Real Field Conditions Research Articles

Plant Disease Detection and Classification Using Deep Learning Methods: A Comparison Study

The presence issue of inaccurate plant disease detection persists under real field conditions and most deep learning (DL) techniques still struggle to achieve real-time performance. Hence, challenges in choosing a suitable deep-learning technique to tackle the problem should be addressed. Plant diseases have a detrimental effect on agricultural yield, hence early detection is crucial to prevent food insecurity. To identify and categorise the indications of plant diseases, numerous developed or modified DL architectures are utilised. This paper aims to observe the performance of the YOLOv8 model, which has better performance than its predecessors, on a small-scale plant disease dataset. This paper also aims to improve the accuracy and efficiency of plant disease detection and classification methods by proposing an optimised and lightweight YOLOv8 architecture model. It trains the YOLOv8 model on a public dataset and optimises the YOLOv8 algorithm with the integration of the GhostNet module into the backbone architecture to cut down the number of parameters for a faster computational algorithm. In addition, the architecture incorporates a Coordinate Attention (CA) mechanism module, which further enhances the accuracy of the proposed algorithm. Our results demonstrate that the combination of YOLOv8s with CA mechanism and transfer learning obtained the best result, yielding score of 72.2% which surpassed the studies that utilised the same dataset. Without transfer learning, our best result is demonstrated by YOLOv8s with GhostNet and CA mechanism yielding a score of 69.3%.

Plant Disease Detection and Classification Using Deep Learning Methods: A Comparison Study

The presence issue of inaccurate plant disease detection persists under real field conditions and most deep learning (DL) techniques still struggle to achieve real-time performance. Hence, challenges in choosing a suitable deep-learning technique to tackle the problem should be addressed. Plant diseases have a detrimental effect on agricultural yield, hence early detection is crucial to prevent food insecurity. To identify and categorise the indications of plant diseases, numerous developed or modified DL architectures are utilised. This paper aims to observe the performance of the YOLOv8 model, which has better performance than its predecessors, on a small-scale plant disease dataset. This paper also aims to improve the accuracy and efficiency of plant disease detection and classification methods by proposing an optimised and lightweight YOLOv8 architecture model. It trains the YOLOv8 model on a public dataset and optimises the YOLOv8 algorithm with the integration of the GhostNet module into the backbone architecture to cut down the number of parameters for a faster computational algorithm. In addition, the architecture incorporates a Coordinate Attention (CA) mechanism module, which further enhances the accuracy of the proposed algorithm. Our results demonstrate that the combination of YOLOv8s with CA mechanism and transfer learning obtained the best result, yielding score of 72.2% which surpassed the studies that utilised the same dataset. Without transfer learning, our best result is demonstrated by YOLOv8s with GhostNet and CA mechanism yielding a score of 69.3%.

An Improved Detection Method for Crop & Fruit Leaf Disease under Real-Field Conditions

Using deep learning-based tools in the field of agriculture for the automatic detection of plant leaf diseases has been in place for many years. However, optimizing their use in the specific background of the agriculture field, in the presence of other leaves and the soil, is still an open challenge. This work presents a deep learning model based on YOLOv6s that incorporates (1) Gaussian error linear unit in the backbone, (2) efficient channel attention in the basic RepBlock, and (3) SCYLLA-Intersection Over Union (SIOU) loss function to improve the detection accuracy of the base model in real-field background conditions. Experiments were carried out on a self-collected dataset containing 3305 real-field images of cotton, wheat, and mango (healthy and diseased) leaves. The results show that the proposed model outperformed many state-of-the-art and recent models, including the base YOLOv6s, in terms of detection accuracy. It was also found that this improvement was achieved without any significant increase in the computational cost. Hence, the proposed model stood out as an effective technique to detect plant leaf diseases in real-field conditions without any increased computational burden.

Analysis of the Impact of Agricultural Production Type and Traffic on Nitrogen Oxide Emissions

The protection of air from pollution is one of the basic principles of sustainable development, and this article analyzes the joint impact of various types of agricultural production and traffic on nitrogen oxide (NOx) emissions. The goal of this research is the development of a new methodology for assessing the combined impact of different types of agricultural production, traffic load, and traffic flow structure on the emission of nitrogen oxides. For the analysis of the joint impact of traffic and agricultural production, a new impact assessment methodology was developed, which is based on two selected impact prediction models. The following two models were used to predict the impact: the BioGrace model and the microsimulation model VISSIM. The developed methodology was applied to a case study in real field conditions. The results showed that the difference in the emission of NOx gases, depending on the type of agricultural production, indicates a reduction in NOx emissions in the walnut–winter-wheat system by 30%, compared to the production of winter wheat in an open field. The statistical analysis showed that there is no statistically significant difference between NOx emissions for different vehicle arrival scenarios for the same traffic load. The results of the joint impact show that the lowest NOx emission was found in walnut orchards and counted traffic. In this example, the difference between the one-hour emission load of three different agricultural types of production and traffic is not large; however, in the cumulative load, it can lead to significant differences. Emphasizing the importance of adopting new agricultural practices and the impact of traffic and the share of freight and heavy goods vehicles in agricultural production, we position them as a proactive and sustainable response to the critical ecological and agricultural challenges highlighted in European and Croatian policy.

Specific ABA-independent tomato transcriptome reprogramming under abiotic stress combination.

Crops often have to face several abiotic stresses simultaneously, and under these conditions, the plant's response significantly differs from that observed under a single stress. However, up to the present, most of the molecular markers identified for increasing plant stress tolerance have been characterized under single abiotic stresses, which explains the unexpected results found when plants are tested under real field conditions. One important regulator of the plant's responses to abiotic stresses is abscisic acid (ABA). The ABA signaling system engages many stress-responsive genes, but many others do not respond to ABA treatments. Thus, the ABA-independent pathway, which is still largely unknown, involves multiple signaling pathways and important molecular components necessary for the plant's adaptation to climate change. In the present study, ABA-deficient tomato mutants (flacca, flc) were subjected to salinity, heat, or their combination. An in-depth RNA-seq analysis revealed that the combination of salinity and heat led to a strong reprogramming of the tomato transcriptome. Thus, of the 685 genes that were specifically regulated under this combination in our flc mutants, 463 genes were regulated by ABA-independent systems. Among these genes, we identified six transcription factors (TFs) that were significantly regulated, belonging to the R2R3-MYB family. A protein-protein interaction network showed that the TFs SlMYB50 and SlMYB86 were directly involved in the upregulation of the flavonol biosynthetic pathway-related genes. One of the most novel findings of the study is the identification of the involvement of some important ABA-independent TFs in the specific plant response to abiotic stress combination. Considering that ABA levels dramatically change in response to environmental factors, the study of ABA-independent genes that are specifically regulated under stress combination may provide a remarkable tool for increasing plant resilience to climate change.

Crop growth dynamics: Fast automatic analysis of LiDAR images in field-plot experiments by specialized software ALFA.

Repeated measurements of crop height to observe plant growth dynamics in real field conditions represent a challenging task. Although there are ways to collect data using sensors on UAV systems, proper data processing and analysis are the key to reliable results. As there is need for specialized software solutions for agricultural research and breeding purposes, we present here a fast algorithm ALFA for the processing of UAV LiDAR derived point-clouds to extract the information on crop height at many individual cereal field-plots at multiple time points. Seven scanning flights were performed over 3 blocks of experimental barley field plots between April and June 2021. Resulting point-clouds were processed by the new algorithm ALFA. The software converts point-cloud data into a digital image and extracts the traits of interest-the median crop height at individual field plots. The entire analysis of 144 field plots of dimension 80 x 33 meters measured at 7 time points (approx. 100 million LiDAR points) takes about 3 minutes at a standard PC. The Root Mean Square Deviation of the software-computed crop height from the manual measurement is 5.7 cm. Logistic growth model is fitted to the measured data by means of nonlinear regression. Three different ways of crop-height data visualization are provided by the software to enable further analysis of the variability in growth parameters. We show that the presented software solution is a fast and reliable tool for automatic extraction of plant height from LiDAR images of individual field-plots. We offer this tool freely to the scientific community for non-commercial use.

Vision based crop row navigation under varying field conditions in arable fields

Accurate crop row detection is often challenged by the varying field conditions present in real-world arable fields. Traditional colour based segmentation is unable to cater for all such variations. The lack of comprehensive datasets in agricultural environments limits the researchers from developing robust segmentation models to detect crop rows. We present a dataset for crop row detection with 11 field variations from sugar beet and maize crops. We also present a novel crop row detection algorithm for visual servoing in crop row fields. The proposed method uses deep learning based crop row skeleton segmentation method followed by a crop row scanning algorithm that identifies the central crop row which the robot then follows. The unique dataset we used with skeleton representations for crop row detection enables robust crop row detection in challenging real world field conditions. Our algorithm can detect crop rows against varying field conditions such as curved crop rows, weed presence, discontinuities, growth stages, tramlines, shadows and light levels. Dense weed presence within inter-row space and discontinuities in crop rows were the most challenging field conditions for our crop row detection algorithm. An End-of Row detector algorithm was developed to detect the end of the crop row and navigate the robot towards the headland area when it reaches the end of the crop row.

Maize plant detection using UAV-based RGB imaging and YOLOv5.

In recent years, computer vision (CV) has made enormous progress and is providing great possibilities in analyzing images for object detection, especially with the application of machine learning (ML). Unmanned Aerial Vehicle (UAV) based high-resolution images allow to apply CV and ML methods for the detection of plants or their organs of interest. Thus, this study presents a practical workflow based on the You Only Look Once version 5 (YOLOv5) and UAV images to detect maize plants for counting their numbers in contrasting development stages, including the application of a semi-auto-labeling method based on the Segment Anything Model (SAM) to reduce the burden of labeling. Results showed that the trained model achieved a mean average precision (mAP@0.5) of 0.828 and 0.863 for the 3-leaf stage and 7-leaf stage, respectively. YOLOv5 achieved the best performance under the conditions of overgrown weeds, leaf occlusion, and blurry images, suggesting that YOLOv5 plays a practical role in obtaining excellent performance under realistic field conditions. Furthermore, introducing image-rotation augmentation and low noise weight enhanced model accuracy, with an increase of 0.024 and 0.016 mAP@0.5, respectively, compared to the original model of the 3-leaf stage. This work provides a practical reference for applying lightweight ML and deep learning methods to UAV images for automated object detection and characterization of plant growth under realistic environments.

Quantifying soil losses and dust emissions by wind tunnel experiments in the cultivated steppe of Kazakhstan

Expanding agriculture and unsustainable farming practices in Central Asia’s steppes may increase the risk of wind erosion and severe dust emissions. However, empirical data from field experiments to assess a potential severe dust source is lacking. Therefore, a mobile wind tunnel was used in northern Kazakhstan to investigate the potential wind-induced soil loss and dust emissions under real field conditions common in agricultural practices. Field experiments were carried out on typical surfaces that act as dust sources: seedbeds as they occur after cultivation, in-field tracks, and dirt roads. Measurements were conducted by sediment collection of total eroded material and optical particle counting for particulate matter ≤30 µm. The results of the wind tunnel experiments show that the same soil can emit significantly different amounts of dust depending on the mechanical stress to which the soil was previously exposed. Soil loss and dust emissions increased from seedbeds to dirt roads due to the intensifying effect of pulverization by tires. In order to assess an area as a dust source or for emission inventories, the total emissions must be adjusted separately to these shares. Further insights of the field experiments will be presented at the conference.

Isolation, Characterization, and Selection of Bacterial Endophytes from Soybean (Glycine max) Nodules and Roots in Some Soils of Cameroon for Promoting Growth of Forage Legume Plants

Aim: The present study aimed to isolate, characterize and select the most effective bacterial endophytes to enhance soybean growth, biomass, and yield. Methodology: Seven soil samples were collected from the rhizosphere of forage legumes in fields across three agroecological zones of Cameroon. Bacterial endophytes were isolated from soybean roots and nodules and cultured on nutrient agar. The isolates were screened for the tolerance tests, extracellular enzymatic activity, P-solubilization activity, and IAA production. The best isolates were selected using a two-factor block factorial design. Each treatment was replicated six times. Results: A total of 85 bacterial endophytes were isolated. Characterization results of 22 preselected isolates revealed an optimal growth temperature of 37°C and a pH range between 6 and 7. Furthermore, the 22 isolates solubilized inorganic phosphate, 7 produced IAA, and 8 exhibited amylase activity. PCR analysis of the nifH and nodC genes showed that the isolates possessed the nifH gene as a nitrogen fixation marker and the nodC gene as a nodulation marker. The findings show that, out of the 22 bacterial endophyte isolates, NTT1 and BOSH9 were the most effective in increasing plant height by 26.74% and 31.78%, respectively. Additionally, they resulted in an increase in biomass of 94.24% to 120.48% and an increase of 71.59% to 76.70% in grain yield compared to control treatment. Conclusion: The selected isolates significantly enhance plant growth, increase biomass, and improve soybean grain yield. However, their potential use as biofertilizers in agriculture will require further investigation under real field conditions.

Detecting soil water redistribution in subsurface drip irrigated processing tomatoes using electrical resistivity tomography, proximal sensing and hydrological modelling

In this study, multiple soil-plant-atmosphere continuum (SPAC) monitoring methodologies, including electrical resistivity tomography (ERT), proximal thermal sensing techniques, and micrometeorological data, were combined with two-dimensional (2-D) soil hydrological modelling using HYDRUS 2-D to explore the soil water redistribution, and infer the relative crop water status in a subsurface drip irrigated (SDI) processing tomato field located in California (Yolo County, USA). Specifically, time-lapse ERT surveys were performed at two transects distributed parallel and perpendicular, respectively, to the SDI line, during an irrigation event. The ERT results were compared to HYDRUS 2-D outputs and the relative differences were explained in the form of local heterogeneities in electrical resistivity (ER) changes, as a proxy for soil water content (SWC) variations. Concurrent simultaneous soil wetting and root water uptake during the last irrigation event of the season caused negligible changes in ER in the active root zone. Slight differences in ER were observed in the top 20 cm along the dripline, confirming that the emitter spacing is small enough to create a wetted strip along the processing tomato bed. These changes were also compared to SWC values measured with time domain reflectometry soil moisture sensors. A comparison between HYDRUS 2-D and ERT confirmed negligible changes in ER during irrigation due to simultaneous wetting and root water uptake processes. In addition, a good correlation was observed between the proximal sensed and the ERT results. Finally, the findings of this study underscore the necessity of using multiple methods for improving our knowledge of the SPAC system under real field conditions.

Actinomycete inoculant improves the growth and yield of rainfed lowland and upland rice under field conditions

This study aimed to assess the effectiveness of actinomycete inoculant in enhancing the growth and yield of rainfed lowland and upland rice across wet and dry seasons in real field conditions. This field assessment took place at four sites, comprising two rainfed lowland and two rainfed upland ecosystems, over two cropping seasons (dry and wet seasons). The experiments involved testing both soil-based and carbonized rice hull (CRH) inoculants. Actinomycetes were found to be effectively carried by CRH and soil, and evidence from field studies in rainfed lowland and upland conditions showed that the actinomycete inoculant significantly improved rice production even under stressful environmental conditions. Regarding plant height, root depth, and tiller number, the inoculated treatments outperformed both the control and the full fertilization rates. Rice yield significantly increased with the application of actinomycete inoculum in both lowland and upland experiments. Inoculation alone led to substantial improvements, with yield increases of up to 48% in Lowland Site 1, 50% in Lowland Site 2, 78% in Upland Site 1, and 43% in Upland Site 2. Similarly, growth was enhanced by inoculation alone, reaching up to 50% in Lowland Site 1, 75% in Lowland Site 2, 24% in Upland Site 1, and 26% in Upland Site 2. When added to the full rate of fertilization, the inoculant significantly boosted yield by up to 16% in Lowland Site 1, 82% in Upland Site 1, and 40% in Upland Site 2. Additionally, growth substantially improved with inoculation in conjunction with the full rate of fertilization, reaching as much as 50% in lowland site 1, and 24% in upland site 1. Actinomycete inoculant proves to be a valuable alternative and addition to agricultural fertilizer management, as it was found to significantly increase growth and yield even in adverse weather conditions.

Evaluation of rapeseed (Brassica napus L.) genotypes for tolerance to PEG (polyethylene glycol) induced drought at germination and early seedling growth

Rapeseed (Brassica napus L.) is an important oilseed crop well adapted to Mediterranean area. However, increasing drought, due to climate change, impairs its growth and development, mainly when this stress occurs early during germination. This study carried out in vitro conditions aimed to investigate the effect of water deficit on seed germination and seedling growth traits in 12 genotypes to select the most drought-tolerant ones. Drought conditions were created using polyethylene glycol 6000 to induce three osmotic potential levels, namely −0.7 MPa for moderate stress, −0.9 MPa for intermediate stress, and −1.1 MPa for severe stress. Germination percentage (GP), germination rate (GR), mean germination time (MGT), root length (RL), shoot length (SL), root-to-shoot ratio (RSR), seedling vigor index (SVI), shoot elongation rate (SER), and root elongation rate (RER) were measured/calculated. ANOVA and PCA were performed to analyze the data gathered. Results showed a significant effect of genotype, drought, and drought × genotype interaction on all studied parameters. Under severe drought conditions, overall values of GP, GR, RL, SL, SER, RER, and SVI decreased by 87.15 %, 53.29 %, 9.33 %, 4.73 %, 78 %, 82 %, and 96 %, respectively, while those of MGT and RSR increased by 56 % and 76 %, respectively, when compared to unstressed conditions. However, the genotypes studied reacted differently to the three levels of water stress. Based on PCA results, RL, RER, and GP proved to be the most discriminating of drought-tolerant genotypes. Overall, higher drought tolerance was associated with higher values in RL, SL, and SVI. Accordingly, the variety ‘Nap10’ and, to a lesser extent, ‘Baraka’ and ‘Nap9’ exhibited higher drought tolerance than the rest of genotypes, which makes them potential cultivars for dry areas. However, further studies in real field conditions are needed to confirm their tolerance to drought occurring at germination as well as during other stages, mainly flowering.

Conversion of mycotoxin-contaminated maize by black soldier fly larvae into feed and fertilizer

Abstract Globally, large amounts of various crops such as cereals, oilseeds, nuts and spices are contaminated with mycotoxins during pre-harvest, postharvest handling, processing and/or storage. Mycotoxin contamination results into economic and health issues, and valorisation options of contaminated crops are urgently needed. The aim of this research was to evaluate whether quality feed and fertilizer can be safely produced from naturally mycotoxin contaminated crops using black soldier fly larvae (BSFL, Hermetia illucens L.) under realistic field conditions in East Africa. Naturally mycotoxin contaminated maize (corn; Zea mays L.) was used as a model due its prevalence as food and feed and utilized by BSFL together with local agri-food by-products at a research facility in Rwanda. To assess the influence of the initial maize mycotoxin contamination and maize inclusion, larval diets with three mycotoxin contamination levels and two maize inclusion levels were tested. BSFL were tolerant against the high mycotoxin concentrations (e.g. 99.4 μg aflatoxin B1 kg dry mass-1) as the presence of mycotoxins in the substrate did not affect BSFL mass at harvest. Product safety was assessed by quantifying the presence of 38 common and emerging mycotoxins and metabolites in the maize, substrates and BSFL products (e.g. larvae and frass). The results show that it is possible to produce feed and fertilizer with BSFL considered safe within the European Union and East African legal limits with maize contaminated with mycotoxins typical for East Africa. Thereby, this research works towards the safe recycling of nutrients from mycotoxin contaminated maize within the food system in East Africa and beyond.

Integrating Agro-Morpho-Physiological Traits and SSR Markers for Detecting the Salt Tolerance of Advanced Spring Wheat Lines under Field Conditions

To successfully enhance the salt tolerance of genotypes, it is crucial to conduct field-based trials, establish effective screening criteria and analysis tools, evaluate salt tolerance at various growth stages, and integrate phenotypic assessment-based traits with molecular markers. This study aimed to assess the salt tolerance of 16 F8 recombinant inbred lines (RILs) and eight genotypes by analyzing 13 agro-morpho-physiological traits using various analysis tools and SSR markers under both control and high salinity levels (15 dS m−1) in real field conditions. Analysis of variance (ANOVA), comparison of mean values, calculation of reduction percentage, and multivariate analysis were used to compare the assessed traits among genotypes and identify which traits are the most effective ones in describing the salt tolerance of these genotypes. A heatmap cluster analysis (HMCA) was also employed to categorize the salt tolerance of genotypes into different clusters based on the stress tolerance index (STI) for all traits. The ANOVA results revealed significant statistical differences (p ≤ 0.05) between the genotypes and salinity levels for all assessed traits in each season and their combined data. Moreover, the 150 mM NaCl treatment led to decreases in the assessed traits by 10.2% to 36.9% when compared to the control treatments. Furthermore, the mean values of assessed traits for certain genotypes were approximately one to three times greater than those of other genotypes. Principal component analysis has identified plant dry weight, green leaf area, leaf area index, and grain yield per hectare as effective screening criteria for explaining the substantial variation observed among the genotypes. The HMCA successfully grouped genotypes into three distinct clusters and distinguished the salt-tolerant genotypes from the salt-sensitive and intermediate ones. The 24 genotypes/RILs were classified into three main groups according to the allelic data of 40 SSRs associated with salt-tolerant genes. A weak yet significant correlation was observed between the similarity coefficients of agro-morpho-physiological traits and SSR markers, as determined by the Mantel test (r = 0.13, p < 0.03, and alpha = 0.05). In conclusion, this study has successfully identified several traits, particularly those associated with SSR markers, that greatly contribute to our understanding of the phenotypic and genotypic basis influencing the salt tolerance of wheat genotypes in real field conditions. Consequently, assessing these traits for a large number of wheat plant materials in a rapid and cost-effective manner will be greatly importance in breeding programs aimed at improving salt stress tolerance in this vital food crop. This will be the main focus of our forthcoming research.

Bioremediation of contaminated soil and groundwater by in situ biostimulation.

Bioremediation by in situ biostimulation is an attractive alternative to excavation of contaminated soil. Many in situ remediation methods have been tested with some success; however, due to highly variable results in realistic field conditions, they have not been implemented as widely as they might deserve. To ensure success, methods should be validated under site-analogous conditions before full scale use, which requires expertise and local knowledge by the implementers. The focus here is on indigenous microbial degraders and evaluation of their performance. Identifying and removing biodegradation bottlenecks for degradation of organic pollutants is essential. Limiting factors commonly include: lack of oxygen or alternative electron acceptors, low temperature, and lack of essential nutrients. Additional factors: the bioavailability of the contaminating compound, pH, distribution of the contaminant, and soil structure and moisture, and in some cases, lack of degradation potential which may be amended with bioaugmentation. Methods to remove these bottlenecks are discussed. Implementers should also be prepared to combine methods or use them in sequence. Chemical/physical means may be used to enhance biostimulation. The review also suggests tools for assessing sustainability, life cycle assessment, and risk assessment. To help entrepreneurs, decision makers, and methods developers in the future, we suggest founding a database for otherwise seldom reported unsuccessful interventions, as well as the potential for artificial intelligence (AI) to assist in site evaluation and decision-making.

Flexible simulation of animal thermoregulatory behavior in three-dimensional synthetic environments from thermal and optical images

Several animals have low metabolism and show body temperatures that are in accordance with environmental temperatures. The relationship between environmental temperature and body temperature is influenced by body size and behavior, in which that some animals accept thermal variation whereas others regulate temperature through behavior. Three-dimensional (3D) environments based on thermal imaging represent a solid tool to elucidate this thermoregulatory behavior to help understand important topics in thermal ecology such as estimating cost of thermoregulation under realistic field conditions, as well as compute the risk of overheating and thermal damage under different ecological scenarios. Although in some studies the proposed models simulate thermoregulation, the connection between thermal and optical image is not explored. This junction can offer a more useful 3D visualization, where topological structures of the landscape can be observed together with thermal information. In this article we present a computational system that is not only capable of generating 3D models by using both thermal and optical images, but is also capable of simulating the thermoregulation behaviors of several animals by means of parameter adjustments made by the user. Our system was analyzed by 22 experts in the biology area – most of them with teaching experience –, who answered objective questions based on their knowledge about the topic, as well as questions about their perception on the simulation provided by our proposal. We used a technology acceptance questionnaire and asked for their contributions through their suggestions. The objective questions reached a hit rate of 68%, which suggests that is relatively easy to use the environment, providing clear comprehension about the simulation, although there is the possibility for improvements. We also found that the hit rate is not related to the experts' level of experience nor with the volunteers' familiarity with 3D environments. Also, 95% of the volunteers indicate a high degree of perceived usefulness, suggesting a potential for using our proposal in teaching and research activities.

Field and laboratory study of iron ore tailings–Portland cement blends for dry stacking

The dry stacking of filtered tailings has emerged as an alternative way of dealing with the safety-related problems of conventional slurry disposal in reservoirs behind upstream dams. Incorporating a cementing agent into the tailings before compaction can enhance the overall geomechanical behaviour of these structures, giving rise to more stable and safer stackings. However, few dry stacks are in operation worldwide and their field performance needs to be better understood. In addition, the addition of cement provides further challenges to the design of these structures. Obtaining reliable laboratory data for properly designing these tailings storage facilities is thus essential. Accordingly, the mechanical behaviour of artificially cemented iron ore tailings for dry stacking purposes was evaluated, with a focus on comparing the responses between field-compacted samples and laboratory-assembled specimens. To do this, ultrasonic pulse velocity, unconfined compression, split tensile and triaxial compression tests were carried out. Both the stiffness and strength data were well described by the porosity/cement index and, despite minor differences, good agreement was found between the responses of the laboratory-assembled and field-compacted samples. The findings highlight the adequacy of laboratory procedures for reflecting the material's real-field conditions.

The use of elements of digital agriculture to obtain climate-based crop yields in specialized crop rotations

Relevance. Precision agriculture has the potential to provide better and more sustainable food production. This term means the use of various technical and software tools for collecting, analyzing and applying information about the state of agrocenoses and implementing mechanisms for their correction directly on the field. Currently, there are many software products on the market that offer to «digitize» production processes in the agro-industrial complex. Most often, this includes the compilation of electronic maps of fields and (based on them) the differentiation of sowing and application of fertilizers and pesticides.Methods. A wide range of field, statistical and analytical methods were used.Results. The data on the possibility and effectiveness of using various elements of digital technologies in precision agriculture in countries with different levels of development of both agriculture and IT technologies are analyzed. The possibilities of using one of the digital agricultural platforms in the cultivation of crops in a specialized crop rotation have been studied. The data of conducting an experiment with flax and annual ryegrass on a digitized field and using modernized equipment are presented. The features of the algorithms of the modules of the information and analytical plant management system for specialized crop rotations with the participation of flax are revealed in real field conditions.

Assessment of Soil–Structure Interaction Effects on the Beirut Port Silos Due to the 4 August 2020 Explosion: A Coupled Eulerian–Lagrangian Approach

Blast loadings have become the subject of research in recent decades due to the threats they pose to the surrounding medium. On 4 August 2020, a huge explosion occurred in the Port of Beirut that led to massive damages in the medium surrounding it. Researchers have conducted studies in order to estimate the equivalent explosive mass as well as the damage extent left on structures; however, the studies considered the soil–structure interaction by simple methods. For that, this paper aims to understand the effect of explosion on the grain silo structure present at the port with an emphasis on the soil–structure interaction effects. The structure consists of a group of silos resting on a raft footing that is supported by group of driven piles. A soil–structure model analysis is performed in order to investigate the soil behavior, the damage extent in piles, and the soil–structure interaction due to the Beirut explosion using the CEL (Coupled Eulerian–Lagrangian) approach that suits events involving large deformation. The analysis is performed using the ABAQUS/Explicit FEM software (version 6.14) taking into account the properties of soil medium, the contact algorithm at the soil–structure interface, and the boundary conditions in order to better simulate the real field conditions and ensure accurate results. The work is primarily validated through site data such as the crater size and silo damage.