI area and to enlarge the existing database including lemon samples from other regions and validate the models built. These discriminant models based on geochemical and spectroscopical profiles of lemon fruits could substantially contribute to implementing a blockchain system for Campanian lemon traceability, providing real-time information not only to the final consumers but also to manufacturers, distributors, and retailers