Horticulture crops (fruit trees) had been grown and cultivated from ancient times in Central Asia. Few researchers have addressed the problem of this profitable sector in the former Great Silk Road, which was at the crossroads of trading avenues. Horticulture has received much attention in the last twenty years. To investigate the current state of research activity of horticulture in Central Asia, we downloaded 4205 English papers from the Scopus database between 2000-2020. We identified a total of 50 papers, and the last four years have witnessed significant growth in publication number, an average of 5 articles per year. Acta Horticulturea was one of the most productive journal. Institute of Plant Biology and Biotechnology (Almaty) and the Academy of Sciences of the Republic of Uzbekistan have worked productively to study high issues. United States Department of Agriculture and Swiss National Science Foundation with Karl Popper Foundation have supported scientific activity in the region. Top 15 highly cited articles were published within the framework of funded projects with international researchers. Researchers of Central Asia focused on walnut, grape and apple, studied on molecular level and cryopreservation of wild relatives for future use. Cherry, apricot, almond and pomegranate crops were less studied by researchers.

This paper summarizes the estimates of the total changes in sales, expenses, and income of participants of the horticulture research and extension programs at the Mississippi State University - Coastal Research and Extension Center for the past five years. Major items outline the estimation procedures for the past five years. The average annual values were used in estimating the total economic impacts of added gross sales, expenses, and incomes of participants in horticulture events. The cumulative total impacts reach $8.7 million in sales, 76 jobs, $1.4 million in labor income, $2.4 million in value-added, and $0.4 million in local, state, and federal taxes. In addition, the total willingness to pay for the horticulture program by the adult participants reached $1.8 million. In comparison, the annual public spending on the horticulture program averaged $1.4 million, creating additional substantial economic impacts to the region.

This review article conducts an in-depth analysis of the role of next-generation technologies in soilless vegetable production, highlighting their groundbreaking potential to revolutionize yield, efficiency, and sustainability. These technologies, such as AI-driven monitoring systems and precision farming methods, offer unparalleled accuracy in monitoring critical variables like nutrient concentrations and pH levels. However, the paper also addresses the multifaceted challenges that hinder the widespread adoption of these technologies. The high initial investment costs pose a significant barrier, particularly for small and medium-scale farmers, thereby risking the creation of a technological divide in the industry. Additionally, the technical complexity of these systems demands specialized expertise, potentially exacerbating knowledge gaps among farmers. Ethical considerations are scrutinized, including data privacy concerns and potential job displacement due to automation. Regulatory challenges, such as international trade regulations and policy frameworks, are discussed as they may need revision to accommodate these new technologies. The paper concludes by emphasizing that while these sustainable technologies offer transformative benefits, their potential for broad adoption is constrained by a complex interplay of financial, technical, ethical, regulatory, and social factors. Comprehensive, multi-faceted solutions are therefore essential for their ethical and equitable implementation.

We present an automated system for nutrient solution management. Prior arts usually measure only pH and EC of the nutrient solutions for maintenance. When EC drops, they just simply add concentrated nutrient to the horticulture bed. Such approach can maintain the density of nutrient solution but cannot maintain the rates of individual ion particles. To prevent nutrition related disorders, fertilization methods with ion selective electrodes are widely introduced. This trend measures individual ion concentration of nutrient solution to maintain appropriate nutrient composition by supplying only insufficient ions. Many researchers have suggested ISE based automated fertilization systems. However, they failed to control a chemical artifact called ion interference effect, which becomes greater at higher density. Our system measures individual concentration of multiple ions and add only deficient nutrients, while handling the ion interference effect issue. To ensure the performance of ion selective electrodes, the system also performs fully automated 3-point calibration 24 times a day. A machine learning algorithm is applied on the sensory parts to remove ion interference effect which make measurement of complex solution with ISE almost impossible. With automated calibration and signal processing technology, the system robustly and continuously maintains nutrient condition for plants. We suggest applying this system on closed hydroponic systems such as smart farms or plant factory, to reduce water consumption and to provide more appropriate environment for the crops.

UAS-based multi-spectral imagery is becoming increasingly popular for the improved monitoring and managing of various horticultural crops. However, for UAS data to be used as an industry standard for assessing tree structure and condition as well as production parameters, it is imperative that the appropriate data collection and pre-processing protocols are established to enable multi-temporal comparison. There are several UAS-based radiometric correction methods commonly used for precision agricultural purposes. However, their relative accuracies have not been assessed for data acquired in complex horticultural environments. This study assessed the variations in estimated surface reflectance values of different radiometric corrections applied to multi-spectral UAS imagery acquired in both avocado and banana orchards. We found that inaccurate calibration panel measurements, inaccurate signal-to-reflectance conversion, and high variation in geometry between illumination, surface, and sensor viewing produced significant radiometric variations in at-surface reflectance estimates. Potential solutions to address these limitations included appropriate panel deployment, site-specific sensor calibration, and appropriate BRDF correction. Future UAS based horticultural crop monitoring can benefit from the proposed solutions to radiometric corrections to ensure they are using comparable image-based maps of multi-temporal biophysical properties.

Agrivoltaics (Agri-PV, AV) – the joint use of land for the production of agricultural products and energy – has recently been rapidly gaining popularity, as it can significantly increase income per unit of land area. In a broad sense, AV systems can include converters of not only solar, but also energy from any other local renewable source, including bioenergy. Current approach to AV represents an evolutionary development of agroecology and integrated PV power supply to the grid. That results in nearly doubled income per unit area. While AV could provide a basis for revolution in large-scale unmanned precision agriculture and smart farming which is impossible without on-site power supply, chemical fertilisation and pesticides reduction, and yield processing on-site. These approaches could change the logistics and the added value production chain in agriculture dramatically, and so, reduce its carbon footprint. Utilisation of decommissioned solar panels in AV could make the technology twice cheaper and postpone the need for bulk PV recycling. Unlike the mainstream discourse on the topic, this review feature is in focusing on the possibilities for AV to be stronger integrated into agriculture that could also help in relevant legal collisions (considered as neither rather than both components) resolution.

Optical sensor data can be used to determine changes in anthocyanins, chlorophyll and soluble solids content (SSC) in apple production. In this study, visible and near-infrared spectra (729 to 975 nm) were transformed to SSC values by advanced multivariate calibration models i.e. partial least square regression (PLSR) in order to test the substitution of destructive chemical analyses through non-destructive optical measurements. Spectral field scans were carried out from 2016 to 2018 on marked ’Braeburn’ apples in Southwest Germany. The study combines an in-depth statistical analyses of longitudinal SSC values with horticultural knowledge to set guidelines for further applied use of SSC predictions in the orchard to gain insights into apple carbohydrate physiology. The PLSR models were investigated with respect to sample size, seasonal variation, laboratory errors and the explanatory power of PLSR models when applied to independent samples. As a result of Monte Carlo simulations, PLSR modelled SSC only depended to a minor extent on the absolute number and accuracy of the wet chemistry laboratory calibration measurements. The comparison between non-destructive SSC determinations in the orchard with standard destructive lab testing at harvest on an independent sample showed mean differences of 0.5 % SSC over all study years. SSC modelling with longitudinal linear mixed-effect (LME) models linked high crop loads to lower SSC values at harvest and higher SSC values for fruit from the top part of a tree.

This paper summarizes opinions on the economic impacts of horticulture research and extension at MSU Coastal Research and Extension Center. More than 8,400 horticulture producers, master gardeners, research and extension staff, and non-profit organizations participated in horticulture events during the last five years. Qualitative assessments of horticulture research and extension performed by the faculty and staff were solicited during voluntary surveys.

Management of Australia’s largest river system, the Murray-Darling Basin (MDB) is controversial, addressing conflicting demands from stakeholders, including irrigators, the agriculture industry, indigenous groups, and the environmental lobby. Oversight is concentrated in a single agency, the MDB Authority, but climate change and the water market have contributed to uncertainty over the Basin’s environmental sustainability. This paper reports on preliminary research in South Australia’s Riverland, one of Australia’s most important wine, citrus, nuts, and stone fruit production areas. It focuses on the Renmark Irrigation Trust (RIT), supplying water to irrigators, primarily horticulturalists. It investigates the chief risks perceived and future-plans in face of concerns over variable water flows and economic uncertainty. Using sequential mixed methods, a questionnaire survey supplied information from a sample of irrigators, covering land use, on-farm environmental actions, risks, decision making, recent and proposed changes to farm management, and attitudes to the MDB Plan and the water market. Focus groups and interviews then enabled key topics to be addressed in greater depth. The RIT’s contribution to river restoration is highlighted, with its plans for additional on-farm water stewardship investigated. There are high levels of uncertainty regarding future viability of smallholdings, including unintended consequences from the water market (e.g., increasing areas under water hungry almonds), the role of water brokers, and environmental viability of the river system linked to climate change. More optimistic voices point to increased diversity of production, resilience of the local horticultural industry, and new entrants in the form of hobby and part-time producers.

Gardening was an important part of the daily duties within several of the religious orders in Europe during the Middle Ages. The rule of Saint Benedict specified that the monastery should, if possible, contain a garden within itself, and before and above all things, special care should be taken of the sick, so that they may be served in very deed, as Christ himself. The cultivation of medicinal and utility plants was important to meet the material needs of the monastic institutions, but no physical garden has yet been found and excavated in either Scandinavia or Iceland. Especially the Cistercians were well known for being pioneer gardeners, but also other orders like the Benedictines and Augustinians practised gardening. The monasteries and nunneries operating in Iceland during medieval times are assumed to have belonged to either the Augustinian or the Benedictine orders. In Norway in addition other orders included the Dominicans, Fransiscans, Premonstratensians and Knights Hospitallers. Based on botanical investigations at all the Icelandic and Norwegian monastery sites, it is concluded that many of the plants found may have a medieval past as medicinal and utility plants, and with all the evidence combined, most probably were cultivated in monastery gardens.

We suggest a deep learning based sensor signal processing method to remove chemical, kinetic and electrical artifacts from ion selective electrodes’ measured values. An ISE is used to investigate the concentration of a specific ion from aqueous solution, by measuring the Nernst potential along the glass membrane. However, application of ISE on a mixture of multiple ion has some problem. First problem is a chemical artifact which is called ion interference effect. Electrically charged particles interact with each other and flows through the glass membrane of different ISEs. Second problem is the kinetic artifact caused by the movement of the liquid. Water molecules collide with the glass membrane causing abnormal peak values of voltage. The last artifact is the interference of ISEs. When multiple ISEs are dipped into same solution, one electrode’s signal emission interference voltage measurement of other electrodes. Therefore, an ISE is recommended to be applied on single-ion solution, without any other sensors applied at the same time. Deep learning approach can remove both 3 artifacts at the same time. The proposed method used 5 layers of artificial neural networks to regress correct signal to remove complex artifacts with one-shot calculation. Its MAPE was less than 1.8% and R² of regression was 0.997. A randomly chosen value of AI-processed data has MAPE less than 5% (p-value 0.016).

Differential equation models to understand interaction between plant and nutrient solution are presented. The root cells selectively emit H+ ions with active transport consuming ATPs to establish electrical gradient along the cell membrane. It establishes electrical field with Nernst potential to make positively charged ions outside the cell membrane flow into the root cell. Anion influx is also modulated by H+ ion concentration because plant root cell absorbs negatively charged particles with symport. If an anion collides with H+ cell to make net charge as neutral, at symport channel, it can flow through. In this paper, mathematical models for cation and anion absorption are introduced. Cation absorption model was induced from Ohm's law combined with Goldman's equation. Anion absorption model is similar to chemical reaction rate model. Both models have physiological terms influenced by gene expression pattern, species or phenotypes. Cation model also includes terms for ion's kinetic and electrical properties, growth of plant and interaction between the root and the surroundings. Simulation for 20 different sets of coefficients showed that the physiology-related coefficient has important role on nutrition absorption tendencies of plants.

Tree condition, pruning and orchard management practices within intensive horticultural tree crop systems can be determined via measurements of tree structure. Multi-spectral imagery acquired from an unmanned aerial system (UAS) has been demonstrated as an accurate and efficient platform for measuring various tree structural attributes, but research in complex horticultural environments has been limited. This research established a methodology for accurately estimating tree crown height, extent, plant projective cover (PPC) and condition of avocado tree crops, from a UAS platform. Individual tree crowns were delineated using object-based image analysis. In comparison to field measured canopy heights, an image-derived canopy height model provided a coefficient of determination (R2) of 0.65 and relative root mean squared error of 6%. Tree crown length perpendicular to the hedgerow was accurately mapped. PPC was measured using spectral and textural image information and produced an R2 value of 0.62 against field data. A random forest classifier was applied to assign tree condition into four categories in accordance with industry standards, producing out-of-bag accuracies >96%. Our results demonstrate the potential of UAS-based mapping for the provision of information to support the horticulture industry and facilitate orchard-based assessment and management.

The use of visual signals in horticulture has attracted significant attention and encompassed a wide range of data types such as 2D images, videos, hyperspectral images, and 3D point clouds. These visual signals have proven to be valuable in developing cutting-edge computer vision systems for various applications in horticulture, enabling plant growth monitoring, pest and disease detection, quality and yield estimation, and automated harvesting. However, unlike other sectors, developing deep learning computer vision systems for horticulture encounters unique challenges due to the limited availability of high-quality training and evaluation datasets necessary for deep learning models. This paper investigates the current status of vision systems and available data in order to identify the high-quality data requirements specific to horticultural applications. We analyse the impact of the quality of visual signals on the information content and features that can be extracted from these signals. To address the identified data quality requirements, we explore the usage of a deep learning-based super-resolution model for generative quality enhancement of visual signals. Furthermore, we discuss how these can be applied to meet the growing requirements around data quality for learning-based vision systems. We also present a detailed analysis of the competitive quality generated by the proposed solution compared to cost-intensive hardware-based alternatives. This work aims to guide the development of efficient computer vision models in horticulture by overcoming existing data challenges and paving a pathway forward for contemporary data acquisition.

Agriculture is the main driver of depletion resources worldwide, and its duty is to ensure food security within a rapidly increasing demographic and urbanization, so it is important to transition to sustainable production systems. Vertical crops (VCs) can reduce the pressure on conventional agriculture because they save water and nutrients and increase crop yield. Therefore, this study aimed to validate a proposed predictive model (PM) to simulate water and nutrient uptake in vertical crops under greenhouse conditions. Based on the Penman-Monteith equation, PM estimates transpiration, while nutrient uptake was estimated using the Carmassi-Sonneveld submodel. PM was experimentally evaluated for vertically grown lettuce under Mediterranean greenhouse conditions, during spring 2023. The irrigation technique was a closed-loop fertigation circuit. The experimental consisted of testing two densities (50 and 80 plants·m-2), where each unit of the experiment unit was divided into three heights (low, medium, and upper). It performed ANOVA with a value of p < 0.05 and R2 to assess PM performance. The results suggest a high degree of PM, since R2 ranged from 0.7 to 0.9 for the uptake of water and nutrients. Both densities had a yield between 17-20 times higher than conventional lettuce production and significant savings in water, between 85-88%. In this sense, PM has great potential to intelligently manage VC fertigation, saving water and nutrients, which represents an advance towards reaching SDG 6 and SDG 12, within the 2030 Agenda.

Organic horticulture is a holistic management system which follows good production practices and should be considered as the cornerstone of mitigating climate change and to produce healthier fruits and vegetables. This agroecosystem practice not only benefits the green economy but promotes and enhances soil biological activity, biodiversity, and other biological cycles in the sphere. The last decade has observed a rise in the production and consumption of organically certified agricultural products due to their higher nutritional value, lowered risk of exposure to harmful chemicals and improved organoleptic properties. The aim of this review was to demonstrate the considerable impact of organic horticulture on mitigating climate change and simultaneously satisfy consumer needs. The outcome of this review demonstrates that there are still numerous research studies required to evaluate how different farming systems and pedoclimatic conditions can contribute to more efficient horticultural practices.

The COVID-19 pandemic has forced businesses to alter the way they operate. This includes, but may not be limited to, changing hours of operation, working with limited staff, and restricting customer access indoors. This could result in several challenges for businesses. In this study, we evaluate the impacts of COVID-19 on the horticultural industry and identify the challenges for businesses). Based on our research findings, the major challenges faced by businesses were not having enough employees and inventory to keep up with consumer demand during COVID-19. We also evaluate the effect of the pandemic on the sales of different plants, gardening products/services, and the overall revenues of businesses. For different types of plants included in the survey, landscape herbaceous flowers, landscape shrubs, and landscape trees showed the most significant increase in sales. In addition, for different gardening products/services, container plants, small plants, and soil & compost showed the most significant increase in sales. 64% of the businesses indicated higher overall sales compared to same season previous year. Over 46% of the businesses reported increased sales in 2020 under COVID-19. These findings imply a greater demand potential for plants and gardening products/services from consumers, in light of this pandemic.

Biodiversity conservation is a key factor in meeting sustainable development goals. This is even more important in cities, where green spaces are becoming increasingly scarce. This study analyzes the Kinshasa's proliferating ornamental plant nurseries, known as informal horticultural sites (IHS). The analysis focused on characterizing the profile of horticulturists, their production conditions and the ornamental species produced. Fifteen IHSs were sampled using the "snowball" technique, and 178 horticulturists were surveyed. Based on the socio-professional profile of the horticulturists, five groups of IHS are distinguished after a hierarchical classification on principal components. We found that IHSs exclusively employed men, most of whom were new to the trade, from all levels of education, and most of whom ranged from 19 to 45 years old. Production conditions are relatively similar from one site to another. However, all IHS are characterized by permanent land insecurity, the use of phytosanitary products, plant conditioning methods that are not very diversified and calibrated to growers' investment capacities, and diversified seed acquisition methods. A total of 139 ornamental species, most of them exotic, were identified. Of these, 37% are phanerophytes and 24% are considered potentially invasive. We suggest ways of professionalizing the activity and protecting the urban environment.

It is well-established that plants are sessile and photoautotrophic organisms that rely on light throughout their entire life cycle. Light quality (spectral composition) is especially important as it provides energy for photosynthesis and influences signaling pathways that regulate plant devel-opment in the complex process of photomorphogenesis. During past years, significant progress has been made in the physiological and biochemical effects of light quality on crops. However, understanding how light quality modulates plant growth and development remains a complex challenge. In this review, we provide an overview of the role of light quality in regulating early development of plants, encompassing processes such as seed germination, seedling de-etiolation, and seedling establishment. These insights can be harnessed to improve production planning and crop quality by producing high-quality seedlings in plant factories and improving the theoretical framework for modern agriculture.

The cultivation of berry crops by the type of "intensive plantings" is an economic and scientific approach in modern horticulture. The principles of forming a red currant assortment adapted to mechanized harvesting are considered. The bush should be compact for mechanized harvesting and have a straight-growing or slightly spreading shape. The morphological structure of the bush is a feature determined by the genotype. The ripening period of berries and the mechanical parameters of berries assess the effectiveness of the berry harvesting process. With the help of agrotechnical techniques, the mechanical parameters of berries cannot be improved and are de-termined by weather, soil, water, and climatic factors. The present study was conducted in the 2021-2022 season using 14 red currant genotypes of different geographical and genetic origins to assess cultivars' suitability for machine harvesting. In most of the studied cultivars, berry quality indicators, the parameters of separation force (Fs) and crushing force (Fc) decreased by the time of biological maturity of the genotype. Several cultivars have shown a non-simultaneous decrease in Fs and Fc. A minor limiting feature determines the duration of harvesting. The high correlation of Fs and Fc (R=0.75-0.85) allows us to predict the most significant period for the high-quality operation of a mechanized harvester. There was no dependence of the strength of the attachment of berries to the peduncle on the thickness of the skin of the berries. Jonkheer Van Tets, Red Lake, Rovada, Rolan, Vika, Asya, and Niva can be attributed to technological cultivars as sources of compact and erect bush habit, Jonkheer Van Tets, Rovada, Rolan, Vika, and Asya are recommended for mechanized harvesting.