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The automated harvesting of clustered fruits relies on fast and accurate visual perception. However, the obscured stem diameters via leaf occlusion lack any discernible texture patterns. Nevertheless, our human visual system can often judge the position of harvesting points. Inspired by this, the aim of this paper is to address this issue by leveraging the morphology and the distribution of fruit contour gradient directions. Firstly, this paper proposes the calculation of fruit normal vectors using edge computation and gradient direction distribution. The research results demonstrate a significant mathematical relationship between the contour edge gradient and its inclination angle, but the experiments show that the standard error projected onto the Y-axis is smaller, which is evidently more conducive to distinguishing the gradient distribution. Secondly, for the front view of occluded lychee clusters, a fully convolutional, feature prototype-based one-stage instance segmentation network is proposed, named the lychee picking point prediction network (LP3Net). This network can achieve high accuracy and real-time instance segmentation, as well as for occluded and overlapping fruits. Finally, the experimental results show that the LP3Net based on this study, along with lychee phenotypic features, achieves an average location accuracy reaching 82%, significantly improving the precision of harvesting point localization for lychee clusters. [ABSTRACT FROM AUTHOR]

Rice lodging not only brings trouble to harvesting but also reduces yield. Therefore, the effective identification of rice lodging is of great significance. In this paper, we have designed a bilinear interpolation upsampling feature fusion module (BIFF) to decompose the quadruple upsampling of the connected part of encoder and decoder into two double upsampling processes and insert the intermediate feature layer in the backbone network for feature fusion in this process. The global attention mechanism(GAM) attention module is added to the feature extraction network, allowing the network to effectively focus on the lodging regions, thus effectively improving the segmentation effect. The average accuracy of the improved network is 93.55%, mrecall is 93.65%, and mIoU is 88.10%, and the feasibility of the improvement is demonstrated by ablation experiments and comparison with other algorithms. In addition, the angle calculation method is designed by combining the detection algorithm, adding a detection head branch to the output results for reading the distance information from the depth camera, and combining the distance information with mechanical analysis to determine the relationship between the angle of the stalk and the vertical direction when the rice is upright, tilted and fallen. A comparison of the calculated angle with the actual measured angle gives a final average error of approximately 5.364%, indicating that the harvest boundary extraction algorithm in this paper is highly accurate and has value for application in real-time harvesting scenarios. [ABSTRACT FROM AUTHOR]

Highlights: [•] Evaluation of new HWP accounting method leads to significant GHG emission reduction for Canada. [•] Use of harvested wood for energy and of construction lead to largest GHG savings. [•] A cascade-use for wood and paper products is paramount for further GHG improvement. [•] The end-of-life effect of using recycled waste wood for energy is delayed due to longer carbon uptake. [•] Cradle-to-cradle utilization of wood waste has an underdeveloped reduction potential. [Copyright &y& Elsevier]

The paper examines the fish population dynamics of a delay logistic model with restrictions on harvesting in a deterministic environment. This is the first time an autonomous model with a harvesting function has been created to regulate the harvesting of fish. The goal of the study is to help external regulatory bodies manage fishing harvests and replenish fish stocks. The dynamics scenario mirrors the facts of fishing systems and enhances the research that has already been done. Two restriction conditions are employed. In the event that the agency detects overfishing, all fish harvesting is prohibited, with the fishing control parameter raised in order to bring the fish back to full stock, while the catchability coefficient is set to zero. However, if the system is designed so that harvesting is carried out in accordance with the fish's sustainable growth level, then we have the partial restriction condition where no ban is imposed on fishing. This model's practical benefit is its adaptability; it may be redesigned to suit any physical system whose functioning is comparable to this phenomenon. As a result, it is advised that the regulatory bodies use the model to manage the fish stock. [ABSTRACT FROM AUTHOR]

From the perspective of ecological control, harvesting behavior plays a crucial role in the ecosystem natural cycle. This paper proposes a diffusive predator-prey system with predator harvesting to explore the impact of harvesting on predatory ecological relationships. First, the existence and boundedness of system solutions were investigated and the non-existence and existence of nonconstant steady states were obtained. Second, the conditions for Turing instability were given to further investigate the Turing patterns. Based on these conditions, the amplitude equations at the threshold of instability were established using weakly nonlinear analysis. Finally, the existence, direction, and stability of Hopf bifurcation were proven. Furthermore, numerical simulations were used to confirm the correctness of the theoretical analysis and show that harvesting has a strong influence on the dynamical behaviors of the predator-prey systems. In summary, the results of this study contribute to promoting the research and development of predatory ecosystems. [ABSTRACT FROM AUTHOR]

Medicinal and aromatic plants (MAPs) are plants that have various uses for human health, nutrition, and well-being. They are sources of herbal medicines, dietary supplements, cosmetics, fragrances, spices, and other products. However, the production and utilization of MAPs also pose challenges and opportunities for environmental sustainability and economic viability. This work offers a wide-ranging review of the sustainable practices and circular economy approaches for the valorization of MAPs. The review discusses the importance and value of MAPs, as well as the principles and benefits of the circular economy, a sustainable model that aims to minimize waste and make the most of resources. The review identifies and analyzes various circular economy approaches for valorizing MAPs, like resource optimization through sustainable cultivation, waste minimization in harvesting and processing, closed-loop supply chains, product diversification and upcycling, circular design thinking for MAP-based products, recycling and reusing MAP-based waste, eco-certification and consumer awareness, and collaborative networks and knowledge sharing. The review paper provides examples and case studies of successful implementation of these approaches, as well as the challenges and limitations that need to be addressed. [ABSTRACT FROM AUTHOR]

Using the multi-product dual revenue function framework, this paper examines the nature and extent of technical and economic interactions among five demersal species of Al-Batinah demersal fishery, Oman during the 2010-2016 period. The parameters of the system of output supply functions are estimated using Zellner's seemingly unrelated regressions (SUR) technique. The likelihood ratio test in relation to the structure of the multi-output production technology rejects the null-hypothesis of input-output separability and non-jointness in inputs. These results suggest that the underlying production technology is non-separable between the outputs and the quasi-fixed input, and the harvesting level of one species is likely to have spill-over economic effects on the harvesting levels of others. The own-price elasticities of output supply are found to be positive, inelastic, and statistically significant in four out of five cases suggesting that, other things being equal, fishers' supply decisions are influenced by the prevailing market prices of outputs. The estimated values of the cross-price elasticity of supply are negative, inelastic and statistically significant at the 5% level for eight output pairs. The calculated values of the Morishima elasticity of substitution (MES) indicate the presence of substitutability between ten output pairs. The cross-price elasticity estimates are in the range of - 0.001 (between Emperor and Catfish) to - 0.275 (between Seabream and Grouper) which is lower (in absolute value) than that of the MES estimates. The magnitudes of the cross-price elasticity and the MES estimates indicate the extent of spill-over effects of one species on the other and signal the extent of species targeting by fishers. A statistically significant spatial, seasonal, and inter-annual variability is observed in all species cases. Finally, the implications of the findings are discussed from the perspective of developing effective management approaches, achieving sustainability of fisheries resources and improving fishers' socio-economic conditions stipulated in the Five-Year development plans for the sector. [ABSTRACT FROM AUTHOR]

A crucial aspect that has to be considered in all fields and, especially, in smart farming, a rapidly developing industry, is maintenance. Due to the costs generated by both under-maintaining and over-maintaining the components of a system, a balance has to be achieved. The paper is focused on presenting an optimal maintenance policy used to ensure cost minimization by determining the optimal time to make a preventive replacement of the actuators of a harvesting robotic system. First, a brief presentation of the gripper with Festo fluidic muscles used in a novel way instead of fingers is given. Then, the nature-inspired optimization algorithm, as well as the maintenance policy are described. The paper also includes the steps and the obtained results of the developed optimal maintenance policy applied for the Festo fluidic muscles. The outcome of the optimization shows that a significant reduction in the costs is obtained if one performs a preventive replacement of the actuators a few days before the lifetime provided by the manufacturer and the lifetime estimated using a Weibull distribution. [ABSTRACT FROM AUTHOR]

The development of agricultural robots is an increasingly popular research field aiming at addressing the widespread labor shortages in the farming industry and the ever-increasing food production demands. In many cases, multiple cooperating robots can be deployed in order to reduce task duration, perform an operation not possible with a single robot, or perform an operation more effectively. Building on previous results, this application paper deals with a cooperation strategy that allows two heterogeneous robots to cooperatively carry out grape harvesting, and its implementation is demonstrated. More specifically, the cooperative grape harvesting task involves two heterogeneous robots, where one robot (i.e., the expert) is assigned the grape harvesting task, whereas the second robot (i.e., the helper) is tasked with supporting the harvesting task by carrying the harvested grapes. The proposed cooperative harvesting methodology ensures safe and effective interactions between the robots. Field experiments have been conducted in order firstly to validate the effectiveness of the coordinated navigation algorithm and secondly to demonstrate the proposed cooperative harvesting method. The paper reports on the conclusions drawn from the field experiments, and recommendations for future enhancements are made. The potential of sophisticated as well as explainable decision-making based on logic for enhancing the cooperation of autonomous robots in agricultural applications is discussed in the context of mathematical lattice theory. [ABSTRACT FROM AUTHOR]

Vietnam’s rice production accounts for a large proportion of rice production in region and the world. The income from rice production accounts for a large proportion of the Country’s total GDP (Gross Domestic Product). Therefore, the investment in machines for rice processing is being interested in this Country. Many types of rice processing machine need to be equipped such as rice harvesters, peelers, separators, etc. In which, the rice harvester is identified as the first type of machine that needs to be equipped for a rice processing line. However, with a large number of rice harvesters available on the market with many different parameters (specifications, selling price) will make it difficult for customers to choose the best rice harvester. In this paper, the selection of rice harvester is carried out using MCDM (Multi-Criteria Decision-Making) method. Five types of rice harvester were offered for ranking, including two drain-type rice harvesters and three bagged-type rice harvesters. Eighteen criteria were used to evaluate each machine type. Those eighteen criteria including: type of machine, length/width/height and weight of machine, length of the sprocket and width of the sprocket, maximum moving speed of the machine when working in slow-moving mode/average-moving mode and fast-moving mode, ground clearance, cutting jaw width, cutting width, lowest cutting height, the maximum cutting height, the width of rake reel, the container capacity, and cost. To determine the weights for the criteria, three different methods have been applied. FUCA (Faire Un Choix Adéquat) was selected as a multi-criteria decision-making method to rank alternatives. The results identified one best drain-type rice harvester and one best bagged-type rice harvester. [ABSTRACT FROM AUTHOR]

In recent years, 360° video cameras have become increasingly accessible and are now being used as valuable research tools across a range of disciplines. Their wide and flexible field of vision can provide immersive and/or alternative perspectives compared to standard video. This paper will present emerging findings from using a 360° video camera to capture natural dye craft practice from an auto-ethnographic perspective and as an observer of other dyers' practice during fieldwork visits. The 360° video data forms part of my doctoral study, in which I explore the embodied interactions between people, plants and materials that connect practitioners to their surroundings, linking them to other species and ecologies. The varied nature of the actions and processes that form the craft practice (e.g. foraging, tending, harvesting, mordanting, dyeing), and the different places and spaces in which these actions occur, presented a practical and observational challenge when trying to record the practice in a video format. Using a 360° camera proved to be a flexible, data-rich and engaging method for recording the craft. The ability to 'move' around and explore different perspectives from within the video after it was recorded was especially valuable, allowing a shift in the focus of the recording and presenting the opportunity to actively centre or decentre plants, people and materials. In this paper, I will reflect on my experiences recording and working with 360° video data and discuss some of the limitations and possible benefits of using this equipment in a craft research setting. [ABSTRACT FROM AUTHOR]

World demand for paper has increased at an average annual rate of 4.7% over the past 40 years. Although future growth will reduce to 2–3% the existing wood resources may be inadequate to meet this growing demand for paper especially in the Asia-Pacific region and Eastern Europe. In addition, logging is coming under increasing pressure from environmentalists concerned about habitat destruction and other longer-term impacts of forest harvesting. It is, therefore, necessary to consider alternative fiber sources to meet the possible shortfall of wood fiber for papermaking. Suitable nonwood fibers are abundantly available in many countries and are the major source of fiber for papermaking in some developing nations. [ABSTRACT FROM AUTHOR]

This paper contributes to the literature and development of knowledge in the topic of energy harvesting by presenting the modelling and calculations of energy from vibration of railway tracks due to moving trains on floating-slab tracks with continuous slabs, considering both the quasi-static and dynamic effects. The floating-slab track is modelled as a double Euler–Bernoulli beam connected by continuous spring and damper elements. The dynamic excitation is accounted for by considering the un-sprung axles of a passing train with a number of coaches. The dynamic excitation is simulated using randomly generated unevenness from standard functions of power spectral density. The responses of rails' beam and slab are calculated for different unevenness realizations, and then used as inputs for a base-excited single-degree-of-freedom system that models the harvester. The change in the harvested energy is investigated due to the change of natural frequency of the harvester, the change of condition of track and change of train's velocity. The parameters used in this paper correspond to tracks and trains for Doha metro and unevenness information from the literature. The results show that more energy can be harvested by tuning the harvester's natural frequency to the frequency of axle-track resonance. It is found that a maximum mean-energy can be harvested from the rails of 0.35 J/kg for a train moving at 100 km/h for a track with poor condition and this is obtained at the axle-track resonance frequency. For the same track condition, a reduction of about 55% and 61% is observed for train's velocities of 70 km/h and 40 km/h, respectively. Using a track with medium and good conditions resulted in reduction of the mean harvested energy at the axle-track resonance by 73.5% and 99.9%, respectively. [ABSTRACT FROM AUTHOR]

Large industrial units generally consume considerable volumes of water for use by workers and sometimes in the manufacturing process, but on the other hand, they generally have large coverage areas that facilitate and enable the capture of large quantities of rainwater. Rainwater harvesting systems (RWHSs) are an alternative water supply with high potential for significant water and economic savings in buildings of this type, also with benefits for water resource sustainability. This paper presents a case study that refers to the design and economic viability determination of an RWHS to be installed in the industrial building of Continental Advanced Antenna Portugal, using an innovative tool called SAPRA—a rainwater harvesting and greywater reuse system in buildings. The main goal was to understand water consumption patterns in social areas (common to most of the industrial typologies) and determine whether RWHSs are feasible in such uses (discarding the production chain). The case study allowed for verification that the assumptions regarding the calculation period design flow significantly interfere with the design flow and the storage capacity. The analysis of the 10-year period yields the most realistic results, and can be framed, if necessary, within the range provided by the analysis of the driest and wettest years. The investment costs should between EUR 90 and 95 million, with annual savings of EUR 7 to 12 million, respectively. The expected payback period is between 7 and 11 years, which is quite feasible and very relevant. This may be an excellent example of how, even within the industries that do not need water for production, this may save significant volumes of water, contributing to the efficient use of this valuable resource. [ABSTRACT FROM AUTHOR]

In order to achieve fast and accurate detection of Gannan navel orange fruits with different ripeness levels in a natural environment under all-weather scenarios and then to realise automated harvesting of Gannan navel oranges, this paper proposes a YOLOv5-NMM (YOLOv5 with Navel orange Measure Model) object detection model based on the improvement in the original YOLOv5 model. Based on the changes in the phenotypic characteristics of navel oranges and the Chinese national standard GB/T 21488-2008, the maturity of Gannan navel oranges is tested. And it addresses and improves the problems of occlusion, dense distribution, small target size, rainy days, and light changes in the detection of navel orange fruits. Firstly, a new detection head of 160 × 160 feature maps is constructed in the detection layer to improve the multi-scale target detection layer of YOLOv5 and to increase the detection accuracy of the different maturity levels of Gannan navel oranges of small sizes. Secondly, a convolutional block attention module is incorporated in its backbone layer to capture the correlations between features in different dimensions to improve the perceptual ability of the model. Then, the weighted bidirectional feature pyramid network structure is integrated into the Neck layer to improve the fusion efficiency of the network on the feature maps and reduce the amount of computation. Lastly, in order to reduce the loss of the target of the Gannan Navel Orange due to occlusion and overlapping, the detection frame is used to remove redundancy using the Soft-NMS algorithm to remove redundant candidate frames. The results show that the accuracy rate, recall rate, and average accuracy of the improved YOLOv5-NMM model are 93.2%, 89.6%, and 94.2%, respectively, and the number of parameters is only 7.2 M. Compared with the mainstream network models, such as Faster R-CNN, YOLOv3, the original model of YOLOv5, and YOLOv7-tiny, it is superior in terms of the accuracy rate, recall rate, and average accuracy mean, and also performs well in terms of the detection rate and memory occupation. This study shows that the YOLOv5-NMM model can effectively identify and detect the ripeness of Gannan navel oranges in natural environments, which provides an effective exploration of the automated harvesting of Gannan navel orange fruits. [ABSTRACT FROM AUTHOR]

The paper presents a two-species prey-predator scenario in which both exhibit swarm behaviour. As a result, due to this co-operative behaviour after harvesting an intra-specific competition occurs between predators. Our research demonstrates that the system's stability is significantly influenced by their inter-dependent competitiveness and joint harvesting effort. We start out by talking about the model's Positivity and Boundedness. The equillibria are analysed and displayed. A Hopf-Bifurcation criterion is discovered. The next step is to do numerical simulations to help explain some of the mathematical findings. We will better grasp the dynamics of predator-prey interaction in natural environments with the help of this model. [ABSTRACT FROM AUTHOR]

This study reports the first recorded CO2 flux measurements of a drained and intensively cultivated lowland peatland in the East Anglian Fens (UK) using the eddy covariance technique. Measurements were made over a complete lettuce crop rotation and a subsequent fallow period. Maximum average daytime CO2 uptake and nocturnal loss rates were -10.39 and 7.63 µmolCO2 m-2 s-1, respectively. Daily CO2 budgets ranged from a net loss of 4.7 to a small net uptake of -1.23 gCO2-Cm-2d-1. Total vertical land/atmosphere CO2 losses were estimated at 227.11±46.5 gCO2-Cm-2 for a 120 day measurement period. Losses over a sixty day interval between field preparation and disking of the field at the end of the crop cycle were 74.22±18.8 gCO2-Cm-2. The site lost 152.89±30.6 gCO2-Cm-2d-1 during a sixty day fallow period. Net ecosystem production was estimated at 117.72±18.8 gCO2-Cm-2 during the crop cycle and 270.61±46.49 gCO2-Cm-2 for the entire measurement period when harvested crop exports were accounted for. These results represent the first micrometeorological measurements obtained over degraded lowland peatland in Britain, and illustrate the scale of CO2 losses associated with agricultural production on temperate organic soils. [ABSTRACT FROM AUTHOR]

Identifying the in-field trajectories of harvests is important for the activity analysis of agricultural machinery. This paper presents a K-means-based trajectory identification method that can automatically detect the "turning", "working", and "abnormal working" trajectories for wheat harvester in-field operation scenarios. This method contains two stages: clustering and correction. The clustering stage performs by the two-step K-means iterative clustering method (D-K-means). In the correction stage, the first step (M1) is performed based on the three distance features between the trajectory segments and the cluster center of the trajectory segments. The second step (M2) is based on the direction change of the "turning" and "abnormal working" trajectories. The third correction step (M3) is based on the operating characteristics to specify the start and stop positions of the turning. The developed method was validated by 50 trajectories. The results for the three trajectories and the five time intervals from 1 s to 5 s both have f1-scores above 0.90, and the f1-score using only the clustering method and the method of this paper increased from 0.55 to 0.95. After removing the turning and abnormal operation trajectories, the error of calculating farmland area with distance algorithm is reduced by 17.04% compared with that before processing. [ABSTRACT FROM AUTHOR]

The mechanical characteristics of paddy straw and wheat Straw were evaluated in terms of shearing strength, specific shearing energy and cutting force with the change in moisture content, loading rate and inter node position. The parameters were determined at three moisture levels: 1%, 7.5% and 19.5% wb, three loading rates: 15, 20 and 25 mm. min-1 and three internodes: first (IN1), second (IN2) and third (IN3) position. The results showed that shear strength and specific shearing energy increased with an increase in moisture content and loading rate from first to third internode position. The shear strength of the paddy straw increased from 5.49 to 13.29, 6.28 to 14.69 and 6.98 to 18.26 MPa and for wheat straw 7.59 to 21.76, 8.16 to 23.8 and 8.77 to 24.8 MPa from first to third internode position. The corresponding value for specific shearing energy were 22.13 to 94.71, 29.86 to 115.6 and 35.02 to 158.6 mJ for paddy straw and 44 to 106.7, 49 to 101.3 and 54.3 to 119.3 mJ for wheat straw, respectively. The cutting force for paddy and wheat straw varied between 13.4 to 24.6 N and 14.8 to 27.5 N for different loading rates and at different inter node positions. Thus, variable load application at different inter node positions can ensure the saving of energy for harvesting and threshing machines. [ABSTRACT FROM AUTHOR]

Addressing the challenge of the current harvester route detection method's reduced robustness within lodging-affected farmland environments and its limited perception of crop lodging, this paper proposes a harvesting operation image segmentation method based on SLIC superpixel segmentation and the AdaBoost ensemble learning algorithm. This segmentation enables two essential tasks. Firstly, the RANSAC algorithm is employed to extract the harvester's operational route through straight-line fitting from the segmented image. Secondly, the method utilizes a 3D point cloud generated by binocular vision, combined with IMU information for attitude correction, to estimate the height of the harvested crop in front of the harvester. Experimental results demonstrate the effectiveness of this method in successfully segmenting the harvested and unharvested areas of the farmland. The average angle error for the detected harvesting route is approximately 1.97°, and the average error for crop height detection in the unharvested area is around 0.054 m. Moreover, the algorithm exhibits a total running time of approximately 437 ms. The innovation of this paper lies in its simultaneous implementation of two distinct perception tasks, leveraging the same image segmentation results. This approach offers a robust and effective solution for addressing both route detection and crop height estimation challenges within lodging-affected farmland during harvesting operations. [ABSTRACT FROM AUTHOR]

Vibration energy harvesting using piezoelectric transduction has emerged as a promising approach for extracting energy from ambient mechanical oscillations. Nonetheless, linear energy harvesters exhibit a confined bandwidth and cannot proficiently capture energy from wide-spectrum or irregular vibrations. To address this limitation, nonlinear energy harvesters have been proposed. This review paper provides an overview of the mechanical design and vibration management aspects of both linear and nonlinear piezoelectric energy harvesters. It also discusses the advantages and shortcomings of both forms. A systematic methodology is applied in surveying the literature on piezoelectric vibration energy harvesting, selecting a set of relevant papers covering the period up until 2023. These papers are then classified dependent on the mechanical design and vibration management aspects of linear and nonlinear power harvesters. Key techniques are identified for harvesting energy from piezoelectric vibrations, including mechanical design methods such as springs, magnets, ropes, and other mechanisms, as well as vibration management techniques involving feedback control, tuned mass dampers, and other approaches. The paper concludes by offering a design viewpoint to enhance energy harvesting proficiency and presenting practical applications for both linear and nonlinear piezoelectric energy harvesting. [ABSTRACT FROM AUTHOR]

Smart agricultural harvesting robots' vision recognition, control decision, and mechanical hand modules all resemble the human eye, brain, and hand, respectively. To enable automatic and precise picking of target fruits and vegetables, the system makes use of cutting-edge sensor technology, machine vision algorithms, and intelligent control and decision methods. This paper provides a comprehensive review of international research advancements in the "eye–brain–hand" harvesting systems within the context of smart agriculture, encompassing aspects of mechanical hand devices, visual recognition systems, and intelligent decision systems. Then, the key technologies used in the current research are reviewed, including image processing, object detection and tracking, machine learning, deep learning, etc. In addition, this paper explores the application of the system to different crops and environmental conditions and analyzes its advantages and challenges. Finally, the challenges and prospects for the research on picking robots in the future are presented, including further optimization of the algorithm and improvement of flexibility and reliability of mechanical devices. To sum up, the "eye–brain–hand" picking system in intelligent agriculture has great potential to improve the efficiency and quality of crop picking and reduce labor pressure, and it is expected to be widely used in agricultural production. [ABSTRACT FROM AUTHOR]

Tactile sensing plays a pivotal role in achieving precise physical manipulation tasks and extracting vital physical features. This comprehensive review paper presents an in-depth overview of the growing research on tactile-sensing technologies, encompassing state-of-the-art techniques, future prospects, and current limitations. The paper focuses on tactile hardware, algorithmic complexities, and the distinct features offered by each sensor. This paper has a special emphasis on agri-food manipulation and relevant tactile-sensing technologies. It highlights key areas in agri-food manipulation, including robotic harvesting, food item manipulation, and feature evaluation, such as fruit ripeness assessment, along with the emerging field of kitchen robotics. Through this interdisciplinary exploration, we aim to inspire researchers, engineers, and practitioners to harness the power of tactile-sensing technology for transformative advancements in agri-food robotics. By providing a comprehensive understanding of the current landscape and future prospects, this review paper serves as a valuable resource for driving progress in the field of tactile sensing and its application in agri-food systems. [ABSTRACT FROM AUTHOR]

There will be great damage in the process of harvesting, transporting, and storing after grain matures. The injury rate is as high as 8% to 12%. After damage, the germination rate of the grain becomes lower, the quality decreases, and it is easily infected with pests and molds. This study of the grain-crushing characteristics is of great significance to ensure grain quality, and an accurate crushing model is a prerequisite for effectively simulating crushing characteristics. This paper studies the shattering characteristics of wheat grains. Two-dimensional slices of wheat grain were obtained using X-ray tomography technology. Then, an accurate three-dimensional outer contour model of the wheat particle was constructed using image filtering and segmentation algorithms. The particle filling process was conducted using EDEM 2018 software to establish a wheat particle simulation model based on the Hertz–Mindlin with a Bonding contact model. Using the DOE experimental design method, single-factor experiments, Plackett–Burman experiments, steepest-climb experiments, and Box–Behnken were designed to study the fragmentation characteristics of wheat particles combined with parameter calibration and physical experiments. The test results show that the normal stiffness per unit area is 7.392 × 1010 N/m3, critical normal stress is 5.293 × 106 Pa, critical tangential stress is 5.001 × 106 Pa, and the relative error about 3%, which verifies the reliability of the simulation parameters in the discrete-element crushing model of wheat grain. This study focuses on two essential aspects: 1. establishing an accurate wheat-grain contour model; and 2. calibrating the bonding parameters of the discrete-element simulation model of wheat grain. The wheat grain discrete-element crushing model and the calibration of its bonding parameters are constructed to provide a foundation for the study of wheat-grain crushing characteristics. It is of great significance to study the situation of wheat grains and where cracks are produced. In this paper, an accurate model of the wheat-grain contour is established, and the bonding parameters of the discrete-element simulation model of the wheat grain are calibrated. The calibration of the model of the discrete elements of wheat-grain fragmentation and its bonding parameters will provide a basis for studying the crushing characteristics of wheat grain. Understanding the condition of wheat grains and the causes of cracks carries significant academic significance. [ABSTRACT FROM AUTHOR]

This review paper provides an overview of various types of photobioreactors (PBRs) that could be used for the production of polyhydroxyalkanoates (PHAs) using anoxygenic photoheterotrophs, with a focus on the design and operation of these systems. The paper highlights the potential of different PBRs based on reactor geometry and growth mode, and also examines the advantages and disadvantages of each PBR type and summarizes their suitability for PNSB-PHA production. The optimization of reactor design and operation is crucial for maximizing PNSB growth and PHA productivity. The self-immobilization of bacteria in granular sludge is a promising technology for wastewater treatment and the production of PHAs, while grooved-surface PBRs and porous-substrate PBRs have limitations due to difficult biomass harvesting in the former and the presence of aerobic conditions incongruent with PNSB culturing in the latter. Limitations exist with all solutions for maximizing rapid growth and maintaining high biomass concentrations due to the requirements of phototrophic growth. [ABSTRACT FROM AUTHOR]

The present paper deals with mathematical modeling of a fishery resource system in an aquatic atmosphere consisting of two zones: a free fishing zone and a reserved zone, where fishing is strictly prohibited. The dynamics of the system is studied in the presence of bird predator. In this paper, the quadratic harvesting of the fish species in unreserved zone has been considered whereas bird predator species is subjected to linear harvesting. All the possible biological and bionomic equilibria of the system are studied extensively for their existence, local as well as global stability. We have found various ranges of harvesting parameter for maintaining the Sustainability in the proposed ecosystem. Optimal harvesting is discussed using Pontryagin's maximum principle. Numerical simulations are done to support the theoretical results obtained. [ABSTRACT FROM AUTHOR]

Tea bud target detection is essential for mechanized selective harvesting. To address the challenges of low detection precision caused by the complex backgrounds of tea leaves, this paper introduces a novel model called Tea-YOLOv8s. First, multiple data augmentation techniques are employed to increase the amount of information in the images and improve their quality. Then, the Tea-YOLOv8s model combines deformable convolutions, attention mechanisms, and improved spatial pyramid pooling, thereby enhancing the model's ability to learn complex object invariance, reducing interference from irrelevant factors, and enabling multi-feature fusion, resulting in improved detection precision. Finally, the improved YOLOv8 model is compared with other models to validate the effectiveness of the proposed improvements. The research results demonstrate that the Tea-YOLOv8s model achieves a mean average precision of 88.27% and an inference time of 37.1 ms, with an increase in the parameters and calculation amount by 15.4 M and 17.5 G, respectively. In conclusion, although the proposed approach increases the model's parameters and calculation amount, it significantly improves various aspects compared to mainstream YOLO detection models and has the potential to be applied to tea buds picked by mechanization equipment. [ABSTRACT FROM AUTHOR]

The present paper presents a comparative study of the threshers that equip different types of self-propelled combines for cereal harvesting. Particular attention is paid to the problems related to the exploitation of the threshers, focusing on some more important aspects, including those regarding the technological adjustments, the organization and management of the harvesting chain, the execution of the harvesting works and their quality control. We measured the constructive and functional parameters of each threshing device and determined the hourly productivity of each combine and the consumption of diesel per ton of harvested wheat. This shows that worldwide harvesting combines tend to use double-flow threshers with tangential flow due to the following advantages: both threshing and separation of the grains from the straw are done, both length of the thresher and vibrations are reduced because there is no shaking for separation, the percentage of non-threshed ears is reduced by the fact that the material is trained in rotary motion inside the threshing device. [ABSTRACT FROM AUTHOR]

Accurately and effectively measuring the breaking quality of harvested corn kernels is a critical step in the intelligent development of corn harvesters. The detection of broken corn kernels is complicated during the harvesting process due to turbulent corn kernel movement, uneven lighting, and interference from numerous external factors. This paper develops a deep learning-based detection method in real time for broken corn kernels in response to these issues. The system uses an image acquisition device to continuously acquire high-quality corn kernel image data and cooperates with a deep learning model to realize the rapid and accurate recognition of broken corn kernels. First, we defined the range of broken corn kernels based on image characteristics captured by the acquisition device and prepared the corn kernel datasets. The corn kernels in the acquired image were densely distributed, and the highly similar features of broken and whole corn kernels brough challenges to the system for visual recognition. To address this problem, we propose an improved model called BCK-YOLOv7, which is based on YOLOv7. We fine-tuned the model's positive sample matching strategy and added a transformer encoder block module and coordinate attention mechanism, among other strategies. Ablation experiments demonstrate that our approach improves the BCK-YOLOv7 model's ability to learn effectively broken corn kernel features, even when high-density features are similar. The improved model achieved a precision rate of 96.9%, a recall rate of 97.5%, and a mAP of 99.1%, representing respective improvements of 3.7%, 4.3%, and 2.8% over the original YOLOv7 model. To optimize and deploy the BCK-YOLOv7 model to the edge device (NVIDIA Jetson Nano), TensorRT was utilized, resulting in an impressive inference speed of 33 FPS. Finally, the simulation system experiment for corn kernel broken rate detection was performed. The results demonstrate that the system's mean absolute deviation is merely 0.35 percent compared to that of manual statistical results. The main contribution of this work is the fact that this is the first time that a set of deep learning model improvement strategies and methods are proposed to deal with the problem of rapid and accurate corn kernel detection under the conditions of high density and similar features. [ABSTRACT FROM AUTHOR]

The paper analyzed the dynamics of Sorghum cultivated area, production and yield in the decade 2010-2019 pointing out the position of Romania among the EU-28 producing countries, the relationship between production and yield using ANOVA, and regression analysis in terms of linear fit. The results showed that the cultivated area increase by 52.7 % in reaching 15,712 ha in 2019. The West and South West regions cultivate 81 % of Romania's cropped area with Sorghum. Production increased 3.2 times reaching 60 thousand tons in 2019, 80% being achieved by West region (63.4 %) and South West Oltenia (10.5%). Sorghum yield was doubled in 2018 achieving 3,819 kg/ha, the highest records being in North East and West. Romania is ranked the 4th in the EU-28 after France, Italy and Hungary for Sorghum cultivated surface, production and yield. Between production and yield is a positive and strong relationship, r = 0.842 and R² = 0.710, reflecting that 71 % of the variation of production is caused by yield change. The regression model Y = 0.0112 x + 13.424, shows that if Sorghum yield increases by one unit, production will grow by 13.43 units. Therefore, a higher productivity per surface unit will contribute to production growth. For this reason, farmers have to use modern technologies involving high potential varieties and hybrids, optimized tillage, fertilization, sowing depth, plant density, distance between rows, correct and timing application of the agricultural works, the use of modern equipments for tillage, sowing, crop maintenance and harvesting. The technologies have to be adapted to climate change, meaning the use of earlier cultivars, looking for a new depth of sowing where soil moisture content favours seeds germination and chose the best harvesting moment. [ABSTRACT FROM AUTHOR]

Soybean seeding monomers can realize the process of opening, seed throwing, covering, and compacting when they work. Due to the complexity of their working process, the relevant process cannot be analyzed by the discrete element method (DEM) alone. The DEM coupled with the multi-rigid body dynamics method (MBD) can solve the above problem, and the simulation analysis of the above process is realized by coupling the EDEM software with RecurDyn software. The changes in the position of soybean seed particles before and after covering and compacting are analyzed. The results show that when the working speed of the seeding monomer increases, the distance along the vertical direction of the soybean seed particles after covering gradually increases, and the distance along the horizontal direction gradually decreases. The effect of different working speeds of seeding monomer on the opening situation and the variation in seed particle positions is studied. The results show that the ditch angle gradually decreases as the working speed of the seeding monomer increases. The distribution of seed particle spacing is also analyzed. The above tests are simulated, and the results show a high agreement between the simulation and test results, proving the accuracy of the coupling method. This paper applies the coupling method for the first time to the simulation of the seeding monomer. This method can be applied not only to the analysis of the sowing process of soybean seeding monomers, but also be applied to the analysis of other machinery working processes, such as the tillage process, the sieving process, the planting and harvesting processes of crops, etc. It also deepens the application of the discrete element method in the field of agriculture. [ABSTRACT FROM AUTHOR]

Purpose: Vertical Farming (VF) is a technique of growing food in vertically stacked layers or vertically inclined surfaces. Theoretically, it has the capability of growing any crops, fruits, or vegetables. The modern techniques of vertical farming through a controlled environment can support the growth and harvest of a crop irrespective of the season and climatic conditions. The newer modern techniques of VF have further reduced the dependency on sunlight, soil, and water. This paper analyses the appropriateness of vertical farming for developing countries like Oman. Design/Methodology/Approach: This paper is based on secondary data and establishes the arguments in favor and against vertical farming. Findings: The findings support the need for vertical farming and suggest that it is very appropriate for the urban setups, where it reduces the transportation cost and facilitates the availability of fresh and pesticide-less food products Research Limitations: The major limitation of this paper is that it only accounts the secondary information, the empirical investigation is missing. Managerial Implications: This paper implies government policies, allied agencies, and the agricultural sector. Originality/Value: This paper is an original paper based on secondary data, providing a conceptual model for Oman, on which very limited studies in the given context is available. [ABSTRACT FROM AUTHOR]

Extracting robust and discriminative local features from images plays a vital role for long term visual localization, whose challenges are mainly caused by the severe appearance differences between matching images due to the day-night illuminations, seasonal changes, and human activities. Existing solutions resort to jointly learning both keypoints and their descriptors in an end-to-end manner, leveraged on large number of annotations of point correspondence which are harvested from the structure from motion and depth estimation algorithms. While these methods show improved performance over non-deep methods or those two-stage deep methods, i.e., detection and then description, they are still struggled to conquer the problems encountered in long term visual localization. Since the intrinsic semantics are invariant to the local appearance changes, this paper proposes to learn semantic-aware local features in order to improve robustness of local feature matching for long term localization. Based on a state of the art CNN architecture for local feature learning, i.e., ASLFeat, this paper leverages on the semantic information from an off-the-shelf semantic segmentation network to learn semantic-aware feature maps. The learned correspondence-aware feature descriptors and semantic features are then merged to form the final feature descriptors, for which the improved feature matching ability has been observed in experiments. In addition, the learned semantics embedded in the features can be further used to filter out noisy keypoints, leading to additional accuracy improvement and faster matching speed. Experiments on two popular long term visual localization benchmarks (Aachen Day and Night v1.1, Robotcar Seasons) and one challenging indoor benchmark (InLoc) demonstrate encouraging improvements of the localization accuracy over its counterpart and other competitive methods. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid marine renewable energy-harvesting system to increase energy production, reduce levelized costs of energy and promote renewable marine energy. Firstly, various marine renewable energy resources and state-of-art technologies for energy exploitation and storage were reviewed. The site selection criteria for each energy-harvesting approach were identified, and a scoring matrix for site selection was proposed to screen suitable locations for the hybrid system. The Triton Knoll wind farm was used to demonstrate the effectiveness of the scoring matrix. An integrated energy system was designed, and FE modeling was performed to assess the effects of additional energy devices on the structural stability of the main wind turbine structure. It has been proven that the additional energy structures have a negligible influence on foundation/structure deflection (<1%) and increased system natural frequency by 6%; thus, they have a minimum influence on the original wind system but increased energy yield. [ABSTRACT FROM AUTHOR]

Rapeseed is an essential oil crop in China. Hubei, Hunan and Jiangxi provinces in the middle reaches of the Yangtze River are the main planting areas, which account for more than 42% of the total planted area of rapeseed in China. The area has more than 3467 ha of winter fallow field, with massive potential for utilization. The total mechanization rate of rapeseed tillage, seeding and harvesting in the 3 provinces is higher than national average. Still, the uneven development, insufficient utilization of idle fields in winter, low levels of mechanization and intelligence, are technical bottlenecks for improving whole mechanization. This paper analyzes the basic situation and existing problems of the rapeseed industry in the middle reaches of the Yangtze River, combing tillage, seeding, harvesting key aspects of mechanization and intelligent technology, and gives the technical routes of the whole process of rapeseed production that could be promoted. Meanwhile, a comparative analysis of the technical characteristics and difficulties in promoting the application of the "tillage, seeding and harvesting" aspects of rapeseed production was carried out. Development trend of rapeseed production technology mode is semi-mechanized, mechanized to intelligent in the winter fallow fields. The technical development paths of each link are discribed as "anti-blocking, anti-sticky, anti-tangle, and high-speed, efficient, low consumption" mechanized tillage. Integrated tillage and seeding technology to complete multiple working procedures simultaneously. High efficiency, low consumption and low loss of rapeseed mechanized harvesting technology should be combined with harvesting and two-stage harvesting synergistic development. The mechanization technology of feeding, vegetable and fertilizer and the critical technology of intelligent rapeseed production should also be developed and applied simultaneously. To achieve high yield, several suggestions for promoting full mechanization technology model for rapeseed in the mid-winter fields of the Yangtze River are proposed. [ABSTRACT FROM AUTHOR]

Intermittent stop–move motion planning is essential for optimizing the efficiency of harvesting robots in greenhouse settings. Addressing issues like frequent stops, missed targets, and uneven task allocation, this study introduced a novel intermittent motion planning model using deep reinforcement learning for a dual-arm harvesting robot vehicle. Initially, the model gathered real-time coordinate data of target fruits on both sides of the robot, and projected these coordinates onto a two-dimensional map. Subsequently, the DDPG (Deep Deterministic Policy Gradient) algorithm was employed to generate parking node sequences for the robotic vehicle. A dynamic simulation environment, designed to mimic industrial greenhouse conditions, was developed to enhance the DDPG to generalize to real-world scenarios. Simulation results have indicated that the convergence performance of the DDPG model was improved by 19.82% and 33.66% compared to the SAC and TD3 models, respectively. In tomato greenhouse experiments, the model reduced vehicle parking frequency by 46.5% and 36.1% and decreased arm idleness by 42.9% and 33.9%, compared to grid-based and area division algorithms, without missing any targets. The average time required to generate planned paths was 6.9 ms. These findings demonstrate that the parking planning method proposed in this paper can effectively improve the overall harvesting efficiency and allocate tasks for a dual-arm harvesting robot in a more rational manner. [ABSTRACT FROM AUTHOR]

In the present paper, we mainly focus on an iterative erythropoiesis model with a nonlinear harvesting term involving a time-varying delay. Under certain conditions and by the Schauder fixed point theorem, we show that the existence of positive periodic solutions can be guaranteed and based on the Banach fixed point theorem, we establish the existence as well as the continuous dependence on parameters theorems of the unique solution. Moreover, two examples are provided to validate the correctness of our outcomes. Our theoretical findings extend and improve several related ones in the existing literature. [ABSTRACT FROM AUTHOR]

Introduction: Real-time fruit detection is a prerequisite for using the Xiaomila pepper harvesting robot in the harvesting process. Methods: To reduce the computational cost of the model and improve its accuracy in detecting dense distributions and occluded Xiaomila objects, this paper adopts YOLOv7-tiny as the transfer learning model for the field detection of Xiaomila, collects images of immature and mature Xiaomila fruits under different lighting conditions, and proposes an effective model called YOLOv7- PD. Firstly, the main feature extraction network is fused with deformable convolution by replacing the traditional convolution module in the YOLOv7- tiny main network and the ELAN module with deformable convolution, which reduces network parameters while improving the detection accuracy of multiscale Xiaomila targets. Secondly, the SE (Squeeze-and-Excitation) attention mechanism is introduced into the reconstructed main feature extraction network to improve its ability to extract key features of Xiaomila in complex environments, realizing multi-scale Xiaomila fruit detection. The effectiveness of the proposed method is verified through ablation experiments under different lighting conditions and model comparison experiments. Results: The experimental results indicate that YOLOv7-PD achieves higher detection performance than other single-stage detection models. Through these improvements, YOLOv7-PD achieves a mAP (mean Average Precision) of 90.3%, which is 2.2%, 3.6%, and 5.5% higher than that of the original YOLOv7-tiny, YOLOv5s, and Mobilenetv3 models, respectively, the model size is reduced from 12.7 MB to 12.1 MB, and the model’s unit time computation is reduced from 13.1 GFlops to 10.3 GFlops. Discussion: The results shows that compared to existing models, this model is more effective in detecting Xiaomila fruits in images, and the computational complexity of the model is smaller. [ABSTRACT FROM AUTHOR]