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Tworzenie połączeń w celu uwolnienia potencjału innowacyjnego w zakresie cyfrowej transformacji europejskiego sektora rolno-spożywczego
Somaclonal Variation for Genetic Improvement of Starch Accumulation in Potato (Solanum tuberosum) Tubers
Starch content is one of the major quality criteria targeted by potato breeding programs. Traditional potato breeding is a laborious duty due to the tetraploid nature and immense heterozygosity of potato genomes. In addition, screening for functional genetic variations in wild relatives is slow and strenuous. Moreover, genetic diversity, which is the raw material for breeding programs, is limited due to vegetative propagation used in the potato industry. Somaclonal variation provides a time-efficient tool to breeders for obtaining genetic variability, which is essential for breeding programs, at a reasonable cost and independent of sophisticated technology. The present investigation aimed to create potato somaclones with an improved potential for starch accumulation. Based on the weight and starch content of tubers, the somaclonal variant Ros 119, among 105 callus-sourced clones, recorded a higher tuberization potential than the parent cv Lady Rosetta in a field experiment. Although this somaclone was similar to the parent in the number of tubers produced, it exhibited tubers with 42 and 61% higher fresh and dry weights, respectively. Additionally, this clone recorded 10 and 75% increases in starch content based on the dry weight and average content per plant, respectively. The enhanced starch accumulation was associated with the upregulation of six starch-synthesis-related genes, namely, the AGPase, GBSS I, SBE I, SBE II, SS II and SS III genes. AGPase affords the glycosyl moieties required for the synthesis of amylose and amylopectin. GBSS is required for amylose elongation, while SBE I, SBE II, SS II and SS III are responsible for amylopectin.
Prediction of Blueberry (Vaccinium corymbosum L.) Yield Based on Artificial Intelligence Methods
In this paper, we present a high-accuracy model for blueberry yield prediction, trained using structurally innovative data sets. Blueberries are blooming plants, valued for their antioxidant and anti-inflammatory properties. Yield on the plantations depends on several factors, both internal and external. Predicting the accurate amount of harvest is an important aspect in work planning and storage space selection. Machine learning algorithms are commonly used in such prediction tasks, since they are capable of finding correlations between various factors at play. Overall data were collected from years 2016–2021, and included agronomic, climatic and soil data as well satellite-imaging vegetation data. Additionally, growing periods according to BBCH scale and aggregates were taken into account. After extensive data preprocessing and obtaining cumulative features, a total of 11 models were trained and evaluated. Chosen classifiers were selected from state-of-the-art methods in similar applications. To evaluate the results, Mean Absolute Percentage Error was chosen. It is superior to alternatives, since it takes into account absolute values, negating the risk that opposite variables will cancel out, while the final result outlines percentage difference between the actual value and prediction. Regarding the research presented, the best performing solution proved to be Extreme Gradient Boosting algorithm, with MAPE value equal to 12.48%. This result meets the requirements of practical applications, with sufficient accuracy to improve the overall yield management process. Due to the nature of machine learning methodology, the presented solution can be further improved with annually collected data.
Correction: Helal et al. Improving Yield Components and Desirable Eating Quality of Two Wheat Genotypes Using Si and NanoSi Particles under Heat Stress. Plants 2022, 11, 1819
In the original publication [...]
Evaluation of Multiple Linear Regression and Machine Learning Approaches to Predict Soil Compaction and Shear Stress Based on Electrical Parameters
This study investigated the relationships between the electrical and selected mechanical properties of soil. The analyses focused on comparing various modeling relationships under study methods that included machine learning methods. The input parameters of the models were apparent soil electrical conductivity and magnetic susceptibility measured at depths of 0.5 m and 1 m. Based on the models, shear stress and soil compaction were predicted. Neural network models outperformed support vector machines and multiple linear regression techniques. Exceptional models were developed using a multilayer perceptron neural network for shear stress (R = 0.680) and a function neural network for soil compaction measured at a depth of 0–0.5 m and 0.4–0.5 m (R = 0.812 and R = 0.846, respectively). Models of very low accuracy (R < 0.5) were produced by the multiple linear regression.
Combining Machine Learning Algorithms with Earth Observations for Crop Monitoring and Management
Combining machine learning algorithms with Earth observations has great potential in the context of crop monitoring and management, which is essential in the face of global challenges related to food security and climate change [...]
GT Biplot and Cluster Analysis of Barley (Hordeum vulgare L.) Germplasm from Various Geographical Regions Based on Agro-Morphological Traits
Barley, an ancient crop, was vital for early civilizations and has historically been served as food and beverage. Today, it plays a major role as feed for livestock. Breeding modern barley varieties for high yield and quality has created significant genetic erosion. This highlights the importance of tapping into genetic and genomic resources to develop new improved varieties that can overcome agricultural bottlenecks and increase barley yield. In the current study, 75 barley genotypes were evaluated for agro-morphological traits. The relationships among these traits were determined based on genotype by trait (GT) biplot analysis for two cropping years (2021 and 2022). This study was designed as a randomized complete block experiment with four replications. The variation among genotypes was found to be significant for all traits. The correlation coefficient and GT biplot revealed that grain yield (GY) was positively correlated with the number of grains per spike (NGS), the grain weight per spike (GW), and the thousand kernel weight (1000 KW). However, the test weight (TW) was negatively correlated with the heading date (HD). Hierarchical analysis produced five groups in the first year, four groups in the second year, and four groups over the average of two years. Genotypes by trait biplot analysis highlighted G25, G28, G61, G73, and G74 as promising high-yielding barley genotypes. This study demonstrated the effectiveness of the GT biplot as a valuable approach for identifying superior genotypes with contrasting traits. It is considered that this approach could be used to evaluate the barley genetic material in breeding programs.
Modeling Callus Induction and Regeneration in Hypocotyl Explant of Fodder Pea (Pisum sativum var. arvense L.) Using Machine Learning Algorithm Method
A comprehensive understanding of genetic diversity and the categorization of germplasm is important to effectively identify appropriate parental candidates for the goal of breeding. It is necessary to have a technique of tissue culture that is both effective and reproducible to perform genetic engineering on fodder pea genotypes (Pisum sativum var. arvense L.). In this investigation, the genetic diversity of forty-two fodder pea genotypes was assessed based on their ability of callus induction (CI), the percentage of embryogenic callus by explant number (ECNEP), the percentage of responding embryogenic calluses by explant number (RECNEP), the number of somatic embryogenesis (NSE), the number of responding somatic embryogenesis (RSE), the regeneration efficiency (RE), and the number of regenerated plantlets (NRP). The findings of the ANOVA showed that there were significant differences (p < 0.001) between the genotypes for all in vitro parameters. The method of principal component analysis (PCA) was used to study the correlations that exist between the factors associated with tissue culture. While RE and NRP variables were most strongly associated with Doğruyol, Ovaçevirme-4, Doşeli-1, Yolgeçmez, and Incili-3 genotypes, RECNEP, NSE, RDE, and RECNEP variables were strongly associated with Avcılar, Ovaçevirme-3, and Ardahan Merkez-2 genotypes. The in vitro process is a complex multivariate process and more robust analyses are needed for linear and nonlinear parameters. Within the scope of this study, artificial neural network (ANN), random forest (RF), and multivariate adaptive regression spline (MARS) algorithms were used for RE estimation, and these algorithms were also compared. The results that we acquired from our research led us to the conclusion that the employed ANN-multilayer perceptron (ANN-MLP) model (R2 = 0.941) performs better than the RF model (R2 = 0.754) and the MARS model (R2 = 0.214). Despite this, it has been shown that the RF model is capable of accurately predicting RE in the early stages of the in vitro process. The current work is an inquiry regarding the use of RF, MARS, and ANN models in plant tissue culture, and it indicates the possibilities of application in a variety of economically important fodder peas.
New Developments in Smart Farming Applied in Sustainable Agriculture
Sustainable agriculture aims to increase agricultural productivity while minimising negative environmental impacts [...]
Genetic Diversity and Population Structure in Bread Wheat Germplasm from Türkiye Using iPBS-Retrotransposons-Based Markers
This study investigated the genetic diversity and population structure of 63 genotypes from Turkish bread wheat germplasm using iPBS-retrotransposons primers. The thirty-four iPBS primers produced a total of 1231 polymorphic bands, ranging from 8 (iPBS-2375) to 60 (iPBS-2381) alleles per marker, with an average number of 36.00 alleles. The polymorphism information content (PIC) per marker varied between 0.048 (iPBS 2087) and 0.303 (iPBS 2382), with an average of 0.175. The numbers of effective alleles (ne), genetic diversity of Nei (h), and Shannon’s information index (I) value were calculated as 1.157, 0.95, and 0.144, respectively. The greatest genetic distance (0.164) was between Eastern Anatolia Agricultural Research Institute genotypes and Çukurova Agricultural Research Institute genotypes. The unweighted pair-group method with arithmetic mean (UPGMA) dendrogram placed the 63 wheat genotypes into three clusters. The percentage of genetic diversity explained by each of the three main coordinates of the basic coordinate analysis was determined to be 44.58, 12.08, and 3.44, respectively. AMOVA (Analysis of Molecular Variance) showed that the variation within populations was 99% and that between populations was 1%. The result of genetic structure analysis suggests that the greatest value of K was calculated as 3. The F-statistic (Fst) value was determined as 0.4005, 0.2374, and 0.3773 in the first to third subpopulations, respectively. Likewise, the expected heterozygosity values (He) were determined as 0.2203, 0.2599, and 0.2155 in the first, second, and third subpopulations, respectively. According to the information obtained in the study, the most genetically distant genotypes were the G1 (Aksel 2000) and G63 (Karasu 90) genotypes. This study provided a deep insight into genetic variations in Turkish bread wheat germplasm using the iPBS-retrotransposons marker system.
Integrative approaches to enhance reproductive resilience of crops for climate-proof agriculture
Exploring Digital Innovations in Agriculture: A Pathway to Sustainable Food Production and Resource Management
Today’s agriculture faces numerous challenges due to climate change, a growing population and the need to increase food productivity [...]
Insights into Drought Tolerance of Tetraploid Wheat Genotypes in the Germination Stage Using Machine Learning Algorithms
Throughout germination, which represents the initial and crucial phase of the wheat life cycle, the plant is notably susceptible to the adverse effects of drought. The identification and selection of genotypes exhibiting heightened drought tolerance stand as pivotal strategies aimed at mitigating these effects. For the stated objective, this study sought to evaluate the responses of distinct wheat genotypes to diverse levels of drought stress encountered during the germination stage. The induction of drought stress was achieved using polyethylene glycol at varying concentrations, and the assessment was conducted through the application of multivariate analysis and machine learning algorithms. Statistical significance (p < 0.01) was observed in the differences among genotypes, stress levels, and their interaction. The ranking of genotypes based on tolerance indicators was evident through a principal component analysis and biplot graphs utilizing germination traits and stress tolerance indices. The drought responses of wheat genotypes were modeled using germination data. Predictions were then generated using four distinct machine learning techniques. An evaluation based on R-square, mean square error, and mean absolute deviation metrics indicated the superior performance of the elastic-net model in estimating germination speed, germination power, and water absorption capacity. Additionally, in assessing the criterion metrics, it was determined that the Gaussian processes classifier exhibited a better performance in estimating root length, while the extreme gradient boosting model demonstrated superior performance in estimating shoot length, fresh weight, and dry weight. The study’s findings underscore that drought tolerance, susceptibility levels, and parameter estimation for durum wheat and similar plants can be reliably and efficiently determined through the applied methods and analyses, offering a fast and cost-effective approach.
A Framework for Financing Post-Registration Variety Testing System: A Case Study from Poland
Agriculture is essential to ensuring food security and prosperity around the world. The importance of cultivating agricultural plant species cannot be overestimated. One of the key challenges faced by modern food producers is to increase efficiency while ensuring sustainability and improving resilience to unfavorable environmental conditions brought about by ongoing climate change. To meet these challenges, it is vital to continue breeding work and to select plant varieties best adapted to local farming conditions. Undoubtedly, future yield increases will only be achievable by way of genetic improvement. In turn, crop-variety recommendations should rely on the results of properly designed post-registration variety testing (PRVT, in polish PDO), followed up by specific variety recommendations for growers. In this article, we attempt to fill a gap in the international literature regarding post-registration variety testing. We present PRVT as a unique scheme that is key to selecting agricultural plant varieties recommended for cultivation, with due account taken of Poland’s specific farming conditions. Every year, over 1000 field cultivar tests are carried out as part of PRVT. The results of these tests constitute reliable, objective source material for farmers and help them make choices regarding the most valuable varieties for cultivation that are also best adapted to local farming conditions. Among the financial benefits of selecting the right crop varieties for agriculture are lower cultivation costs, including reduced fertilizer and pesticide spending, and higher income generated by larger yields.
Predictions and Estimations in Agricultural Production under a Changing Climate
In the 21st century, agriculture is facing numerous challenges [...]
Digital Repeat Photography Application for Flowering Stage Classification of Selected Woody Plants
Digital repeat photography is currently applied mainly in geophysical studies of ecosystems. However, its role as a tool that can be utilized in conventional phenology, tracking a plant’s seasonal developmental cycle, is growing. This study’s main goal was to develop an easy-to-reproduce, single-camera-based novel approach to determine the flowering phases of 12 woody plants of various deciduous species. Field observations served as binary class calibration datasets (flowering and non-flowering stages). All the image RGB parameters, designated for each plant separately, were used as plant features for the models’ parametrization. The training data were subjected to various transformations to achieve the best classifications using the weighted k-nearest neighbors algorithm. The developed models enabled the flowering classifications at the 0, 1, 2, 3, and 5 onset day shift (absolute values) for 2, 3, 3, 2, and 2 plants, respectively. For 9 plants, the presented method enabled the flowering duration estimation, which is a valuable yet rarely used parameter in conventional phenological studies. We found the presented method suitable for various plants, despite their petal color and flower size, until there is a considerable change in the crown color during the flowering stage.
Prediction of Potato (Solanum tuberosum L.) Yield Based on Machine Learning Methods
This research delves into the application of machine learning methods for predicting the yield of potato varieties used for French fries in Poland. By integrating a comprehensive dataset comprising agronomical, climatic, soil, and satellite-based vegetation data from 36 commercial potato fields over five growing seasons (2018–2022), we developed three distinct models: non-satellite, satellite, and hybrid. The non-satellite model, relying on 85 features, excludes vegetation indices, whereas the satellite model includes these indices within its 128 features. The hybrid model, combining all available features, encompasses a total of 165 features, presenting the most-comprehensive approach. Our findings revealed that the hybrid model, particularly when enhanced with SVM outlier detection, exhibited superior performance with the lowest Mean Absolute Percentage Error (MAPE) of 5.85%, underscoring the effectiveness of integrating diverse data sources into agricultural yield prediction. In contrast, the non-satellite and satellite models displayed higher MAPE values, indicating less accuracy compared to the hybrid model. Advanced data-processing techniques such as PCA and outlier detection methods (LOF and One-Class SVM) played a pivotal role in model performance, optimising feature selection and dataset refinement. The study concluded that machine learning methods, particularly when leveraging a multifaceted approach involving a wide array of data sources and advanced processing techniques, can significantly enhance the accuracy of agricultural yield predictions. These insights pave the way for more-efficient and -informed agricultural practices, emphasising the potential of machine learning in revolutionising yield prediction and crop management.
Improving Yield Components and Desirable Eating Quality of Two Wheat Genotypes Using Si and NanoSi Particles under Heat Stress
Global climate change is a significant challenge that will significantly lower crop yield and staple grain quality. The present investigation was conducted to assess the effects of the foliar application of either Si (1.5 mM) or Si nanoparticles (1.66 mM) on the yield and grain quality attributes of two wheat genotypes (Triticum aestivum L.), cv. Shandweel 1 and cv. Gemmeiza 9, planted at normal sowing date and late sowing date (heat stress). Si and Si nanoparticles markedly mitigated the observed decline in yield and reduced the heat stress intensity index value at late sowing dates, and improved yield quality via the decreased level of protein, particularly glutenin, as well as the lowered activity of α-amylase in wheat grains, which is considered a step in improving grain quality. Moreover, Si and nanoSi significantly increased the oil absorption capacity (OAC) of the flour of stressed wheat grains. In addition, both silicon and nanosilicon provoked an increase in cellulose, pectin, total phenols, flavonoid, oxalic acid, total antioxidant power, starch and soluble protein contents, as well as Ca and K levels, in heat-stressed wheat straw, concomitant with a decrease in lignin and phytic acid contents. In conclusion, the pronounced positive effects associated with improving yield quantity and quality were observed in stressed Si-treated wheat compared with Si nanoparticle-treated ones, particularly in cv. Gemmeiza 9.
Estimating Energy Consumption During Soil Cultivation Using Geophysical Scanning and Machine Learning Methods
The agricultural sector is one of the most significant sectors of the global economy, yet it is concurrently a highly energy-intensive industry. The issue of optimizing field operations in terms of energy consumption is therefore a key consideration for sustainable agriculture, and the solution to this issue leads to both environmental and financial benefits. The aim of this study was to estimate energy consumption during soil cultivation using geophysical scanning data and machine learning (ML) algorithms. This included determining the optimal set of independent variables and the most suitable ML method. Soil parameters such as electrical conductivity, magnetic susceptibility, and soil reflectance in infrared spectra were mapped using data from Geonics EM-38 and Veris 3100 scanners. These data, along with soil texture, served as inputs for predicting fuel consumption and field productivity. Three machine learning algorithms were tested: support vector machines (SVMs), multilayer perceptron (MLP), and radial basis function (RBF) neural networks. Among these, SVM achieved the best performance, showing a MAPE of 4% and a strong correlation (R = 0.97) between predicted and actual productivity values. For fuel consumption, the optimal method was MLP (MAPE = 4% and R = 0.63). The findings demonstrate the viability of geophysical scanning and machine learning for accurately predicting energy use in tillage operations. This approach supports more sustainable agriculture by enabling optimized fuel use and reducing environmental impact through data-driven field management. Further research is needed to obtain training data for different soil parameters and agrotechnical treatments in order to develop more universal models.
Genotype-Trait (GT) Biplot Analysis for Yield and Quality Stability in Some Sweet Corn (Zea mays L. saccharata Sturt.) Genotypes
A strong statistical method for investigating the correlations between traits, assessing genotypes based on numerous traits, and finding individuals who excel in particular traits is genotype–trait (GT) biplot analysis. The current study was applied to evaluate 11 sweet corn (Zea mays L. saccharata) genotypes and correlate them based on genotype–trait (GT) biplot analysis for two cropping seasons in Erzurum, Türkiye using the RCBD experimental design with three reputations. The results showed that the genotypes were significantly different for the majority of the examined variables according to the combined analysis of variance findings at 0.01 probability level. An ecological analysis was performed to evaluate sweet corn varieties and environmental conditions and interactions between them (genotype × environmental conditions). Our results showed that the summation of the first two and second main components was responsible for 73.51% of the combined cropping years of the sweet corn growth and development variance, demonstrating the biplot graph’s optimum relative validity, which was obtained. In this study, the Khan F1 (G6) genotype was found to be the stablest genotype, and the Kompozit Seker (G7) genotype was the non-stable genotype, moreover based on the first cropping year, second cropping year, and the average mean of the two cropping years. As a conclusion, the Khan F1 (G6) genotype is the highest-yielding genotype, and the Kompozit Seker (G7) is the lowest. Based on the heat map dendrogram, the context of the differential extent of trait association of all genotypes into two clusters is indicated. The highest genetic distance was shown between the BATEM Tatlı (G3) and Febris (G5) genotypes. Our results provide helpful information about the sweet corn genotypes and environments for future breeding programs.
