Quality Of Agricultural Products Research Articles

SERS detection of triazole pesticide residues on vegetables and fruits using Au decahedral nanoparticles

Triazole pesticides are widely used in modern agricultural practices to improve agricultural production quality. Simultaneously, unreasonable and standardized use of triazole pesticides could induce a series of potential diseases of humans. Surface-enhanced Raman spectroscopy has attracted enormous research attention because of its label-free and fingerprint detection capability to noninvasively trace extremely low concentration analytes. To the best of our knowledge, there is a lack of systematic comparison regarding the Raman spectral information of triazole pesticides in existing literatures. In this work, we successfully captured the characteristic peaks of six different triazole pesticides individually and simultaneously using Au decahedral nanoparticles. The proposed method exhibited remarkable detection sensitivity, a wide dynamic range, and the capability for in-situ detection of multiple pesticide residues on bean, apple, and vegetable surfaces with satisfactory recovery rates. Therefore, our proposed SERS platform have great applications in agricultural products safety, environmental monitoring and other fields.

Machine learning for the identification of colour cues to estimate quality parameters of rocket leaves

Computer Vision Systems (CVSs) have proved to be a powerful tool to evaluate the quality of agricultural products in a non-destructive, contactless, sustainable and objective way. Machine learning techniques have proved to simplify the development of CVS and to provide better performance and greater flexibility in matching the requirements of different products and environmental characteristics, but they are often computationally complex and difficult to be understood by humans. It is desirable to develop methods that exploit the benefits of learning and generate simple and fast solutions that are also interpretable by humans. The approach described in this paper analyses a previously developed and effective machine learning model to extract the information useful to develop computationally light and easily understandable algorithms that evaluate the characteristics of interest on rocket leaves. A Random Forest model previously developed to classify visual quality and to estimate chlorophyll and ammonia contents in rocket leaves has been studied to identify a small set of visual characteristics (colours) that correlate with relevant properties of the product. These visual characteristics have been used as input for several simple, fast and easily understandable algorithms that classify visual quality (QL) and estimate chlorophyll and ammonia contents with lower computational complexities compared to the original Random Forest model. Results obtained by these methods are shown and compared with the ones provided by the original Random Forest model. All the algorithms provided a good separation between marketable and non-marketable samples. They required from 1ms to 22 ms to classify a new sample instead of the 25 ms of the original Random Forest model. Additionally, two methods provided good prediction of chlorophyll (R2v = 0.70) and ammonia (R2v = 0.72) contents requiring only 3 ms and 1 ms respectively.

The effect of different biopreparations on soil physical properties and CO2 emissions when growing winter wheat and oilseed rape

Abstract. The introduction of innovative technologies in agriculture is key not only to improving the efficiency of agricultural production and crop yields and quality but also to balancing energy use and preserving a cleaner environment. Biopreparations are environmentally friendly means of restoring the vitality of the soil in which plants can thrive. Biopreparations have an impact on soil health and alter greenhouse gas emissions. The aim of this study was to investigate the effects of different biopreparations on soil porosity, temperature, and CO2 (carbon dioxide) emissions from the soil in northeast Europe (Lithuania) when growing winter wheat and oilseed rape. The experimental studies were carried out over 3 years, and each spring, after the resumption of winter crops, the soil surface was sprayed with biopreparations of different properties or with mixtures of biopreparations under seven scenarios, with one scenario left as a control. Soil porosity, temperature, and CO2 emissions from the soil were measured regularly every month from April to August. The application of the biopreparations showed a cumulative effect on the soil properties. In the third year of the study, the total porosity of the soil was higher in all scenarios compared to the control, ranging between 51 % and 74 %. The aeration porosity of the soil was also higher in all years of the study than in the control, although no significant differences were obtained. The results of the studies on CO2 emissions from the soil showed that, in the first year, the application of the biopreparations increased emissions compared to the control. However, when assessing the cumulative effect of the biopreparations on soil respiration intensity, it was found that, in the third year, most of the biopreparations led to a reduction in CO2 emissions compared to the control. The lowest emissions were achieved with the biopreparations consisting of essential oils of plants, 40 species of various herbs extracts, marine algae extracts, Azospirillum sp., Frateuria aurentia, Bacillus megaterium, mineral oils, Azotobacter vinelandi, humic acid, gibberellic acid, sodium molybdate, Azototbacter chroococcum, Azospirillum brasilense, etc. Evaluating the effectiveness of biopreparations on soil porosity, temperature, and CO2 emissions from the soil, it can be stated that the best effect was achieved in all 3 research years in using biopreparations with Azotobacter chroococcum, Azotospirilum brasilense, various herbs, marine algae extracts, oils of plants, and mineral substances. The multiple-regression model showed that soil temperature has a greater influence on the variation of CO2 emissions than soil aeration porosity.

Spectral Heterogeneity Analysis and Soil Organic Matter Inversion across Differences in Soil Types and Organic Matter Content in Dryland Farmland in China

Soil organic matter (SOM) plays an important role in agricultural production and arable land quality improvement. Hyperspectral technology enables frequent surveys over large areas. In this study, we explored the spectral heterogeneity of differences in soil types and SOM content, and proposed a method for measuring SOM content in large areas using spectroscopy. The results indicate regional variations in factors affecting soil spectral absorption peaks, with noticeable latitudinal disparities. The first-order differential partial-least-squares method provided the best prediction for the SOM inversion. The coefficient of determination (R2) for the SOM inversion model was 0.93, and the root mean square error (RMSE) was 3.42, with an 8.49 g/kg difference in the SOM content. When the difference in SOM content fell between 8 and 15 g/kg, the inversion effect model performed best. The optimal model R2 exceeded 0.95, and the RMSE was less than 5. The comprehensive analysis showed that the organic matter content was an important factor affecting the SOM content estimate and must be considered in the real process. In addition, it is crucial to categorize soil samples on the basis of distinct soil types while maintaining a consistent range of SOM content within the same soil type, ideally between 8 and 15 g/kg. Subsequently, the first-order differential partial least squares method is applicable. These results are expected to contribute to the acquisition of high-quality information on variations in the SOM of complex large-scale areas.

EVALUATION OF THE USE OF SUPPORT FINANCED IN SMALL AND MEDIUM SIZED ENTERPRISES UNDER THE SAPARD PROGRAM FOR AGRICULTURAL PROCESSING AND THE REGIONAL OPERATIONAL PROGRAM IN THE WARMIŃSKO-MAZURSKIE VOIVODESHIP

The aim of the article is to present the essence and assessment of the use of financial assistance for the SME sector from the SAPARD Program and the Regional Operational Program 2014-2020 in the Warmian-Masurian Voivodeship. The subject of the research was the assessment of EU subsidies as a source of enterprise financing. Their aim was to assess the strengths and weaknesses, opportunities and threats resulting from obtaining funds from the ROP for the development of SMEs in the Warmian-Masurian Voivodeship. SWOT analysis was used to achieve the research objective. The research was carried out using the interview method, in which the research tool was a standardized survey questionnaire. They were implemented among the beneficiaries of the Regional Operational Program of Warmia and Mazury for 2014-2020. In addition, classic methods and techniques developed in legal sciences were used. The basic method of research in this sphere was the formal dogmatic method. In addition, the source of data was information obtained from the Agency for Restructuring and Modernization of Agriculture. The SAPARD program gave processing plants in the Warmian-Masurian Voivodeship the opportunity to become familiar with the new support rules used in the EU structural funds. Projects implemented by the SME sector in the Warmian-Masurian Voivodeship have brought tangible benefits in terms of adapting quality and sanitary standards of agricultural processing production to EU requirements. In the case of the Regional Operational Program 2014-2020, the availability of EU funds in the Warmia and Mazury region was assessed by the respondents as insufficient. Due to the completed financial perspective 2014-2020 of the Regional Operational Program of Warmia and Mazury, the possibility of obtaining EU funds has decreased, which significantly limits the development of SME sector enterprises.

Greenhouse environmental control target constrained by discrete surface curvature and multi-objective optimization algorithm

Protected greenhouse provides a suitable micro-environment for crop growth, which enhances the quality and yield of agricultural products. How to optimize the greenhouse environment is currently a pressing issue in protected agriculture. In this research, we propose a new environmental control method from the perspectives of crop photosynthesis rate and environmental control cost. The photosynthesis rate data are collected from a nested experiment of tomatoes, and these data are combined with the support vector regression algorithm to establish a photosynthesis rate model. The cost function of greenhouse environmental control is established by theoretical calculation. The non-dominated solution sets of photosynthesis rate and control cost are obtained through an improved non-dominated sorting genetic algorithm Ⅱ. To determine the optimal solution among the numerous Pareto solutions, the geometric characteristics of the photosynthetic response surface are considered to constrain the control target. Then, a discrete surface curvature method is proposed to computer the constraint region. The intersection between the Pareto solutions and the constraint region is regarded as the environmental control target. The validation experiment demonstrates that the proposed method can effectively enhance photosynthetic accumulation and reduce control cost. Compared to the traditional saturation method, this method achieves a 53% cost reduction while still obtaining 89% photosynthetic accumulation. Compared to the U-chord method, this method increases photosynthetic accumulation by 12% with only a 3% increase in cost. The research has important implications for protected agriculture production.

Geographical origin discriminatory analysis of onions: Chemometrics methods applied to ICP-OES and ICP-MS analysis

Geographical origin is an important determinant of agricultural product quality and safety. Herein, inductively coupled plasma (ICP) analysis was applied to determine the inorganic elemental content of onions and identify their geographical origin (Korean or Chinese). Chemometric, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least square discriminant analysis (OPLS-DA) were applied to the ICP results. OPLS-DA distinguished each group, and 17 elements with variable importance in projection (VIP) values of ≥ 1 were selected. The receiver operating characteristic (ROC) curve had an area under the curve (AUC) of 1, indicating excellent discriminatory power. Differences in elemental content between groups were visually observed in a heatmap, and the country of origin was determined with 100% accuracy using canonical discriminant analysis (CDA). This method accurately distinguishes between Korean and Chinese onions and is expected to be beneficial for identifying agricultural products.

Comparison of the Efficiency of Hyperspectral and Pulse Amplitude Modulation Imaging Methods in Pre-Symptomatic Virus Detection in Tobacco Plants.

Early detection of pathogens can significantly reduce yield losses and improve the quality of agricultural products. This study compares the efficiency of hyperspectral (HS) imaging and pulse amplitude modulation (PAM) fluorometry to detect pathogens in plants. Reflectance spectra, normalized indices, and fluorescence parameters were studied in healthy and infected areas of leaves. Potato virus X with GFP fluorescent protein was used to assess the spread of infection throughout the plant. The study found that infection increased the reflectance of leaves in certain wavelength ranges. Analysis of the normalized reflectance indices (NRIs) revealed indices that were sensitive and insensitive to infection. NRI700/850 was optimal for virus detection; significant differences were detected on the 4th day after the virus arrived in the leaf. Maximum (Fv/Fm) and effective quantum yields of photosystem II (ΦPSII) and non-photochemical fluorescence quenching (NPQ) were almost unchanged at the early stage of infection. ΦPSII and NPQ in the transition state (a short time after actinic light was switched on) showed high sensitivity to infection. The higher sensitivity of PAM compared to HS imaging may be due to the possibility of assessing the physiological changes earlier than changes in leaf structure.

AGRI-FOOD PRODUCTS QUALITY AS EXPORTS COMPETITIVENESS DETERMINANT OF THE REPUBLIC OF SERBIA

Products quality and safety are important determinants of countries' export competitiveness. Issues of the quality of agri-food products are gaining more and more importance, bearing in mind modern challenges and consumer needs. This is very important for developing countries, including the Republic of Serbia, bearing in mind the importance of this sector for exports and overall economic development. On the other hand, these countries are lagging behind developed countries in terms of quality standards, due to the expensive certification and quality standardization procedures. The growth of competitiveness should be achieved through the improvement of product quality standards, product differentiation and introduction of innovations in the agri-food sector. The goal of the research is to examine the quality of the most important export agri-food products of the Republic of Serbia. The unit value (UV) index, as a measure of product quality, is used to analyse the quality of the most important agri-food products on the most important export markets, with using secondary ITC data. The multiple regression determined that the improvement of agricultural products quality has a positive impact on the growth of exports and competitiveness of agri-food sector of the Republic of Serbia. Namely, the competitiveness of agri-food sector will depend primarily on the ability of this sector to respond to the requirements related to food safety and quality standards, as well as the possibility of investing and innovating processing capacities, in order to create a high-quality final product with high value added.

Motion-Control Strategy for a Heavy-Duty Transport Hexapod Robot on Rugged Agricultural Terrains

Legged agricultural transportation robots are efficient tools that can autonomously transport goods over agricultural terrain, and their introduction helps to improve the efficiency and quality of agricultural production. Their effectiveness depends on their adaptability to different environmental conditions, which is especially true for heavy-duty robots that exert ground forces. Therefore, this study proposes a motion-control strategy for a heavy-duty transport hexapod robot. Two critical tasks were accomplished in this paper: (1) estimating the support surface angle based on the robot’s foot position and body posture, and accordingly determining the motion constraint conditions on this support surface and the body posture based on energy optimization; (2) proposing an adaptive fuzzy impedance algorithm for real-time force–position composite control for adjusting foot position, in order to reduce the steady-state force tracking error caused by terrain stiffness, thus ensuring body stability through tracking of variable foot-end forces. An element of hardware in the loop control platform for a 3.55-ton device was designed and compared with the current popular force-control methods under different external contact terrains. The results show that the proposed control method can effectively reduce force errors, establish support forces faster on less-stiff environments, and reduce the torso tilt during phase switching.

First report of leaf spot on sweet potato (Ipomoea batatas) caused by Alternaria alternata in Bulgaria

For the first time in Bulgaria, leaf spotting on sweet potatoes was investigated, and the causative agent, Alternaria alternata, was reported. The collected morphological data aimed to determine the identity of the fungus. According to Koch, the primary isolates from the damaged parts are identical to those produced from pathogenicity tests. In a laboratory we tested sixty-two leaves which had similar symptoms, with slight to medium-sized lesions with concentric circles and a yellowish halo. In general, the attack from Alt. alternate affected 7% of all plants and 32% of the examined leaves. The pathogen tests proved that the pathogen could also cause damage to tubers during storage. Knowing the etiology of diseases is essential for taking preventive measures and thus preserving the quality and quantity of agricultural production. Keywords: Alternaria alternata, sweet potato, leaf spots, pathogen, pathogenicity

Development of a novel nutritional assessment model based on strontium and other compositional factors in apples across seven regions in China

In the aftermath of the Green Revolution’s remarkable enhancement of global crop yields, particularly in rice and wheat, the focus on hunger has shifted from mere caloric sufficiency to addressing deficiencies in essential micronutrients. This transition highlights the concept of hidden hunger, where inadequate intake of vital nutrients persists despite access to an adequate number of calories. Among these crucial elements, strontium (Sr), an alkaline earth metal, plays a pivotal role in human physiology, notably in bone health and disease. Despite its significance, variations in Sr. content across different regions, combined with diverse dietary habits, result in insufficient intake levels, particularly within China. This study aims to bridge this gap by integrating traditional sensory attributes and various nutritional parameters including Sr. content for a comprehensive assessment of apples from major Chinese apple-producing regions. Through meticulous analysis and the establishment of characteristic indices, a novel apple quality evaluation system was developed that exemplifies a broader framework for evaluating agricultural products within China. This innovative approach not only enhances our understanding of agricultural product quality but also provides a practical solution to hidden hunger by ensuring informed consumer choices and improving nutritional intake. Furthermore, it serves as a valuable foundation for addressing deficiencies in essential micronutrients and advancing global efforts against hidden hunger.

Image based Chili Crop Disease Prediction Using Deep Transfer Learning

Crop diseases have a terrible impact on food protection and can result in considerable reductions in both the supply and quality of agricultural products. Human professional have traditionally been relying on to diagnose crop diseases caused by insects, pests, virus, bacteria, fungal, inadequate nutrition, or adverse environmental conditions. This, however, is costly, time demanding, and in some situations unworkable. Thus, in the area of agricultural information, the automatic identification of crop diseases is significantly required. Many strategies have been presented to solve this challenge, with deep learning becoming as the preferred approach due to its outstanding performance.This research describes a method for detecting chili leaf diseases using a deep convolutional neural network. We compared performances of four architectures: MobileNet, Inception-ResnetV2, EfficientNetB0, and DenseNet. The proposed approach evaluated the findings using measures such as accuracy, loss and time. Our model compares favorably to EfficientNetB0 with an accuracy of 0.995, a loss of 0.023, and time is 5 minutes 45 seconds. EfficientNetB0, a compact deep learning architecture has fine tuned to classify two forms of chili leaf diseases. The method was tested on 2475 photos from the Plant Village dataset.

SIDERATION — A FACTOR OF THE FORMATION OF THE MAIN INDICATORS OF THE SOIL IN ORGANIC AGRICULTURE

The article proves the relevance of using sideral crops in organic farming technologies. The results of scientific research on the use of sideration as an ecologically safe factor in the formation of the main indicators and properties of the soil are analyzed. It is noted that nowadays the cultivation of sideral crops is one of the main alternatives for replacing chemically synthesized fertilizers. Examples of the use of siderates as environmentally safe types of fertilizers that contribute to the restoration of soil fertility and the increase in the yield of agricultural crops are given. It was noted that side crops under organic land use are an effective and productive component of the technology of growing agricultural crops, contribute to improving the structure and quality of the soil, providing mineral nutrition and creating favorable conditions for the growth and development of plants, which affects the increase in yield with the complete rejection of chemical fertilizers and synthetic pesticides. It is noted that when using sideration in organic technologies for growing agricultural crops, it is necessary to make a reasonable selection of sideration crops with a high adaptive potential, taking into account their biological characteristics. The selection of siderates must be carried out taking into account the soil and climatic zone of cultivation, moisture availability and temperature regime of the soil, biological features of growing siderate cultures and the agronomic value of their introduction, features of the effect of siderate on the structure of the soil, its density and porosity, providing the soil with organic matter (humus) and nutritional mineral elements, as well as the yield and quality of agricultural products.

Withdrawal: Yuan, M., Zhao, C., Cheng, C., Outsourcing behavior of farmers in agricultural production under rapid transformation. Agronomy Journal. 2022; Accepted Articles. https://doi.org/10.1002/agj2.21228.

AbstractAgricultural convergence technology based on the Internet of Things (IoT) provides high values for enhancing the productivity of processing the quality increase of agricultural products in agriculture production. Smart Farming is rising exponentially as data have been turned into a core factor for digital agriculture to enable farmers to make important decisions. The objective knowledge gathered through sensors provides practical advantages to increase efficiency and sustainable growth. The biggest challenge in agriculture is encouraging farming and providing customers with the highest quality possible. Furthermore, the internet of things‐assisted production monitoring model (IoT‐PMM) with the digital platform has been proposed to improve agricultural production and the quality of agricultural products. The proposal focuses on predicting the different diseases that affect crop growth and providing the farmer with information about the ratio of pesticides used to decrease the risk of pesticide overuse in the environment and human health. The experimental results show the IoT‐PMM to implementation precision farming, an approach to future agriculture that facilitates supply and demand estimation, real‐time management, and quality conservation over the entire life cycle of agricultural produce through convergence technologies. The simulation analysis result shows that the IoT‐PMM scores the high precision ratio of 94.9%, high accuracy ratio of 98.3%, a better performance ratio of 98.1%, improves prediction ratio of 94.9%, reduces energy consumption ratio of 21.7%, enhances effective crop monitoring ratio of 96.1%, production density ratio of 92.7% and reduces moisture content ratio of 15.1% when compared to other popular models.This article is protected by copyright. All rights reserved

Pelatihan Smart Farming Berbasis IoT Kepada Kelompok Tani Jamur Tiramu Dalam Peningkatan Ekonomi Di Kabupaten Kampar

Purpose - This community service activity aims to socialize Smart Farming as a new concept in agriculture by integrating technology as an innovation. Smart Farming makes it easy for farmers to increase the quality and quantity of agricultural products through technology. The technology used in this activity is IoT (Internet of Things), a mushroom media sprayer that can be controlled automatically and adjusted to the required temperature. Design/methodology/approach – The methodology used in this community service activity is mentoring and outreach. This activity is a training that concerns two activity themes, namely mentoring the installation of IoT on mushroom media and socializing the business management of oyster mushroom products. Assistance activities will be carried out by technology experts who will guide partners in assembling IoT circuits. In comparison, the socialization that will be carried out will involve resource persons who are experts in agribusiness and will provide debriefing on the management of oyster mushroom input supplies. Findings – Community service activities for Tiramu oyster mushroom partners that are carried out obtain the results of activities that bring significant benefits to partners. The benefit obtained from this activity is an automatic sprayer technology that can be adjusted to the room temperature conditions of the oyster mushroom required by the oyster mushroom. Partners' skills and knowledge also increase with socialization activities that equip partners with knowledge in improving the quality and quantity of oyster mushroom products produced. From some of the things mentioned above, community service activities with results in a series of IoT and partner skills can be said to have enormous benefits for Tiramu oyster mushroom partners. Originality/value – The implications of community service activities produce smart farming technology tools and the ability of Tiramu oyster mushroom partners to improve product quality and quantity.

Comprehensive Analysis of Phenolic Compounds, Carotenoids, and Antioxidant Activities in Lactuca sativa var. longifolia Cultivated in a Smart Farm System

Smart farming is a promising strategy for future agriculture, and the global market for smart agriculture is growing rapidly. The aim of this study was to compare the quality of agricultural products based on their cultivation method. The components and antioxidant activities of Lactuca sativa (LS) var. longifolia cultivated using conventional agriculture methods (CAMs) and smart farm agriculture methods (SAMs) were compared. ABTS+ and DPPH radical scavenging activity assays were performed to compare the antioxidant activities of LS grown using CAMs and SAMs. High-performance liquid chromatography with a reverse-phase column was employed to analyze the phenolic and carotenoid compounds. In both the SAM and the CAM, β-carotene was found to be the most abundant compound. LS grown using the CAM had high total polyphenol content and ABTS+ radical scavenging activity, while LS grown using the SAM had high individual phenolic content and DPPH radical scavenging activity. This study developed a new method of SAM and confirmed that SAM produces a higher quantity of phenolic compounds and total flavonoid contents than CAM in LS. This study also highlights the SAM’s functioning, gives crop quality inspection guidelines, and standardizes LS. The new SAM is effective for application in undeveloped urban spaces, is more convenient than the CAM, and represents a way to develop and manage LS quality.

Design and fabrication of a gripper propotype for a fruit harvesting machine

In Vietnam, post-harvest losses of fruits and vegetables are very high. According to analysis reports, Vietnam must invest more in harvesting and post-harvest technologies to enhance the global competitiveness of agricultural products, including fruits and vegetables. Fruit harvesting machines are an effective solution to enhance the quality of agricultural products, including fruits and vegetables. The gripper can be considered the most important component of a harvesting machine when it comes to horticulture products, since it acts as interface between the robotic system and product. To pick up fruits, which frequently have complex shapes and poor mechanical properties, a gripper must be designed carefully. In addition to being able to pick the produce, it is crucial that a gripper does not damage the fruit during harvest. The weak force may cause the gripper to be unable to pick the fruit. If excessive force is applied or improper picking technique is used, the grippers could damage fruits. However, Vietnam has not widely adopted the use of automatic machine in agriculture. This is due to the uniqueness of fruits in each region; for example, fruits in Vietnam have their own shape and mechanical properties. Therefore, it is occasionally inappropriate to utilize harvesters that have already been imported. This paper shows the development and fabrication of a gripper for common Vietnam fruit grasping. A new prototype has been fabricated by 3D printing method and tested in the laboratory. The prototype of a gripper has been subjected to simulation and experimental testing with real fruits to verify that the proposed model is capable of meeting all objectives’ requirements. The proposed gripper was straightforward and efficient. The developed device exhibited dependability and stability and is suitable for farmers who require independent harvesting. In addition, the designed gripper can be produced at a lower cost than comparable machines developed by other commercial products. Key words: Harvesting machine, gripper, Solidwork, prototype, 3D printing, low-cost production

Machine learning approaches for estimating apricot drying characteristics in various advanced and conventional dryers

AbstractDrying plays a crucial role in preserving the quality of agricultural products. Nevertheless, suboptimal conditions in drying systems have an adverse effect on drying characteristics and energy efficiency. Machine learning approaches are innovative and reliable that have been successfully used to solve such challenges and achieve optimization in drying processes. In this study, five machine learning approaches (multilayer perceptron [MLP], gaussian processes [GP], support vector regression [SVR], k‐nearest neighbors [kN], and random forest [RF]) were used to estimate moisture content and moisture ratio of apricot in five various dryers (convective [CV], microwave [MW], infrared [IR], microwave‐convective [MW‐CV], and infrared‐convective [IR‐CV]). Also, the values of specific energy consumption (SEC) and effective moisture diffusivity (Deff) were calculated in these dryers. Accordingly, the best result of the Deff (3.14 × 10−10 m2/s) and the minimum value of the drying time (130 min) and SEC (18.67 MJ/kg) were obtained using MW‐CV hybrid dryer. While the lowest values of Deff (2.09 × 10−11 m2/s) and highest drying time (18.5 h) and SEC (209.34 MJ/kg) were detected in CV dryer at 50°C. The best correlation coefficients (R) for the estimation of moisture content were gained using RF technique for k‐fold cross validation and train‐test split with the values of 0.9908 and 0.9912, respectively. Moreover, moisture ratio results showed that the MLP achieved the highest R value over 0.9985 for both validation methodologies. In the discrimination of the drying methods, the MLP had the greatest accuracy as 82.00% and 86.00% for k‐fold cross validation and train‐test split, respectively. The results showed that the RF and ML models could potentially be used for estimation and discrimination for drying applications.Practical ApplicationsRecently, there has been an increased interest in healthy food choices such as foodstuffs, snacks, and dried products. This trend has captured the attention of both dietitians and conscious consumers. Apricots are a prime example of a valuable dried product that can be dry in various conditions. Machine learning techniques can be used for rapid and non‐destructive determination of drying characteristics and such techniques yield objective and accurate results. Present findings revealed that texture machine learning models could be used as an effective and reliable discrimination tool for dried products.

Principles of legal regulation of relations in the field of production, circulation and labeling of organic agricultural products

The article is aimed at studying the legal framework for organic agricultural production through the prism of the principles of legal regulation in this area, and also at formulating, on this basis, conclusions and proposals aimed at improving current legislation. One of the modern objects of agrarian legal regulation, as well as a way to implement the strategy of sustainable agricultural development, is organic agricultural production, which is a guarantee of balancing economic, environmental and social priorities of agricultural development, a guarantee of a high level of safety and quality of agricultural products, and ensuring food security of the State. The effectiveness of legal regulation depends on the level of scientific development of the principles, but a significant drawback of the current organic legislation is the absence of some important principles. An important basis for legal regulation of organic agricultural production is the principle of priority, which is a general requirement in the law that organic agricultural production has advantages over conventional production in the form of rules designed to ensure not only its stable functioning, but also the spread and improvement of such activities. The manifestation of the principle of priority of organic agricultural production is also evidenced by the state’s interest in a separate legal regulation of this type of activity and filling its content with some public law features that ensure the production of agricultural products of the highest quality. Given the undeniable strategic importance and priority of organic agricultural production, one of the main directions of the state agricultural policy should be state support for this activity. An extremely important characteristic of organic agricultural products is a high level of quality and safety. Therefore, the defining principle of the State policy of these social relations should be the principle of ensuring quality and safety and State control at all stages of production and circulation. Based on the analysis of current organic legislation, the article concludes that an essential need for proper legal regulation of relations in this area is the need to legislate the above-mentioned legal principles on which the State policy in the field of production, circulation and labeling of organic agricultural products should be based.