Feasibility study using remote sensing technologies to improve zonal vineyard management

The primary purpose of this research was to examine the feasibility of using remote sensing data to improve efficiency of zonal vineyard management. To achieve this goal, correlation analysis between the significant vineyard management variables and different remote sensing data analysis tools were undertaken. The variables included leaf water potential, soil moisture, canopy size, vine health, vineyard yield, and fruit composition, which further impacts wine quality. The remote sensing data analysis tools included normalized difference vegetation index (NDVI), and other indices extracted from electromagnetic reflectance data of grapevine leaves and canopies. In each site, sentinel vines (i.e., 72-81) were identified in a grid form. GPS-based geolocation was carried out for six Cabernet Franc vineyards in Ontario's Niagara wine country. Even though remote sensing data analysis tools were not associated with several other important variables for quality grape production, this research still confirmed that remote sensing data analysis has significant potential to differentiate specific zones of canopy size, water stress, yield, some superior fruit compositions, and the resulting wine sensory attributes within a single vineyard site. This study also confirmed that the mechanism of plant defense systems against biotic stress could have impacts on the spectral behaviour of grapevine leaves and hyperspectral remote sensing technologies could be applied as a tool to identify the spectral behaviour changes due to stress. Overall, this study verified the feasibility of remote sensing technologies to enhance the efficiency of vineyard management in the correlation of data from various remote sensing data-analysis techniques and viticulturally important variables for plant health and growth, and fruit and wine quality. As a first step to develop a site-specific crop management (SSCM) model for vineyard management, it also proposes future research opportunities to test and develop an efficient vineyard management decision making model

Recent Innovations in Post-harvest Preservation and Protection of Agricultural Products

The global food supply chain relies on engineered systems, operational practices, and logistics to preserve, protect, process, and deliver agricultural crops along complex supply lines from farmers in low-, middle-, and high-income countries to markets around the world. Food and nutrition security is compromised by post-harvest losses (and food waste) that have been estimated to be as high as 20% in durable and 40% in perishable crops. Preserving crops using technologies and practices such as timely harvesting, evaporative cooling, cold and frozen storage, drying, and dehydrating, and protecting crops using technologies and practices such as damage-less handling, controlled and modified atmosphere storage, non-chemical heat and gas treatment, plant-derived protective films for individual fruits and vegetables, and improved packaging containers are critical to preserving nutrients, improving livelihoods, and realizing an efficient food system. This Special Issue aims to cover recent progress and innovations in science, technology, engineering, operational practices, and logistics related to post-harvest preservation and protection of durable and perishable agricultural crops. It seeks contributions that improve effectiveness, efficiency, reliability and sustainability in post-harvest handling of crops from field to end use that preserve product quality and result in foods and feeds which are nutritious and safe for human and animal consumption

Anthocyanins

This book contains 20 articles published in Molecules that concern the color quality of food and wine, anthocyanin biosynthesis and regulation, anthocyanin composition and the biological properties of anthocyanin pigments

Remote Sensing for Precision Nitrogen Management

This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment

Study of the Influence of Abiotic and Biotic Stress Factors on Horticultural Plants

We would like to provide the scientists a set of studies entitled "Study of the Influence of Abiotic and Biotic Stress Factors on Horticultural Plants". The reprint book contains 12 papers about the influence of the stress factors on the plant growth and soil parameters. Authors descripted the impact of the biotic and abiotic stress factors (i.e., high, and low temperature, salt, inorganic pollutants such as salts, heavy metals, phosphite, as well as irrigation) on the physiological, biochemical, and anatomical changes occurring in the plants at the cellular, tissue, organ, and whole plant level. The subject of these studies were different plant species, i.e., watermelon, lettuce, kale, potato, grapevine, hops, orchid, strawberry, and boxwood. The ideas of the papers can be divided into five topics: (1) achieving better quality of plant material for food production by changes made in the growth conditions, metabolic and genetic modifications; (2) increasing the plant resistance to environmental stresses by application of exogenous compounds of different chemical character; (3) reducing plant stress caused by anthropogenic activity applying nonmodified and genetically modified plants; (4) mitigating drought stress by irrigation; and 5) the positive effect of plant growth-promoting microorganisms on horticulture plants performance during drought stress

Postharvest Management of Fruits and Vegetables

All articles in the presented collection are high-quality examples of both basic and applied research. The publications collectively refer to apples, bananas, cherries, kiwi fruit, mango, grapes, green bean pods, pomegranates, sweet pepper, sweet potato tubers and tomato and are aimed at improving the postharvest quality and storage extension of fresh produce. The experimental works include the following postharvest treatments: 1-methylcycloprpene, methyl jasmonate, immersion in edible coatings (aloe, chitosan, plant extracts, nanoemulsions, ethanol, ascorbic acid and essential oils solutions), heat treatments, packaging, innovative packaging materials, low temperature, low O2 and high CO2 modified atmosphere, and non-destructible technique development to measure soluble solids with infra- and near infra-red spectroscopy. Preharvest treatments were also included, such as chitosan application, fruit kept on the vine, and cultivation under far-red light. Quality assessment was dependent on species, treatment and storage conditions in each case and included evaluation of color, bruising, water loss, organoleptic estimation and texture changes in addition to changes in the concentrations of sugars, organic acids, amino acids, fatty acids, carotenoids, tocopherols, phytosterols, phenolic compounds and aroma volatiles. Gene transcription related to ethylene biosynthesis, modification of cell wall components, synthesis of aroma compounds and lipid metabolism were also the focus of some of the articles

An examination of postharvest techniques to enable seafreight export of feijoa (Acca sellowiana [O.Berg.] Burret) : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Food Technology at Massey University, Manawatu Campus, Palmerston North, New Zealand

Export of feijoa (Acca sellowiana [O.Berg.] Burret) to the main markets in Europe, Asia and North America is currently by airfreight that is not only expensive but rather unsustainable as the industry expands. With the ongoing breeding works and expansions of plantings, growers will eventually have to seek for an economic mode of transport. As is the case with kiwifruit, apples, avocadoes, and squash, seafreight will provide an alternative option that is both cheaper and accommodates large fruit volumes. The short storage life of feijoa, however, is likely to pose a challenge to seafreight (that requires at least 6 weeks of storage) if appropriate postharvest techniques are not identified to extend storage life. Feijoa stores for about 4 weeks at 4 °C after which it becomes overripe, loses flavour, and develops chilling injury and internal browning. This study was undertaken to examine potential postharvest techniques that could extend storage life and maintain quality of feijoa. The postharvest techniques investigated were temperature and relative humidity management, harvest timing, step down conditioning, intermittent warming, chlorophyll fluorescence and development of non-destructive grading tools. The varieties used in this study were ‘Kakariki’, ‘Wiki Tu’ and ‘Triumph’ that were stored for 8 weeks under various conditions. To assess the effects of temperature and relative humidity in storage, ‘Kakariki’, ‘Wiki Tu’ and ‘Triumph’ were stored at 1 °C (85% RH) and 4 °C (88% RH). These conditions were set to result in equal water vapour pressure deficits at both temperatures. The effects of RH on feijoa quality during 8 weeks storage were tested by using a polyethylene liner (polyliner) to cover the fruit in each tray, for half of the treatments. Despite good retention of some attributes indicating quality (firmness and skin colour) for up to 8 weeks at 1 °C, many fruit developed chilling injury making it unsaleable and therefore causing huge losses. At both 4 °C and 1 °C the use of a polyliner resulted in reduced water loss, suggesting polyliners may be beneficial for feijoa storage. Given the chilling injury results, it is imperative to consider treatments that may reduce chilling injury and yet maintain fruit quality. To alleviate chilling injury and extend storage life of ‘Kakariki’, 2 harvesting times (early (H1) and commercial (H2)), 2 storage temperatures (2 °C and 4 °C) and three conditioning treatments (single step down, [6 d at 9 °C then moved to 2 °C or 4 °C ], double step down [3 d at 9 °C , 3 d at 6 °C then moved to 2 °C or 4 °C] and ‘no conditioning’ control [stored direct to 2 °C or 4 °C]) were established. Results showed that early harvested fruit had lower chilling injury incidence and retained more quality attributes thereby providing a possibility of extending Kakariki’ feijoa storage life. There was no evidence for a difference in quality arising from storage at 2 °C or 4 °C but it was evident that single or double step down conditioning simply allowed an extended period of postharvest ripening because of the 6 d delay in reaching the more appropriate storage temperature of 2 °C or 4 °C. This led to faster deterioration of fruit. Therefore, it is advisable to rapidly cool feijoa soon after harvest to reduce metabolism and ripening; but then sell the fruit before they develop CI. To assess the effects of intermittent warming (IW) on improving quality of ‘Triumph’ fruit. Three (3) intermittent warming conditions were tested (IW from 4 °C to 20 °C for 1 d after every 6 d storage, IW from 4 °C to 20 °C for 1 d after every 10 d storage and control) and stored at 4 °C for 6 weeks. Chlorophyll fluorescence was used as a non-destructive tool to assess quality. The results showed that intermittent warming just like conditioning treatments, accelerated ripening leading to faster deterioration. A decline in quantum yield (Fv/Fm) was observed during storage in the absence of CI. This suggests that it is linked to loss of chlorophyll content and chloroplast membrane injury associated with photosystem II (PSII) as feijoa ripened. The continuous decline in quantum yield (Fv/Fm) offers potential for a non-destructive technique to assess feijoa ripeness and could therefore be used in a cool store to detect batches of fruit that are ripening more quickly for immediate sales or those ripening slowly that may be more suited to export or long storage. To re-evaluate the internal maturity/ripeness scale developed by scanned images of ‘Kakariki’, ‘Wiki Tu’ and ‘Triumph’ varieties from at harvest through storage were assessed against the PFR scale. The results showed that the PFR scale worked well for maturity assessment of ‘Kakariki’, ‘Wiki Tu’ and ‘Triumph’ varieties at harvest, despite their quite different internal anatomy. The same scale was also appropriate for each variety as a post-storage ripeness indicator. Evidence also suggested that one new step was required an internal maturity rating of 1.5. The problem was that fruit at stage 1 could be immature (and not ripen during storage) or mature. This new stage was used to describe fruit showing the first signs of locular gel clearing, suggesting that ripening was definitely underway. Firmness (non-destructively assessed) at harvest was correlated with quality after storage and therefore showed potential to predict fruit ripening behaviour in storage. This implies, that firmness could be used non-destructively in sorting lines to select firm fruit for long storage or soft fruit for immediate consumption. Based on these findings’, storage life of ‘Kakariki’, ‘Wiki Tu’ and ‘Triumph’ feijoa could not be reliably extended beyond 4 to 6 weeks and therefore seafreight export is still risky. The wide range of maturity variation within any batch of harvested feijoas accounts for much of this risk. Future research should focus on finding a rapid, non-destructive technique that can detect the new internal maturity/ripeness rating 1.5. This would assist growers to grade early harvested fruit and select mature but longer-storing fruit for export

Nutritive Value, Polyphenolic Content, and Bioactive Constitution of Green, Red and Flowering Plants

Plants, including vegetables, are an essential element of the human diet, considering their dense nutritional content and bioactive content that could assist in boosting nutritional quality and food security. Plants are exhibiting a colossal rebound in the context of healthier lifestyles, especially as functional foods empowered with bioactive phytochemicals; they synthesize uncountable “ecochemicals” via secondary metabolism, which command medical and socioeconomic significance. Among these secondary metabolites, phenolic compounds are of prime interest and are largely present in medicinal plants, herbs, vegetables, and flowers. These metabolites are at the helm of the bitterness, color, and scent of plants, and are correlated to the beneficial health qualities expressed by the antioxidant capacity. The accretion of these health-promoting phytochemicals depends chiefly on the genetic material and the maturity stage at harvest, notwithstanding the main role that is played by preharvest factors, i.e., eustress, fertilization, irrigation, light, biostimulants, biofortification, and other agronomic practices. This Special Issue is a collection of 11 original research articles addressing the quality of seeds, microgreens, leafy vegetables, herbs, flowers, berries, fruits, and byproducts. Mainly preharvest factors were assessed regarding their effect on the qualitative aspects of the aforementioned plants

Antioxidant Capacity of Anthocyanins and other Vegetal Pigments: Modern Assisted Extraction Methods and Analysis

This reprint contains thirteen contributions on recent advances in the field of anthocyanins and other vegetal pigments and state-of-the-art extraction methods applied to different matrices. The interdisciplinary character of the subject and the breadth of the contents presented by the authors make this book very interesting and comprehensive. This reprint covers different topics such as the most modern and cutting-edge methods for the analysis and extraction of anthocyanins, their geographical variability, the improvement and protection of their antioxidant properties, the valorization of by-products, stability studies and the metabolomics of chlorophylls and carotenoids, all of which are the subject of research and review in this reprint

Development and Applications of Machine Learning Methods for Hyperspectral Data

Die hyperspektrale Fernerkundung der Erde stützt sich auf Daten passiver optischer Sensoren, die auf Plattformen wie Satelliten und unbemannten Luftfahrzeugen montiert sind. Hyperspektrale Daten umfassen Informationen zur Identifizierung von Materialien und zur Überwachung von Umweltvariablen wie Bodentextur, Bodenfeuchte, Chlorophyll a und Landbedeckung. Methoden zur Datenanalyse sind erforderlich, um Informationen aus hyperspektralen Daten zu erhalten. Ein leistungsstarkes Werkzeug bei der Analyse von Hyperspektraldaten ist das Maschinelle Lernen, eine Untergruppe von Künstlicher Intelligenz. Maschinelle Lernverfahren können nichtlineare Korrelationen lösen und sind bei steigenden Datenmengen skalierbar. Jeder Datensatz und jedes maschinelle Lernverfahren bringt neue Herausforderungen mit sich, die innovative Lösungen erfordern. Das Ziel dieser Arbeit ist die Entwicklung und Anwendung von maschinellen Lernverfahren auf hyperspektrale Fernerkundungsdaten. Im Rahmen dieser Arbeit werden Studien vorgestellt, die sich mit drei wesentlichen Herausforderungen befassen: (I) Datensätze, welche nur wenige Datenpunkte mit dazugehörigen Ausgabedaten enthalten, (II) das begrenzte Potential von nicht-tiefen maschinellen Lernverfahren auf hyperspektralen Daten und (III) Unterschiede zwischen den Verteilungen der Trainings- und Testdatensätzen. Die Studien zur Herausforderung (I) führen zur Entwicklung und Veröffentlichung eines Frameworks von Selbstorganisierten Karten (SOMs) für unüberwachtes, überwachtes und teilüberwachtes Lernen. Die SOM wird auf einen hyperspektralen Datensatz in der (teil-)überwachten Regression der Bodenfeuchte angewendet und übertrifft ein Standardverfahren des maschinellen Lernens. Das SOM-Framework zeigt eine angemessene Leistung in der (teil-)überwachten Klassifikation der Landbedeckung. Es bietet zusätzliche Visualisierungsmöglichkeiten, um das Verständnis des zugrunde liegenden Datensatzes zu verbessern. In den Studien, die sich mit Herausforderung (II) befassen, werden drei innovative eindimensionale Convolutional Neural Network (CNN) Architekturen entwickelt. Die CNNs werden für eine Bodentexturklassifikation auf einen frei verfügbaren hyperspektralen Datensatz angewendet. Ihre Leistung wird mit zwei bestehenden CNN-Ansätzen und einem Random Forest verglichen. Die beiden wichtigsten Erkenntnisse lassen sich wie folgt zusammenfassen: Erstens zeigen die CNN-Ansätze eine deutlich bessere Leistung als der angewandte nicht-tiefe Random Forest-Ansatz. Zweitens verbessert das Hinzufügen von Informationen über hyperspektrale Bandnummern zur Eingabeschicht eines CNNs die Leistung im Bezug auf die einzelnen Klassen. Die Studien über die Herausforderung (III) basieren auf einem Datensatz, der auf fünf verschiedenen Messgebieten in Peru im Jahr 2019 erfasst wurde. Die Unterschiede zwischen den Messgebieten werden mit qualitativen Methoden und mit unüberwachten maschinellen Lernverfahren, wie zum Beispiel Principal Component Analysis und Autoencoder, analysiert. Basierend auf den Ergebnissen wird eine überwachte Regression der Bodenfeuchte bei verschiedenen Kombinationen von Messgebieten durchgeführt. Zusätzlich wird der Datensatz mit Monte-Carlo-Methoden ergänzt, um die Auswirkungen der Verschiebung der Verteilungen des Datensatzes auf die Regression zu untersuchen. Der angewandte SOM-Regressor ist relativ robust gegenüber dem Rauschen des Bodenfeuchtesensors und zeigt eine gute Leistung bei kleinen Datensätzen, während der angewandte Random Forest auf dem gesamten Datensatz am besten funktioniert. Die Verschiebung der Verteilungen macht diese Regressionsaufgabe schwierig; einige Kombinationen von Messgebieten bilden einen deutlich sinnvolleren Trainingsdatensatz als andere. Insgesamt zeigen die vorgestellten Studien, die sich mit den drei größten Herausforderungen befassen, vielversprechende Ergebnisse. Die Arbeit gibt schließlich Hinweise darauf, wie die entwickelten maschinellen Lernverfahren in der zukünftigen Forschung weiter verbessert werden können