Fruit Category Research Articles

Impact of fruit reserve conditions from harvest to processing on the chemical composition and quality of coffee

Changes in the different stages of fruit processing are becoming a differentiating factor in coffee quality. Currently, generating delays in the processing of the fruit, that is, reserve, is a practice that can modify the sensory profile of the beverage. In this study, the fruit was reserved in three stages of maturity, two temperatures (15° and 20°C) and at two timepoints (24 and 48 h), establishing its chemical composition and sensory characteristics using analytical techniques such as liquid and gas chromatography. The state of maturity 1 showed an effect on malic and quinic acids due to the interaction of temperature and reserve time, while stage of maturity 3 showed an effect on glucose and fructose. The state of maturity 2 showed an effect of the reserve time on the same compounds of states 1 and 3, in addition to the content of defective beans and sucrose. The sensory quality expressed as the total SCA (Specialty Coffee Association) score and the attributes did not show treatments effect, the reserve of the fruit under the evaluated conditions presents an effect on the descriptors of the flavor. Extending the fruit reserve time from 24 to 48 h showed an increase in the groups of flavor descriptors associated with fruit and red fruits from 33.33 to 66.66% and from 34.04 to 65.95%. This implies that the constituent parts of the fruit such as the exocarp and mesocarp interact with the bean, leading to changes in the chemical composition and the improvement of the qualitative complexity of the sensory quality. This is a consequence of the increase in the frequency of the flavor descriptors of the fruit and red fruit category.

Fruit Variation in Yellow-Fleshed Actinidia (Actinidia chinensis Planch) Plants Grown in Southern Italy as a Function of Shoot Type

One of the goals of modern orcharding is to produce a high volume of fruits with uniform size, organoleptic parameters, and health characteristics. The aim of this work was to study various shoot types and their prevailing positions along the cane and to identify how shoot type can influence the quality of fruit from the Actinidia tree. The experiment was conducted over a two-year period in a commercial orchard of Actinidia chinensis, cv. Gold 3. The shoots along the cane were classified as follows: spur shoots (SPs), terminated shoots (TEs), non-terminated shoots (NTs), and cut non-terminated shoots (CNTs). The data were statistically processed using ANOVA and Principal Component Analysis (ACP). Four different categories of fruit were obtained from the four shoot types, and their various attributes were compared. The prevailing category (comprising 55% of the studied fruits) was TEs, which are characterised by a higher soluble solid content, sweetness, and excellent health characteristics, as well as the reduced hardness of their pulp, which would support the hypothesis that harvesting could be brought forward. The second most common category (comprising 19% of total fruit) with the lowest soluble solid content, but a high antioxidant capacity, was that which was detached from the CNTs, while 13% of the fruit was produced from NTs, which had the lowest health value but good sweetness perception. Finally, the category with the lowest fruit percentage over the total fruit harvested (10%) was SPs, which are characterised by their smaller size. It has yet to be determined what the performance of each category will be post-harvest; whether it is possible to assign the quality categories while harvesting the fruit or to differentiate the harvest time accordingly remains subject to debate.

Implementation of Convolutional Neural Network Algorithms to Detect the Ripeness of Palm Fruits based on Image Colors

The ripeness of oil palm fruit greatly affects the quality of the oil produced. Usually, humans manually detect the maturity of oil palm fruit, a process that can be inconsistent and time-consuming. Therefore, there is a need for automated methods that can improve accuracy and efficiency in determining the maturity of oil palm fruit. The study proposes using the Convolutional Neural Network algorithm to detect oil palm fruit maturity based on digital color images. We expect a convolutional neural network, known for its effectiveness in image analysis, to deliver accurate results in classifying fruit maturity. The aim of this study is to develop and test a model of a convolutional neural network that can classify oil palm fruit into three maturity categories: raw, ripe, and rotten. This study employs a maturity-categorized data set of oil palm images. We built and trained a convolutional neural network model using this data set. We evaluate the model performance using four main metrics: accuracy, precision, recall, and f1-score. We perform an in-depth analysis to assess model performance in each maturity category. The confusion matrix yielded an accuracy of 74.6%. The convolutional neural network model developed showed the highest precision results for the ripe fruit (86.00%), followed by rotten fruit (72.30%), and raw fruit (67.30%). We obtained the highest recalls (85.40%) for the raw fruit category, followed by ripe fruits (75.50%) and rotten fruits (72.30%.

Do promotions of healthier or more sustainable foods increase sales? Findings from three natural experiments in UK supermarkets

BackgroundDietary changes are necessary to improve population health and meet environmental sustainability targets. Here we analyse the impact of promotional activities implemented in UK supermarkets on purchases of healthier and more sustainable foods.MethodsThree natural experiments examined the impact of promotional activities on sales of a) no-added-sugar (NAS) plant-based milk (in 199 stores), b) products promoted during ‘Veganuary’ (in 96 stores), and c) seasonal fruit (in 100 non-randomised intervention and 100 matched control stores). Data were provided on store-level product sales, in units sold and monetary value (£), aggregated weekly. Predominant socioeconomic position (SEP) of the store population was provided by the retailer. Analyses used interrupted time series and multivariable hierarchical mixed-effects models.ResultsSales of both promoted and total NAS plant-based milks increased significantly during the promotional period (Promoted:+126 units, 95%CI: 105–148; Overall:+307 units, 95%CI: 264–349). The increase was greater in stores with predominately low SEP shoppers. During Veganuary, sales increased significantly for plant-based foods on promotion (+60 units, 95%CI: 37–84), but not for sales of plant-based foods overall (dairy alternatives: -1131 units, 95%CI: -5821–3559; meat alternatives: 1403 units, 95%CI: -749–3554). There was no evidence of a change in weekly sales of promoted seasonal fruit products (assessed via ratio change in units sold: 0.01, 95%CI: 0.00–0.02), and overall fruit category sales slightly decreased in intervention stores relative to control (ratio change in units sold: -0.01, 95%CI: -0.01–0.00).ConclusionDuring promotional campaigns there was evidence that sales of plant-based products increased, but not seasonal fruits. There was no evidence for any sustained change beyond the intervention period.

Assessing Frontal Lobe Function on Verbal Fluency and Emotion Recall in Autism Spectrum Disorder by fNIRS.

This study applied the functional near-infrared spectroscopy (fNIRS) to investigate frontal activity in autism when performing verbal fluency test and emotion recall task. We recruited 32 autistic adults without intellectual disability and 30 typically-developing controls (TDC). Prefrontal hemodynamic changes were evaluated by fNIRS when the participants performed the verbal fluency test and emotion recall task. fNIRS signals in the prefrontal cortex were compared between autism and TDC. Compared to TDC, autistic adults showed comparable performance on the verbal fluency test but exhibited lower frontal activity on the vegetable category. In the verbal fluency test, left frontal activity in TDC significantly increased in the vegetable category (vs. fruit category). In the emotion recall task, left frontal activity increased significantly in TDC when recalling emotional (vs. neutral) events. This increase of left frontal activity on the more difficult works was not found in autism. Similarly, brain activities were related to test performance only in TDC but not in autism. In addition, more severe social deficits were associated with lower frontal activity when recalling emotional events, independent of autism diagnosis. Findings suggested reduced frontal activity in autism, as compared to TDC, when performing verbal fluency tests. The reduction of left frontal activation in verbal fluency test and emotion recall tasks might reflect on the social deficits of the individual. The fNIRS may potentially be applied in assessing frontal lobe function in autism and social deficits in general population. Trial registration number: NCT04010409.

Fruit Quality Analysis and Disease Detection using Deep Learning Techniques

Defective fruits are the main reason for worldwide financial catastrophes in agricultural production. It affects both the dependability and quality of the fruits. Post-harvest, quality checking requires a significant amount of time and labor-intensive skill. Automatically identifying fruit quality enables saving time and labor during harvest. Various algorithms have been created using machine learning and image processing methods to detect and categorize fruit quality. A system using Convolutional Neural Networks (CNN) and transfer learning techniques has been developed to enhance the fruit classification process. Two methods are suggested for predicting fruit freshness. One tailored CNN architecture is proposed by modifying the network's parameters to suit the dataset. The second technique utilizes the pre-trained VGG model with transfer learning to assess the freshness of the fruit. The proposed models are capable of differentiating between fresh and spoiled fruit by analyzing the input photos. This research used 70 diverse types of fruit, such as apples, bananas, oranges, and others. The first CNN obtains 98.55% accuracy for heterogeneous fruit dataset while second VGG16 achieves 99.05% accuracy on similar dataset. The plant village global dataset is utilized with various fruit categories. As a result, The VGG16 provides higher accuracy than conventional CNN and other deep learning algorithms.

Atypical semantic cognition in schizotypal personality disorder and borderline personality disorder

ABSTRACT Increased schizotypal traits have previously been associated with atypical semantic cognition in community samples. However, no study has yet examined whether adults diagnosed with schizotypal personality disorder (SPD) display atypical semantic fluency and memory. We hypothesized that 24 adults diagnosed with SPD would name more idiosyncratic words on the semantic fluency task and show decreased semantic recall for animal and fruit category words compared with 29 participants with borderline personality disorder (BPD) and a community sample of 96 age-matched controls. We examined whether atypical semantic cognition was specifically associated with disorganized and eccentric speech and thinking, or more broadly with pathological personality traits and personality functioning. Our main hypothesis was confirmed, as the SPD participants named more idiosyncratic words and recalled fewer semantically related words compared with controls. Surprisingly, participants with BPD likewise named more atypical words compared with controls. More idiosyncratic semantic fluency was associated with more eccentric speech and thinking. Increased idiosyncratic semantic fluency and reduced semantic recall were both coupled to increased detachment and lowered personality functioning, while reduced semantic recall further was related to increased interpersonal problems. Our findings suggest that persons with SPD, and to a lesser degree BPD, show atypical semantic cognition, which is associated with eccentric speech and thinking, and more broadly with impaired personality function, social withdrawal, and emotional flatness. The idiosyncratic semantic cognition may worsen difficulties with social reciprocity seen in SPD and BPD.

Method development, validation, and risk assessment of multiple pesticide residues of fruits in China.

In this study, a muti-residue analysis method of 40 pesticides in five different categories of fruits in China was developed based on ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Five hundred real samples were analyzed and assessed for the dietary exposure risk. The sample treatment method was optimized by comparing four clean-up methods. The matrix effects of different fruits were evaluated. The analytical method was validated in terms of linearity, limit of detection (LOD), limit of quantification (LOQ), accuracy, and precision. The results showed that the optimal method was the treatment by clean up with ODS (octadecylsilane) and MgSO4. The matrix effect was the strongest in orange and weakest in apple. The LOD and LOQ of pesticides were 0.04-5.9 μg kg-1 and 0.13-19.5 μg kg-1, respectively. The recoveries at three spiked levels were ranged from 71.2 to 115.2% with the RSDs from 0.1 to 19.6%. Twenty-two pesticides were detected in 500 fruit samples fromthe major production regions of China, with concentrations ranging from 0.1 to 1930 μg kg-1. A total of 13, 10, 9, 8, and 4 pesticides were detected in peach, orange, grape, apple, and strawberry. Both the acceptable daily intake (ADI) and acute reference dose (ARfD) for all the detected pesticides were lower than 100%, indicating that the dietary intake risks are acceptable and would not pose potential health risks.

A novel method for vegetable and fruit classification based on using diffusion maps and machine learning

Vegetable and fruit classification can help all links of agricultural product circulation to better carry out inventory management, logistics planning and supply chain coordination, and improve the efficiency and response speed of the supply chain. However, the current classification of vegetables and fruits mainly relies on manual classification, which inevitably introduces the influence of human subjective factors, resulting in errors and misjudgments in the classification of vegetables and fruits. In response to this serious problem, this research proposes an efficient and reproducible novel model to classify multiple vegetables and fruits using handcrafted features. In the proposed model, preprocessing operations such as Gaussian filtering, grayscale and binarization are performed on the pictures of vegetables and fruits to improve the quality of the pictures; statistical texture features representing vegetable and fruit categories, wavelet transform features and shape features are extracted from the preprocessed images; the feature dimension reduction method of diffusion maps is used to reduce the redundant information of the combined features composed of statistical texture features, wavelet transform features and shape features; five effective machine learning methods were used to classify the types of vegetables and fruits. In this research, the proposed method was rigorously verified experimentally and the results show that the SVM classifier achieves 96.25% classification accuracy of vegetables and fruits, which proves that the proposed method is helpful to improve the quality and management level of vegetables and fruits, and provide strong support for agricultural production and supply chain.

CottonSense: A high-throughput field phenotyping system for cotton fruit segmentation and enumeration on edge devices

High-throughput phenotyping (HTP) has become a powerful tool for gaining insights into the genetic and environmental factors that affect cotton (Gossypium spp.) growth and yield. With the recent advances in the field of computer vision, namely the integration of deep learning algorithms, the accuracy and efficiency of HTP systems have improved dramatically, enabling them to automatically quantify such fundamental phenotypic traits as fruit identification and enumeration. However, there is currently no HTP system available for counting all the reproductive phases of cotton crop that can be deployed in agronomic field conditions throughout the growing season. This study presents CottonSense, an advanced HTP system that overcomes the challenges of deployment across multiple growth periods by effectively segmenting and enumerating cotton fruits at four stages of growth, including square, flower, closed boll, and open boll. Consequently, CottonSense enhances agronomic management through increased opportunities for data collection and analysis. Using RGB-D cameras, it captures and processes both two and three-dimensional data, facilitating a wider range of phenotypic trait extractions such as crop biomass and plant architecture. To segment the cotton fruits, a Mask-RCNN model is trained and optimized for faster inference using TensorRT. The model yields an average AP score of 79% in segmentation across the four fruit categories. Moreover, the model's accuracy in estimating total fruit count per image is validated by a strong agreement with the counts given by ten domain experts, as reflected by an R2 value of 0.94. Furthermore, to accurately count the segmented fruits over large populations of plants, an enumeration algorithm based on a tracking strategy is developed that achieves an R2 value of 0.93 when compared to hand-counted fruits in the field. The proposed HTP system, which is implemented entirely on an edge computing device, is cost-effective and power-efficient, making it an effective tool for high-yield cotton breeding and crop improvement. The code for CottonSense is publicly available at https://github.com/FeriBolour/CottonSense.

Fast Recognition and Counting Method of Dragon Fruit Flowers and Fruits Based on Video Stream.

Dragon fruit (Hylocereus undatus) is a tropical and subtropical fruit that undergoes multiple ripening cycles throughout the year. Accurate monitoring of the flower and fruit quantities at various stages is crucial for growers to estimate yields, plan orders, and implement effective management strategies. However, traditional manual counting methods are labor-intensive and inefficient. Deep learning techniques have proven effective for object recognition tasks but limited research has been conducted on dragon fruit due to its unique stem morphology and the coexistence of flowers and fruits. Additionally, the challenge lies in developing a lightweight recognition and tracking model that can be seamlessly integrated into mobile platforms, enabling on-site quantity counting. In this study, a video stream inspection method was proposed to classify and count dragon fruit flowers, immature fruits (green fruits), and mature fruits (red fruits) in a dragon fruit plantation. The approach involves three key steps: (1) utilizing the YOLOv5 network for the identification of different dragon fruit categories, (2) employing the improved ByteTrack object tracking algorithm to assign unique IDs to each target and track their movement, and (3) defining a region of interest area for precise classification and counting of dragon fruit across categories. Experimental results demonstrate recognition accuracies of 94.1%, 94.8%, and 96.1% for dragon fruit flowers, green fruits, and red fruits, respectively, with an overall average recognition accuracy of 95.0%. Furthermore, the counting accuracy for each category is measured at 97.68%, 93.97%, and 91.89%, respectively. The proposed method achieves a counting speed of 56 frames per second on a 1080ti GPU. The findings establish the efficacy and practicality of this method for accurate counting of dragon fruit or other fruit varieties.

Embarking on Comprehensive Exploration of Classification System of Fruits and Vegetables

This research thoroughly investigates the fruit and vegetable classification system, emphasizing exhaustive investigation. This research aims to comprehend, analyze, and document numerous facets of this classification with MobileNetV2. This investigation included a comprehensive literature review, field investigation, and review of relevant scientific documents. In this investigation, we divide the classification of fruits and vegetables into various levels, ranging from the most general, such as kingdom and division, to the most specific, such as order and family. We also investigate the central role of taxonomy in comprehending the evolutionary and phylogenetic relationships between various categories of fruits and vegetables. This research enables us to identify and understand the taxonomic relationships between multiple varieties of fruits and vegetables and classify them into the appropriate botanical families. In addition, we investigate the global diversity of fruit and vegetable varieties, emphasizing the significance of conservation and genetic management to preserve the diversity of these precious commodities. In their efforts to comprehend, manage, and maintain the genetic variety of fruits and vegetables, this research provides researchers, botanists, and producers valuable insights. The findings of this study indicate that investigating fruit and vegetable classification systems is a crucial step in comprehending and conserving this irreplaceable natural resource, which provides direct benefits to humans in the context of global biodiversity conservation. MobileNetV2 research results accuracy epochs(5) = 94.84%, epochs(10) = 98.35%, epochs(15) = 98.69%.

North Sulawesi Single Local Fruit Detection Using Efficient Attention Module Based on Deep Learning Architecture

A Local fruit detection system is an agricultural vision field that can be implemented to increase the profit of a commodity. Besides that, North Sulawesi has a variety of local fruits which are widely used by people in their area and have a high selling value. The sorting system is an essential process of agricultural robots to sequentially separate fruit one by one. This automation process requires an accurate vision system to detect and separate fruit precisely and precisely. In addition, the implementation of a practical application demands a method to be able to work in real-time on low-cost devices. This work aims to design a local single fruit detection system for Sulawesi North by applying deep learning architecture to produce high performance. The architecture is designed to consist of an effective backbone for rapidly separating the distinctive features, an efficient attention module to improve feature extraction performance, and a classifier module employed to estimate the probabilities of each local fruit category. As a result, the designed model produces an accuracy value of 99,27% and 99,57% on the Fruits-360 and the local datasets, respectively. It outperforms other light architectures. In addition, deep learning models are designed to produce higher efficiency values than other competitors and can operate quickly at 100,488 Frames per Second.

Sintesis Nanoemulsi dari Ekstrak Kulit Manggis dengan Metode Energi Tinggi

Mangosteen (Garcinia mangostana L.) has become the main agricultural products contributing to the largest annual foreign exchange from the fruit category. Mangosteen rind contains xanthone compounds as a source of antioxidants, with α-mangostin and γ-mangostin, as the main components which are used as traditional and modern ingredients to treat various diseases. However, mangosteen rind extract has low bioavailability thus resulting in low effectiveness in the application. This study aimed to find the ideal formulation for producing the extract and nanoemulsion of mangosteen peel. The research carried out in two stages: extraction of mangosteen peel and production of nanoemulsion. Ethanol 70% was the best for mangosteen rind extraction, with a percentage yield of 40.96±0.51%, antioxidant activity of 5708.33±159.57 µg AEAC/mL sample, and total xanthones of 373.84±6.49 µg/mL sample. The use of Tween 20 at nanoemulsion production gave the best test results, with particle size of 10.58±1.25 nm, polydispersity index 0.27±0.03, zeta potential -2.34±0.46 mV, and total xanthones 35,28±0,04 µg/mL sample.

Fruit Freshness Detection Based on YOLOv8 and SE attention Mechanism

Fruit is a crucial component of daily diets, emphasizing the importance of ensuring its freshness. Our proposed method utilizes YOLOv8 and SE attention mechanism for detecting the freshness of fruits. Our method balances accuracy and real-time performance while detecting the freshness of different types of fruits. The proposed method achieves an average accuracy of 87.8% and a maximum accuracy of 95.0% for detecting the freshness level of a single fruit category. Experimental results confirm the effectiveness of our proposed method in accurately detecting and localizing the freshness level of fruits, demonstrating its potential for wide application and promotion in the fruit industry.

Citrus Huanglongbing (Greening) Disease

Abstract Huanglongbing disease (HLB), also known as citrus greening disease, is the most important and damaging disease of citrus fruits worldwide. The disease is caused by three closely related bacteria belonging to the genus Candidatus Liberibacter . The bacteria are spread over short distances by sucking insects known as psyllids and by the movement of infected (yet often undetected) planting material between countries and continents. Citrus is a general category of fruits which includes oranges, lemons, grapefruits and tangerines. All types are susceptible to infection and researchers have yet to develop resistant or tolerant varieties. The leading growers of citrus fruits are China, Brazil and India, which collectively produced 75 million tonnes in 2021. All three are affected by the disease; in total around 65 countries have confirmed the disease. HLB is not present in Europe, where Spain and Italy are leading citrus producers. Climate change poses a significant threat to the expansion of the disease. The arrival of HLB in the USA in the early 2000s, at around the same time as Brazil, marked a significant stage in the continuing battle to manage the disease. The USA is a major producer of citrus fruits, which was primarily from Florida before the arrival of HLB. Research efforts were intensified and over $100 million was allocated in the USA from 2007 to 2016 to develop effective ways to reduce damaging losses. Despite major advances in knowledge of the pathogen and insect vector, and how they interact with citrus hosts, it is clear that much more needs to be done to achieve long-lasting success in managing a challenging disease. Information © The Author 2023

Changes in Physicochemical and Bioactive Properties of Quince (Cydonia oblonga Mill.) and Its Products.

Quince (Cydonia oblonga Miller) is a plant that is commonly cultivated around the world, known for centuries for its valuable nutritional and healing properties. Although quince fruit are extremely aromatic, due to their high hardness and sour, astringent, and bitter taste, they are not suitable for direct consumption in an unprocessed form. However, they are an important raw material in fruit processing, e.g., in the production of jams, jellies, and juices. Quince fruits fall under the category of temperate fruits, so their shelf life can be predicted. Considering that technological processing affects not only the organoleptic properties and shelf life but also the functional properties of fruits, the aim of this research was to determine the impact of various types of technological treatments on the physicochemical and bioactive properties of quince fruit. In fresh, boiled, and fried fruits and in freshly squeezed quince fruit juice, basic parameters, such as the content of dry matter, moisture, soluble solids (°Brix), pH, total acidity, water activity, and color parameters (L*a*b*) were determined. The content of key bioactive ingredients, i.e., tannins, carotenoids, flavonoids, phenolic acids, and total polyphenols, was also determined, as well as the antioxidant activity of raw and technologically processed (cooked, fried, and squeezed) quince fruits. The conducted research showed that fresh quince fruit and processed quince products can be a very good source of bioactive ingredients in the diet, such as tannins (3.64 ± 0.06 mg/100 g in fresh fruit; from 2.22 ± 0.02 mg/100 g to 5.59 ± 0.15 g/100 g in products), carotenoids (44.98 ± 0.18 mg/100 g in fresh fruit; from 141.88 ± 0.62 mg/100 g to 166.12 ± 0.62 mg/100 g in products), and polyphenolic compounds (246.98 ± 6.76 mg GAE/100 g in fresh fruit; from 364.53 ± 3.76 mg/100 g to 674.21 ± 4.49 mg/100 g in products). Quince fruit and quince products are also characterized by high antioxidant properties (452.41 ± 6.50 µM TEAC/100 g in fresh fruit; 520.78 ± 8.56 µM TEAC/100 g to 916.16 ± 6.55 µM TEAC/100 g in products). The choice of appropriate technological processing for the quince fruit may allow producers to obtain high-quality fruit preserves and act a starting point for the development of functional products with the addition of quince fruit in its various forms, with high health-promoting values and a wide range of applications in both the food and pharmaceutical industries.

Kuantitas dan Karakteristik Sampah Bahan Pangan : Studi Kasis Surabaya Selatan

Food loss can be defined as the reduction of food quantity at each stage of the food supply chain, particularly in the type of food consumed by humans. This study aims to directly measure the amount and composition of food waste in order to provide complete and detailed information, which can also be used as a reference in formulating policies or preventive actions. Measurement of the quantity and composition of waste refers to SNI 19-3964-1994 with a sampling process for seven consecutive days based on the EPA's "A Guide to Conducting and Analyzing a Food Waste Assessment." The sampling results show that the quantity of food waste for one week (7 days) at Wonokromo Market is 1.08 tons and for Mangga Dua Market it is 1.06 tons, with an average food waste produced at Wonokromo Market of 154.6 kg per day and at Mangga Dua Market of 152.7 kg per day. This number includes only the categories of vegetables, fruits, starchy foods, whole grains, oily fruits and grains, and meat. Vegetables are in the category of food waste with the highest percentage. waste characteristics obtained from laboratory tests with test parameters for water content, calorific value, and CHON.

Development of models and assessment of the world walnut variety fund by fruit quality

Walnut (Juglans regia L.) is a particularly significant plant for humans in terms of its useful properties and in the Russian Federation it can be attributed to the most valuable introducers for forestry and horticulture. It is grown in many countries of the world and the area of its cultivation is constantly expanding. Breeding species for food purposes requires a selection assessment of the quality of its fruits. For this, at different times, appropriate methods were developed and the existing gene pool of the culture was assessed. By now, new varieties and forms have been bred and brought, which also need to be evaluated for rational use for scientific and practical purposes. The descriptions of varieties and forms of walnut often contain insufficient information about all selected traits, which requires the development of new models to assess the variety fund using incomplete data. The purpose of the research is to develop the missing models of selection evaluation using incomplete data and evaluate the world walnut variety fund in terms of fruit quality, to identify varieties and forms of the highest quality category. The well-known methodology and new models have been used for the evaluation. When developing evaluation models for incomplete data, a database of 50 varieties evaluated for all indicators has been used. Using the method of excluding individual indicators, the corresponding multiple regression models have been calculated on its basis, taking into account a different combination of features. The licensed programs Stadia 8.0, Microsoft Excel have been used in the research. The proposed models provide an estimate with an average error of ± 0.48 – 3.72 points at R2 = 0.63 – 0.99. A promising walnut gene pool from 23 countries has been assessed, 69 representatives with fruits of the breeding category of the highest quality identified, and a list of them compiled. Of the 512 ancestors, 19.73% had the selection category of fruits of the highest quality, 62.88% of high-quality fruits, 16.80% of ordinary fruits, and 0.59% of varieties and forms of low-quality fruits.

Very High Food Plant Diversity among Ethnic Groups in Northern Thailand

The combination of the high biodiversity and many ethnicities in Thailand results in extensive ethnobotanical studies, especially in the northern part of the country. Here we have assembled 7620 records from 60 references regarding how plants are used for food among 14 ethnicities in northern Thailand. The uses are based on 1182 different plant species. Vegetables are the most common food category, which contributed more than 60% of all use-reports and involved nearly 900 species. This was followed by the dessert fruit category, which included about 350 species and over 1800 use-reports. The similarity among the different ethnicities was low and the number of uses and species recorded among different ethnicities were significantly related to the number of studies that had been conducted for each group. This implies that additional ethnobotanical studies are still needed in order to conserve and compile the valuable traditional knowledge related to food plants. Many exotic species are now an important part of traditional knowledge, whereas rare endemic species are uncommon among wild food plants. This situation leads to a loss of traditional knowledge about food plants and reduced awareness of their importance. As a key to using this great reservoir of food for local people, traditional knowledge related to local wild food plants can contribute to the zero-hunger goal of the Sustainable Development Goal (SDG). More studies on the nutrient content and health properties of these plants could lead to the development of new crops to meet present consumer demands.