Optimal Harvest Time Research Articles

Intelligent Agriculture System using KNN Algorithm

The agricultural sector faces the challenge of maximizing crop yields while maintaining sustainability and resource efficiency. Nostalgic or Romantic Views, oftenreliant on manual monitoring and subjective decision-making, can result in inefficiencies and lower productivity. To address these issues, an intelligent agriculture system utilizing the K-Nearest Neighbors (KNN) algorithm has been proposed. This paper presents an intelligent agriculture system that leverages the K- Nearest Neighbors (KNN) algorithm to enhance crop management and yield prediction. By analyzing various environmental factors such as soil moisture, temperature, and humidity, the system accurately classifies the optimal planting and harvesting times for different crops. Additionally, it provides real-time recommendations for irrigation and fertilization based on predictive analysis. The integration of KNN allows for adaptive learning from historical data, thereby improving decision-making processes for farmers. This approach aims to optimize resource utilization, increase productivity, and promote sustainable agricultural practices. The proposed intelligent agriculture system leverages the K- Nearest Neighbors (KNN) algorithm to enhance crop management and sustainability. By analyzing real-time environmental data and historical agricultural records, the system provides precise recommendations for optimal planting and harvesting times, efficient irrigation schedules, and tailored fertilization strategies. This data-driven approach aims to increase crop yields, optimize resource utilization, and reduce the environmental impact of farming practices. The system's continuous learning capability allows it to adapt to changing conditions, ensuring that farmers receive up-to-date and accurate guidance. Overall, the integration of KNN in agriculture represents a significant advancement towards sustainable and efficient farming.

Late Harvest: Freezing Temperatures Reduce the Root Yield and Sugar Content of Beta vulgaris L.

ABSTRACTSugar beet (Beta vulgaris L.) is the main source of white sugar in northern China, and an optimal harvesting time is key for maximising its yield and sugar content. The goal of this study was to investigate the effect of gradually extending harvest times on the growth, physiological characteristics, yield and sucrose accumulation in sugar beet and identify the optimal harvesting time. We conducted a 3‐year experiment across different harvesting times from 23 Sep to 28 Oct (harvest every 7 days) to examine the effects of different magnitudes of temperature reduction. The yield and sugar content were the highest in time III (the daily mean/minimum temperature: 9.5°C/5°C) in 2020, time III (the daily mean/minimum temperature: 8.2°C/2°C) and IV (the daily mean/minimum temperature: 10.1°C/2°C) in 2021 and time IV (the daily mean/minimum temperature: 10.5°C/5°C) and V (the daily mean/minimum temperature: 7°C/−1°C) in 2022. The yield and sugar content were low at an early harvest, as the biomass and sucrose accumulation process was not complete. However, the decrease in temperature (daily minimum temperature below 0°C) during late harvest leads to a decrease in the yield and sugar content because of the decreased sucrose accumulation of source leaves, increased sucrose decomposition and the poor transport capacity of phloem in the stem. Therefore, the optimal harvesting time for sugar beet in northern China depends on temperature conditions. It is optimal when (1) the daily minimum temperature gradually decreases to 0°C but not lower than 0°C and when (2) the daily mean temperature is approximately 10°C. This work will help sugar beet producers harvest high‐quality crops and reduce unnecessary losses in northern China.

A Review of The Effects Of Light-Emitting Diodes (LEDs) on The Growth of Sunflower Microgreens and Their Nutritional Potential

Sunflower (Helianthus annuus) microgreens have become known as a potent source of essential nutrients and bioactive compounds with numerous health benefits. The microgreens industry has traditionally favored popular microgreens from the Brassicaceae family such as kale, rocket, and broccoli. Sunflower microgreens are characterized by their richness in vitamins, minerals, antioxidants, and phytochemicals that contribute significantly to a nutritious diet. However, their nutrient content can be influenced by various factors, including growing conditions and lighting. Light-emitting diodes (LEDs) offer precise control of light spectrum, light intensity, and lighting duration, enabling customized lighting systems optimized for growing sunflower microgreens. Pre-treatment and optimal harvest timing affect the quality and yield of microgreens, and sunflower microgreens are no exception. Accordingly, sunflower microgreens are typically harvested within 7 days of cultivation, making them ideal for mass production. The use of LED technology in the cultivation of microgreens offers the opportunity to further enhance their nutritional value and therapeutic potential. This review provides an overview of the benefits of sunflowers, sunflower microgreens, pre-treatments, and the ideal harvest period. The potential improvements from LED lighting are discussed and its impact on human health is explained.

Co-cultivation of high-value microalgae species with filamentous microalgae for dairy wastewater treatment

The study examined the feasibility of co-culturing high-value microalgae sp. (Chlorella vulgaris (C.), and Scenedesmus (S.)) with filamentous microalgae sp. (Tribonema (T.) and Lyngbya (L.)) to remediate dairy wastewater (DW) and enhance biomass production and harvesting. The results showed that biomass productivity increased by 12‒174% compared to monocultures, and the best consortium was S:T. This consortium achieved the highest biomass productivity of 84.25 mg L−1 d−1 while removing 86.7% of chemical oxygen demand (COD), >88.7% of NO3−-N and >98.5% of PO43–-P. The study also tested the effect of harvesting time on the accumulation of biochemical components and found the optimal harvesting times of day 9 and day 11 to achieve maximum carbohydrate and lipid productivity, respectively. Additionally, the microalgae consortium S:T achieved a high biomass recovery of 78.5%, compared to 32.4% obtained for S. alone, highlighting its potential for efficient DW remediation and resource recovery.

Looking for the optimal harvest time of red grapes with an enzymatic electrochemical multisensory system

The timing of the grape harvest is a critical decision for winemakers, as it greatly impacts the quality and organoleptic characteristics of the resulting red wines. One key indicator for determining the optimal harvest time is the phenolic content of the grapes. As the berries ripen, phenolic compounds in the grape skin cells migrate from the seeds to the pulp and finally to the skin. However, monitoring these phenolic changes, particularly in the seeds, presents a challenge. This research addresses this problem by developing a novel technique to track the phenolic composition of seeds during ripening. The objective is to provide a reliable method for winemakers to monitor the phenolic evolution and improve harvest decision-making.In this study, a multisensory system consisting of four electrochemical enzymatic carbon paste sensors, modified with tyrosinase and electrocatalytic materials (AuNPs, lutetium phthalocyanine, and nickel oxide nanoparticles), was employed to analyze the phenolic content in grape seed extracts. The system monitored weekly changes in the phenolic composition of the seeds from three red grape varieties—Cabernet Sauvignon, Tempranillo, and Prieto Picudo—during their ripening from veraison to being overripe. Using voltammetric techniques, the electrochemical responses were characterized by shifts in peak positions and intensity changes, reflecting the oxidation/reduction of phenols. Principal Component Analysis (PCA) demonstrated the ability of the array of sensors to discriminate phenolic changes across ripening stages, while Partial Least Squares (PLS) regression provided robust correlation models between the electrochemical responses and seed phenolic content, with correlation coefficients ranging from 0.93 to 0.99. The developed methodology successfully tracked phenolic changes, offering a promising tool for monitoring grape seed maturation and assisting in determining the optimal harvest time.

Multimodal Data Fusion for Precise Lettuce Phenotype Estimation Using Deep Learning Algorithms.

Effective lettuce cultivation requires precise monitoring of growth characteristics, quality assessment, and optimal harvest timing. In a recent study, a deep learning model based on multimodal data fusion was developed to estimate lettuce phenotypic traits accurately. A dual-modal network combining RGB and depth images was designed using an open lettuce dataset. The network incorporated both a feature correction module and a feature fusion module, significantly enhancing the performance in object detection, segmentation, and trait estimation. The model demonstrated high accuracy in estimating key traits, including fresh weight (fw), dry weight (dw), plant height (h), canopy diameter (d), and leaf area (la), achieving an R2 of 0.9732 for fresh weight. Robustness and accuracy were further validated through 5-fold cross-validation, offering a promising approach for future crop phenotyping.

Nutritional content of black soldier fly (Hermetia illucens L.) larvae at different stages of active feeding

Abstract Hermetia illucens L., commonly known as the black soldier fly (BSF), is an exemplary group of insects known for waste decomposition, and its maggots can be utilized as a protein source for livestock, poultry, and fish applications. The propagation of BSF maggots is relatively straightforward and can be cultivated on a large scale, contingent upon the type of feed provided. Chitin, present in the outer layer of BSF maggots, can act as an anti-nutrient, diminishing its benefits as a protein feed. This research aimed to evaluate maggot development based on the optimal harvesting time to achieve the lowest chitin content along with other proximate elements (nutrients) of significance. A quantity of 10 grams of BSF eggs was cultivated in a waste medium comprising tofu dregs and fruit and vegetable wastes. Harvesting was performed on 14, 16, 18, and 20-day. Proximate analysis results indicate that the 18th day was the optimum time for harvesting BSF maggots, just before reaching maturity, exhibiting the lowest chitin content and the highest protein levels. The findings of this study will serve as a reference for pre-treatment to reduce chitin content in BSF maggots before in vivo testing on broiler chickens.

Development of a visuo-tactile sensor for non-destructive peach firmness and contact force measurement suitable for robotic arm applications

Precise measurement of firmness was crucial for determining optimal harvesting times, implementing rational storage strategies and minimizing avoidable waste. Current technologies for assessing peach firmness struggled to balance high precision and non-destructive methods, while demonstrating high sensitivity to environmental disturbances, thereby limiting their application to production line. Future various scenarios in agriculture would increasingly rely on robotic arms, yet existing firmness assessment technologies were not compatible with these automated systems. Additionally, monitoring contact force was essential for flexible operation of the robotic arms. This work introduced a visuo-tactile sensor equipped with markers to capable of measuring peach firmness and monitoring contact force simultaneously during a single contact process, making it suitable for robotic arm applications. The contact was operated by the texture analyzer to simulate the fruit grasping process by a robotic arm. Utilizing deep neural networks and machine learning-based techniques to process high-precision geometric images collected by an internal camera, the visuo-tactile sensor achieved non-destructive measurements of peach firmness and contact force. For firmness measurement in the test set, the sensor achieved coefficient of determination (R2) of 0.878 and a root mean square error (RMSE) of 0.732. For contact force detection, the R2 was 0.942, and RMSE was 1.115 in the test set. The results showed visuo-tactile sensor was feasible for non-destructive detection of peach firmness and contact force, and has a broad application prospect in the field of agricultural robotics.

Effect of Sodium Selenite Foliar Treatment on the Nutritional Quality of Cyclocarya paliurus (Batalin) Iljinsk

Cyclocarya paliurus (Batalin) Iljinskaja is a rare, endemic tree species in China. The leaves of C. paliurus contain a high concentration of biologically active compounds, and they are often used to make herbal tea. Exogenous selenium (Se) enrichment can promote the levels of active ingredients in C. paliurus. To explore the effects of exogenous Se enrichment, this study applied different concentrations of sodium selenite solution on the leaves (0, 40, 80, 120, 160, and 300 mg/L) and measured the changes in Se, secondary metabolites, soluble sugar, soluble protein, starch, and trace elements in the leaves at different treatment times. The results showed that with the increase in the concentration of sodium selenite spray, the selenium content in the leaves of C. paliurus gradually increased, and high selenium concentrations could cause seedling burning to the leaf surface (>160 mg/kg). When the selenium treatment concentration was 80 mg/kg, the selenium content reached the maximum value of the selenium-rich tea standard (4 mg/kg). The content of polyphenols and most elements reached their maxima under the treatment of 40 mg/L Na2SeO3, except for Mg and Mn, which peaked at 300 mg/L. The content of soluble sugars (38.1 mg/g) and starch reached their maxima under relatively high selenium treatment concentrations, whereas soluble protein (10.63 mg/g) exhibited its maximum value in the control group. The optimal harvest time was mainly concentrated in the 10–20 day treatment period. The objective of this research is to investigate the effects of exogenous selenium application on the nutritional quality of C. paliurus leaves. The findings will provide guidance for the effective cultivation of selenium-enriched Poria cocos as a medicinal and health-promoting product.

Rating pome fruit quality traits using deep learning and image processing.

Quality assessment of pome fruits (i.e. apples and pears) is used not only for determining the optimal harvest time but also for the progression of fruit-quality attributes during storage. Therefore, it is typical to repeatedly evaluate fruits during the course of a postharvest experiment. This evaluation often includes careful visual assessments of fruit for apparent defects and physiological symptoms. A general best practice for quality assessment is to rate fruit using the same individual rater or group of individual raters to reduce bias. However, such consistency across labs, facilities, and experiments is often not feasible or attainable. Moreover, while these visual assessments are critical empirical data, they are often coarse-grained and lack consistent objective criteria. Granny, is a tool designed for rating fruit using machine-learning and image-processing to address rater bias and improve resolution. Additionally, Granny supports backward compatibility by providing ratings compatible with long-established standards and references, promoting research program continuity. Current Granny ratings include starch content assessment, rating levels of peel defects, and peel color analyses. Integrative analyses enhanced by Granny's improved resolution and reduced bias, such as linking fruit outcomes to global scale -omics data, environmental changes, and other quantitative fruit quality metrics like soluble solids content and flesh firmness, will further enrich our understanding of fruit quality dynamics. Lastly, Granny is open-source and freely available.

The Effect of Harvest Time of Sapu-Sapu Leaves (Baeckea frutescens L.) the Yield, Characteristics and Composition of Essential Oils Extached Using Steam-Hydro Distillation Method

The sapu-sapu plant (Baeckea frutescens L.) is widespread in the Bangka Belitung Islands Province, thriving in sandy areas such as beaches and highlands with less fertile soil. This study aimed to determine the optimal harvest time for sapu-sapu leaves to obtain essential oils' highest yield and most desirable characteristics. The research focused on the duck-type sapu-sapu leaves, using five variations of harvest time (coded as A1, A2, A3, A4, and A5, representing leaves harvested at 1, 2, 3, 4, and 5 months of growth, respectively). Essential oil extraction was performed using the steam distillation method. The resulting oils were then analyzed using Gas Chromatography-Mass Spectrometry (GC-MS) to determine their chemical composition. The results showed that sample A1 (1-month growth) produced the highest essential oil yield at 0.74% (w/w). This sample also exhibited the most optimal essential oil characteristics: clear yellow color, characteristic sapu-sapu odor, warm bitter taste, solubility in 96% ethanol at 1:13, specific gravity of 0.8863 g/mL, optical rotation of (+) 10.28°, and refractive index of 1.474. GC-MS analysis revealed that the main compounds in the A1 sample were α-pinene (43.84%), β-pinene (13.56%), and 1,8-cineol (24.26%). The study concluded that while the variation in harvest time of sapu-sapu leaves did not significantly affect the yield, characteristics, or composition of the essential oils, there were slight differences in yield and color. Sample A1, representing the youngest leaves, produced the most optimal results.

Antioxidant and Whitening Effects of Dahlia based on Variety, Flowering Form, Harvest Time, and Plant Part

Purpose: This study evaluated the antioxidant activity of dahlias based on variety, flowering form, harvest time, and plant parts, analyzed the content of bioactive compounds, and assessed the whitening effect of different plant parts to explore potential applications in the food, pharmaceutical, and cosmetic industries.Methods: Antioxidant activity was assessed using DPPH radical scavenging assays, whereas bioactive compounds were quantified through total polyphenol content (TPC) and total flavonoid content (TFC) measurements. The whitening effect was evaluated based on tyrosinase inhibition rates.Results: Among the six dahlia varieties examined, <i>D. diva</i> demonstrated the highest antioxidant activity, with purple and deep red varieties, showing particularly high levels of activity and polyphenol content. Analysis of flowering forms indicated that flowers in the early half-blooming stage exhibited the highest antioxidant activity. Regarding harvest time, dahlias harvested during the third period in the fall, showed the greatest antioxidant activity. Examination of different plant parts revealed that petals had the highest antioxidant activity, whereas leaves and tubers had comparatively lower activity. Additionally, petals exhibited the highest tyrosinase inhibition rate among the plant parts, although the overall whitening effect was minimal.Conclusion: The study identified the optimal dahlia variety, flowering form, harvest time, and plant parts for maximizing antioxidant effects. These findings provide valuable insights for potential applications of dahlias in various industries.

Transcriptome and Metabolome Based Mechanisms Revealing the Accumulation and Transformation of Sugars and Fats in Pinus armandii Seed Kernels during the Harvesting Period.

Pinus armandii seed kernel is a nutrient-rich and widely consumed nut whose yield and quality are affected by, among other things, harvesting time and climatic conditions, which reduce economic benefits. To investigate the optimal harvesting period of P. armandii seed kernels, this study determined the nutrient composition and seed kernel morphology and analyzed the gene expression and metabolic differences of P. armandii seed kernels during the harvesting period by transcriptomics and metabolomics. The results revealed that during the maturation of P. armandii seed kernels, there was a significant increase in the width, thickness, and weight of the seed kernels, as well as a significant accumulation of sucrose, soluble sugars, proteins, starch, flavonoids, and polyphenols and a significant decrease in lipid content. In addition, transcriptomic and metabolomic analyses of P. armandii seed kernels during the harvesting period screened and identified 103 differential metabolites (DEMs) and 8899 differential genes (DEGs). Analysis of these DEMs and DEGs revealed that P. armandii seed kernel harvesting exhibited gene-metabolite differences in sugar- and lipid-related pathways. Among them, starch and sucrose metabolism, glycolysis, and gluconeogenesis were associated with the synthesis and catabolism of sugars, whereas fatty acid degradation, glyoxylate and dicarboxylic acid metabolism, and glycerophospholipid metabolism were associated with the synthesis and catabolism of lipids. Therefore, the present study hypothesized that these differences in genes and metabolites exhibited during the harvesting period of P. armandii seed kernels might be related to the accumulation and transformation of sugars and lipids. This study may provide a theoretical basis for determining the optimal harvesting time of P. armandii seed kernels, changes in the molecular mechanisms of nutrient accumulation, and quality directed breeding.

Integrated nutritional and functional components analyses reveal insights into the peel and pulp quality at different harvest times of ‘Dahongpao’ tangerine (Citrus reticulata Blanco)

The fruit of Citrus reticulata ‘Dahongpao’ (DHP) is typically harvested when fully ripe, exhibiting a dark red color, high sweetness, and pleasant taste. However, it remains uncertain whether the optimum harvesting time for its medicinal part (peel) aligns with that of the fruit. The findings of the study indicated that the peel exhibited the highest concentration of total flavonoids (4.018 mg/g) during the middle stage of maturity. Additionally, the total polysaccharide content increased progressively with ripening, reaching its peak (5.36 %) at full maturity. Furthermore, the DHP pulp demonstrated the highest concentration of total polyphenols (11.5 %) and the lowest titrable acid content (0.97 %) during the middle stage of maturity. Furthermore, the peel and pulp of DHP at the middle stage of ripening exhibited the highest antioxidant capacity. Considering the nutritional and functional components at various harvest times of DHP, it is recommended to harvest the peel at the intermediate stage of ripeness. Additionally, during this stage, the pulp also exhibited greater abundance of nutritional components. The findings of this study elucidate the process of accumulation and alteration of nutritional and functional constituents during the ripening of DHP fruit.

Research Progress and Biosynthetic Mechanisms of Nutritional Compounds Obtained from Various Organs During the Developmental Stages of a Medicinal Plant (Chinese Jujube).

The fruit of the jujube tree is high in nutrients and has various health benefits. China is a major producer of jujube, and it is now cultivated all around the world. Numerous studies have demonstrated the nutritional value and potential health advantages of bioactive compounds found in the jujube tree. Furthermore, the jujube tree has a remarkable 7000-year agricultural history. The jujube plant has developed a rich gene pool, making it a valuable resource for germplasm. Different studies have focused on the developmental stages of jujube fruits to identify the optimal time for harvest and to assess the changes in their bioactive natural compounds or products during the process of development but the molecular mechanism underlying the production of bioactive natural products in Z. jujuba is still poorly understood. Moreover, the potential differential expressed genes (DEGs) identified as responsible for the synthesis of these compounds should be further functionally verified. It has been noticed that the contents of total flavonoids, total phenolic, and vitamin C increase significantly during the ripening process, while the contents of soluble sugars and organic acids decrease gradually. In this review, we have also scrutinized the challenges that hinder the utilization of jujube fruit resources and suggested potential areas for further research. As such, our review serves as a valuable resource for the future development of jujube-based nutritional compounds and the incorporation of their nutritional elements into the functional foods industry.

Multi-omics strategy to reveal the numbing taste of Zanthoxylum armatum DC. at different developmental stages

Green Sichuan pepper (Zanthoxylum armatum DC.) holds important development and utilization values in the food and pharmaceutical industries due to the special pungent and numbing taste of their fruits. Interactions among genes, metabolites and proteins are activated at the molecular level during the development of Z. armatum fruits. In this study, five key stages of Z. armatum development were characterized using the transcriptome, metabolome and proteome, and a comprehensive analysis of the numbing taste was performed. Transcriptome enrichment analyses showed that the differentially expressed genes were mainly associated with flavonoid biosynthesis and tyrosine metabolism. The results also revealed that there were significant differences in the accumulation of metabolites such as fatty acids, tyrosine metabolism, terpenoids, and alkaloids in Z. armatum at different developmental stages. Proteome enrichment analyses indicated that these proteins were mainly involved in flavonoid biosynthesis, and terpenoid backbone biosynthesis. Integrated analysis of transcriptome, metabolome and proteome reveals that different developmental stages of Z. armatum were associated with flavonoid biosynthesis, fatty acids and terpenoid biosynthesis pathways, contributing to its numbing taste and medicinal value. These findings provide novel insights into the numbing taste of Z. armatum, and are expected to inform optimal harvesting times for enhanced economic benefits.

Aggregation and assessment of grape quality parameters with visible-near-infrared spectroscopy: Introducing a novel quantitative index

Grape quality is generally assessed by measuring individual indicators rather than utilizing a comprehensive metric. A flexible approach to aggregating multiple quality-related parameters of grapes is introduced in this study, and an aggregative index was proposed for the synthetical evaluation of grape quality. First, visible-near-infrared (Vis-NIR) spectral data of two grape varieties were obtained, and the physical and chemical quality indicators were measured as reference standards. The variation trends and correlations of the indicators were analyzed, and an aggregative quality indicator (AQI) was proposed using the min-max normalization and factor analysis methods. The maturity stages of grapes were categorized by analyzing the AQI values. The spectral data underwent preprocessing using multiplicative scatter correction (MSC), Savitzky-Golay smoothing (SG), first-order derivative (FD), SG+FD and de-trending (DT) methods. Feature wavelengths were extracted using uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and the UVE-CARS algorithms. Three regression models for the AQI were established based on partial least squares regression (PLSR), support vector machine regression (SVR) and convolutional neural network (CNN). Results indicated that the PLSR combined with the FD+SG preprocessing and CARS-selected feature wavelengths achieved optimal performance for Cabernet Sauvignon grapes, with the coefficient of determination for the prediction set (Rp2)=0.972, for the calibration set (Rc2)=0.995, the root mean square error of prediction (RMSEP)=0.040 and residual predictive deviation (RPD)=6.064. In addition, for the Muscat Kyoho grape, CNN combined with DT preprocessed full-spectrum data yielded the best prediction for the AQI, with Rp2=0.989, Rc2=0.981, RMSEP=0.103 and RPD=9.482. This research demonstrated that the non-destructive prediction of grape aggregative qualities can be achieved through the combination of Vis-NIR spectroscopy and a chemometric analysis, offering a practical and comprehensive metric for determining grape maturity stages and optimal harvest times. This approach is particularly valuable for the wine industry, as it facilitates better decision-making and quality control.

Changes in growth characteristics and ginsenoside contents of wild-simulated ginseng with different harvest times in South Korea

Wild-simulated ginseng (WSG, Panax ginseng C.A. Meyer) is grown in mountainous forests, without the chemical treatment or installation of artificial facilities. This study aimed to investigate monthly changes in growth characteristics and ginsenoside contents in WSG to suggest the optimal harvest time. Four-year-old WSG were collected in the same area every month, and their growth characteristics and ginsenoside contents were measured. The growth characteristics of aerial and root parts were measured from May to July and from March to December, respectively. For the aerial part, most growth characteristics of WSG decreased over time, except for stem length. For the root part, rhizome length increased over time except for September, while the root diameter and weight of root part were mostly consistent. The root length increased by September, while the number of rootlets was the highest at May. At July, the total ginsenoside content of WSG was significantly the highest, while the total ginsenoside content at October was the lowest. This result was believed to be due to the F2, Rd, and Rg1 contents of the aerial part, rather than the root part. Also, based on these growths and the ginsenoside contents of WSG, the optimal harvest time for WSG is considered to be late spring–summer (May–July) when the aerial part can be identified.

Nutritive value of indian dwarf wheat ears as feed

Interest in growing ancient wheat species and studying the possibility of their use as raw material for feed preparation has increased recently. Indian dwarf wheat (Triticum sphaerococcum Percival) is one of the promising grain crops. The aim of the study was to determine the nutritive value of ears of Indian dwarf wheat in different phases of ripeness for use as raw material in the production of feed in comparison with soft wheat and to determine the rational time frame of their harvesting for feed preparation. Harvesting was carried out by combing without threshing and separation of grain heap in different phases of maturity. The feed was prepared from the grain heap. Chemical analyses of feed samples were performed according to standard methods. The mid-wax ripeness phase is the optimum harvesting time of Indian dwarf wheat for preparation of feed from its ears. Its ears in this phase contain maximum essential amino acids and minimum cellulose. Feed prepared from ears of Indian dwarf wheat has a better quality than feed from soft wheat, as it contains 1.06–2.23 % more of the sum of essential amino acids and 1–5 % more of individual amino acids. This allows the crop to be used in feed preparation to improve protein quality. But its 4–10 % more higher cellulose content than feed from soft wheat is a disadvantage. Grain heap (ears) of Indian dwarf wheat, harvested without threshing in the phase of early wax and mid-wax ripeness, can be a source of increasing the nutritive value of feeds, as it contains more essential amino acids than soft wheat.

Evaluation of Pre-Harvest Nutrient Composition and Functional Active Substances in Various Lotus Roots.

This study investigated the impact of variety and harvest time on the visual appearance, nutritional quality, and functional active substances of six lotus root cultivars: "Xinsanwu", "Wuzhi No. 2", "Baiyuzhan", "Huaqilian", "Elian No. 6", and "Elian No. 5". Samples were collected monthly from December 2023 to April 2024. A nutrient analysis revealed a decrease in the water content with a delayed harvest. The total soluble solids and soluble sugar content peaked towards the end and middle-to-late harvest periods, respectively. Starch levels initially increased before declining, while the soluble protein exhibited a triphasic trend with an initial rise, a dip, and a final increase. The vitamin C (Vc) content varied across cultivars. Functional active substances displayed dynamic changes. The total phenolics initially decreased, then increased, before ultimately declining again. The total flavonoid content varied by both cultivar and harvest time. The phenolic acid and flavonoid content mirrored the trends observed for total phenolics and total flavonoids. Gastrodin was the most abundant non-flavonoid compound across all varieties. "Wuzhi No. 2" and "Baiyuzhan" displayed higher levels of functional active substances and starch, while the Elian series and "Xinsanwu" cultivar exhibited a greater content of Vc, soluble sugar, and soluble protein. Specific harvest periods yielded optimal results: "Wuzhi No. 2" (H1 and H5), "Huaqilian" (H2), and "Baiyuzhan" (H3 and H4) demonstrated a high nutrient and functional active substance content. Overall, the lotus roots harvested in period H4 achieved the highest score. Overall, this study provides the foothold for the rapid identification of superior lotus root cultivars and the valorization of lotus root by-products via advanced processing methods. Additionally, it offers valuable insights for market participants and consumers to select optimal varieties and harvest times based on their specific needs.