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This paper shares results from a pedagogical experiment that assigns undergraduates to "cheat" on a final class essay by requiring their use of text-generating AI software. For this assignment, students harvested content from an installation of GPT-2, then wove that content into their final essay. At the end, students offered a "revealed" version of the essay as well as their own reflections on the experiment. In this assignment, students were specifically asked to confront the oncoming availability of AI as a writing tool. What are the ethics of using AI this way? What counts as plagiarism? What are the conditions, if any, we should place on AI assistance for student writing? And how might working with AI change the way we think about writing, authenticity, and creativity? While students (and sometimes GPT-2) offered thoughtful reflections on these initial questions, actually composing with GPT-2 opened their perspectives more broadly on the ethics and practice of writing with AI. In this paper, I share how students experienced those issues, connect their insights to broader conversations in the humanities about writing and communication, and explain their relevance for the ethical use and evaluation of language models. [ABSTRACT FROM AUTHOR]

Now days there has been an increase in the consumers demand for great food, produced in environmentally cost-effective and friendly processes, being increasingly necessary new cultivation techniques able to ensure the required standards for banana growers and consumers. Therefore, the research aimed to evaluate the effects of bunch cover materials on development and quality of 'BRS Platina' banana fruits. The experimental design was in randomized blocks, with six treatments, four replications and four plants per plot, being the treatments: WB - without bagging; BBTK - black polypropylene bag (TNT) + kraft paper; BBPK - black polyethilene bag + kraft paper; KP - kraft paper; WBT - non-woven polypropylene white bag (TNT) and WBP - white polyethylene plastic bag. The 'BRS Platina' banana bunches cover improved fruits quality and the treatment polyethylene white bag (WBP) can be an advantageous option for the banana growers because of its effectiveness on the reduction of damages caused by flowers thrips (Frankliniella spp), providing a good agronomic performance and also does not delay the harvesting. [ABSTRACT FROM AUTHOR]

Surface charge density is a key factor that greatly enhances the performance of a natural-based triboelectric nanogenerator (TENG), which is essential for future sustainable sensing and harvesting devices. This work introduced a conductive interlayer between a main frictional layer and electrode. This approach can suppress the charge recombination rate and improve the amount of charges produced during the triboelectrification process. Bacterial cellulose (BC) film was selected as a main frictional layer for the TENG. A conductive nanomaterial, i.e. silver flake, was incorporated into the BC film as an intermediate layer for enhancing TENG performance. As firstly reported, the maximum electrical outputs for the multi-layer BC structure could be found when using silver flake/BC composite (ratio 1:5) as an intermediate layer, which has 122 V and 8.2 µA of output voltage and current, respectively. This is higher than the output voltage and current of a single layer BC TENG by approximately 3 and 8 times, respectively. The maximum output power of ∼440 µW is achieved by connecting with a load resistor of ∼10 MΩ. This demonstrates an efficient strategy for designing a high performance energy harvester by adding an intermediate layer for the target of practical purposes in sustainable systems. [ABSTRACT FROM AUTHOR]

The hornification processes undergone by the fibers in the paper industry recycling processes lead to the loss of properties of the final products, which exhibit poor mechanical properties. Among the most promising solutions is the reinforcement of secondary fibers with cellulose nanofibers. The present work addresses two important issues: the efficient production of cellulose nanofibers from scarcely exploited agricultural wastes such as horticultural residues and vine shoots, and their application as a reinforcement agent in recycled linerboard recycling processes. The effect of the chemical composition and the pretreatment used on the nanofibrillation efficiency of the fibers was analyzed. Chemical pretreatment allowed a significantly higher nanofibrillated fraction (45–63%) than that produced by mechanical (18–38%), as well as higher specific surface areas (>430 m2/g). The application of the nanofibers as a reinforcing agent in the recycled linerboard considerably improved the mechanical properties (improvements of 15% for breaking length, 220–240% for Young's modulus and 27% for tear index), counteracting the loss of mechanical properties suffered during recycling when using chemically pretreated cellulose nanofibers from horticultural residues and vine shoots. It was concluded that this technology surpasses the mechanical reinforcement produced by conventional mechanical refining used in the industry and extends the number of recycling cycles of the products due to the non-physical modification of the fibers. [ABSTRACT FROM AUTHOR]

Fruit bagging protects fruits from pest infestation and multiple biotic and abiotic stresses during development without reducing yield. It also can potentially improve the quality and market value of fruits. Hence, the present study aimed to determine the effect of pre-harvest fruit bagging on the yield and quality of tomato (Solanum lycopersicum) cv. BARI Tomato-2. The experiment was laid out in a Randomized Complete Block Design (RCBD) with three replications that constituted the various treatments-T0: Control (no bagging), T1: White paper bag (single layer), T2: Brown paper bag (double layer), T3: Non-woven fabric bag, and T4: Jute bag from October 2021 to March 2022. The results showed that the non-woven fabric bag had the highest fruit length (57.79 mm), diameter (57.81 mm), number of fruits plant-1 (55.9), yield (4.06 kg plant-1), vitamin C (24.63mg/ 100g-1), and lycopene content (0.12 mg g-1). The maximum insect incidence (18.41%) was recorded in control and the minimum was counted in T3 (2.17%). The highest total soluble solid (6.73%0Brix) and dry matter (8.63%) were observed in a white paper bag. The highest mean color score (6.08), firmness (26.81%), and shelf life (13 days) found in fabric bags outperformed non-bagged fruits. The fabric bag showed maximum net income (21.12 TK) and the highest benefit: cost ratio (2.36). Thus, the fabric bag was the best for improving the physical and chemical qualities of tomatoes (Solanum lycopersicum) among the various fruitcovering materials. [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]

In the Antarctic, the whale population had been reduced dramatically due to the unregulated whaling. It was expected that Antarctic krill, the main prey of whales, would grow significantly as a consequence and exploratory krill fishing was practiced in some areas. However, it was found that there has been a substantial decline in abundance of krill since the end of whaling, which is the phenomenon of krill paradox. In this paper, to study the krill–whale interaction we revisit a harvested predator–prey model with Holling I functional response. We find that the model admits at most two positive equilibria. When the two positive equilibria are located in the region { (N , P) | 0 ≤ N < 2 N c , P ≥ 0 } , the model exhibits degenerate Bogdanov–Takens bifurcation with codimension up to 3 and Hopf bifurcation with codimension up to 2 by rigorous bifurcation analysis. When the two positive equilibria are located in the region { (N , P) | N > 2 N c , P ≥ 0 } , the model has no complex bifurcation phenomenon. When there is one positive equilibrium on each side of N = 2 N c , the model undergoes Hopf bifurcation with codimension up to 2. Moreover, numerical simulation reveals that the model not only can exhibit the krill paradox phenomenon but also has three limit cycles, with the outmost one crosses the line N = 2 N c under some specific parameter conditions. [ABSTRACT FROM AUTHOR]

In this paper, we consider a predator–prey model with Cosner type functional response and combined harvesting. First, we explore the existence and stability of the equilibria. Then using the center manifold theorem and normal form theory, we investigate codimension one and codimension two bifurcations of the model. The analysis shows that the system has a variety of bifurcation phenomena including transcritical bifurcation, saddle‐node bifurcation, Hopf bifurcation, Bogdanov–Takens bifurcation and homoclinic bifurcation. Our findings indicate that the dynamics with harvesting are significantly richer than the system without harvesting. Finally, numerical simulations are provided to support the analytical results. [ABSTRACT FROM AUTHOR]

• Saffron stigmas crop generates a large amount of biomass called by-product with high added value. • Saffron floral by-products (tepals, leaves, stamens, styles, spaths, and others) are rich in flavonoids, anthocyanins, carotenoids, and volatile compounds. • Chemical compounds contained in the saffron floral by-products have excellent antioxidant and anti-microbiological activities. • Bioactive compounds of saffron floral by-products are a great coloring and aromatic agents strongly used in industrial formulations. Saffron (Crocus Sativus L.), is the most expensive spice in the world, derived from the red filaments (stigmas) of a sterile triploid geophyte plant that belongs to the Iridaceae family. Saffron stigmas have been traditionally used in food and cosmetic products due to their coloring, flavoring, and aromatic aspects, attributed to their bioactive compounds, namely crocin, picrocrocin, and safranal. After harvesting saffron stigmas, a considerable amount of saffron floral parts so-called by-products are generated: petals, leaves, stamens, and others. Recent scientific studies have focused on cosmetic, therapeutic, and nutraceutical applications of not only saffron stigmas but also their several floral by-products. The chemical compounds contained in the whole saffron flower such as polyphenols, flavonoids, and anthocyanins, are endowed with excellent potentialities like antioxidant, and antimicrobial activities which makes them strongly used in food and cosmetic products. This paper aims to provide a systematic overview of specialized (secondary) metabolites applications of saffron stigmas as well as its by-products in various fields, also, the mechanisms of action of saffron bioactive compounds activities have been explained in this paper. [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]

In this paper, we investigate a two-species competition model with seasonal succession and different harvesting strategies. We aim to comprehend the impact of the proportion of good season on species competition outcomes and harvesting practices. For the case of weak harvest, we establish proportion thresholds for the good season for the species. Based on these threshold settings, we determine conditions for the existence and uniqueness of two semi-trivial periodic solutions and the existence, uniqueness and stability of positive periodic solutions under certain conditions. Numerical simulations and brief discussions are presented to validate our theoretical findings. [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 this paper, we discuss how a professor could have students, using rational and exponential functions, develop models for several different density-dependent populations based on characteristics of the species. Students would then need to choose an appropriate method from among three different analytic methods (find a solution, phase-line analysis, graphing approximate solutions) to analyse their models. We then discuss how to use aspects of a species' behaviour to choose an appropriate model from among the ones developed. Finally, we show how a professor could apply what students have learned to develop general principles for sustainable harvesting of such species. [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]

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]

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]

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]

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]

Triggered by the disappearance of the bivalve‐eating red knots Calidris canutus from feeding areas in the western Dutch Wadden Sea after these intertidal flats were exposed to the harvesting of edible cockles Cerastoderma edule by shallow‐draft suction dredging vessels, the admission of such dredging practices in this state nature monument, RAMSAR‐site and area designated protection under the EC Habitat and Bird Directives became an issue of politicized and heated societal debate in the 1990s and early 2000s. In the absence of focussed scientific research yielding peer‐reviewed papers, the few published studies on the issue were 'side‐products' of a long‐term study on foraging ecology and population dynamics of red knots.In their contribution, van der Meer and Folmer criticized some of the statistical analyses in, what they consider, key papers leading to the closure of these forms of shellfish‐harvesting in the Dutch Wadden Sea.The material provided by van der Meer and Folmer represents useful hindsight, further analytical sophistication and a (in our view biased) literature review, and thus raises new issues of concern, while leaving intact the arguments for concern about harmful ecological effects of large‐scale suction‐dredging in protected intertidal areas.Policy implications. We conclude that, even in the light of the statistical concerns expressed by van der Meer and Folmer, there is still reasonable doubt on an absence of harmful ecological effects of suction‐dredging. This means that the eventual precautionary ruling on suction‐dredging for cockles in the protected Dutch Wadden Sea would remain perfectly valid. [ABSTRACT FROM AUTHOR]

Autonomous navigation is an important guarantee for intelligent agricultural machinery to complete harvesting operations. Navigation line recognition is an important step in achieving autonomous navigation of intelligent agricultural machinery. Real-time and accurate detection of harvesting navigation lines can effectively accelerate the progress of operations and reduce crop loss rates. Visual sensors have the advantages of low cost and rich information acquisition, making them widely used in route detection. Frequent changes in lighting under cloudy weather make it difficult for a single feature to cope with wheat field environments. In this study, a wheat harvesting boundary detection method based on multi texture feature fusion was proposed. In the harvested area, the characteristics of low stubble and no top wheat ears result in differences in uniformity, density, and other aspects when harvesting under both light and backlight compared to the non-harvested area. At the same time, due to the exposed wheat stubble in the harvested area, it appears more regular on the image, and its gradient direction is more consistent than that in the non-harvested area. Therefore, a two-dimensional feature vector composed of image entropy and directional gradient was constructed to classify the harvested and unharvested areas of the wheat field, and then the harvesting boundary was extracted. Due to the susceptibility of image entropy features to perspective phenomena, this paper compared the entropy features of different regions in the image when fixing the camera installation angle and height, and ultimately selected the middle region of the image as the region of interest. After analyzing the characteristics of image entropy feature extraction, a histogram statistical method based on sliding windows was proposed to accelerate the speed of image entropy feature extraction. By further dividing the window to be calculated into several sub windows, sliding and combining these sub windows significantly reduced the computation. The entropy feature extraction in this paper took 0.53 seconds, which is 49.52% faster than the traditional method of directly extracting entropy from the entire window. After extracting twodimensional features, based on the distribution characteristics of the feature histogram and combined with the maximum entropy threshold segmentation algorithm, the wheat field image was preliminarily classified. For entropy features, low noise points affected the threshold segmentation effect. By comparing the impact of discarding different proportions of low entropy points on the segmentation threshold, this paper discarded 10% of low entropy points, enhancing the threshold segmentation effect. The method of removing small connected regions was used to eliminate the misclassified regions in the initial classification binary image. Then, the Canny operator was used to extract edge contour points distributed near the harvesting boundary. Due to the consistent direction between the harvesting boundary and the agricultural machinery's forward direction, the Ransac algorithm was used to restrict the fitting line in the region, obtaining the accurate harvesting boundary. In order to verify the feasibility of the method proposed in this article, wheat field images under different lighting conditions were collected, including 2 200 weak light images, 758 local strong light images, and 1 134 strong light images. After selecting the region of interest, the size of the image to be processed was 240 pixels×1 280 pixels. The algorithm in this paper took an average of 0.88 s to process each image on laptops. The detection rates under weak light, local strong light, and strong light were 90.41%, 88.26%, and 89.68%, respectively, with an average of 89.45%. Compared with the traditional method of using Adaboost algorithm for machine learning, the detection speed of this algorithm was improved by 73.89%, and the detection rate was increased by 46.19 percentage points, 46.00 percentage points, and 49.64 percentage points under weak light, local strong light, and strong light, respectively. Compared with traditional algorithms, the method proposed in this paper significantly improved real-time performance and detection rate, which can provide reference for the detection of field agricultural machinery navigation lines. [ABSTRACT FROM AUTHOR]

Continuing progress in machine learning (ML) has led to significant advancements in agricultural tasks. Due to its strong ability to extract high-dimensional features from fruit images, deep learning (DL) is widely used in fruit detection and automatic harvesting. Convolutional neural networks (CNN) in particular have demonstrated the ability to attain accuracy and speed levels comparable to those of humans in some fruit detection and automatic harvesting fields. This paper presents a comprehensive overview and review of fruit detection and recognition based on DL for automatic harvesting from 2018 up to now. We focus on the current challenges affecting fruit detection performance for automatic harvesting: the scarcity of high-quality fruit datasets, fruit detection of small targets, fruit detection in occluded and dense scenarios, fruit detection of multiple scales and multiple species, and lightweight fruit detection models. In response to these challenges, we propose feasible solutions and prospective future development trends. Future research should prioritize addressing these current challenges and improving the accuracy, speed, robustness, and generalization of fruit vision detection systems, while reducing the overall complexity and cost. This paper hopes to provide a reference for follow-up research in the field of fruit detection and recognition based on DL for automatic harvesting. [ABSTRACT FROM AUTHOR]

Blueberries have high nutritional and economic value and are easy to cultivate, so they are common fruit crops in China. There is a high demand for blueberry in domestic and foreign markets, and various technologies have been used to extend the supply cycle of blueberry to about 7 months. However, blueberry grows in clusters, and a cluster of fruits generally contains fruits of different degrees of maturity, which leads to low efficiency in manually picking mature fruits, and at the same time wastes a lot of manpower and material resources. Therefore, in order to improve picking efficiency, it is necessary to adopt an automated harvesting mode. However, an accurate maturity detection model can provide a prerequisite for automated harvesting technology. Therefore, this paper proposes a blueberry ripeness detection model based on enhanced detail feature and content-aware reassembly. First of all, this paper designs an EDFM (Enhanced Detail Feature Module) that improves the ability of detail feature extraction so that the model focuses on important features such as blueberry color and texture, which improves the model's ability to extract blueberry features. Second, by adding the RFB (Receptive Field Block) module to the model, the lack of the model in terms of receptive field can be improved, and the calculation amount of the model can be reduced at the same time. Then, by using the Space-to-depth operation to redesign the MP (MaxPool) module, a new MP-S (MaxPool–Space to depth) module is obtained, which can effectively learn more feature information. Finally, an efficient upsampling method, the CARAFE (Content-Aware Reassembly of Features) module, is used, which can aggregate contextual information within a larger receptive field to improve the detection performance of the model. In order to verify the effectiveness of the method proposed in this paper, experiments were carried out on the self-made dataset "Blueberry—Five Datasets" which consists of data on five different maturity levels of blueberry with a total of 10,000 images. Experimental results show that the mAP (mean average precision) of the proposed network reaches 80.7%, which is 3.2% higher than that of the original network, and has better performance than other existing target detection network models. The proposed model can meet the needs of automatic blueberry picking. [ABSTRACT FROM AUTHOR]

Developing different robotic platforms for farm operations is vital to addressing the increasing world population. A harvesting robot significantly increases a farm's productivity while farmers focus on other relevant farm operations. From the literature, it could be summarized that the design concepts of the harvesting mechanisms were categorized as grasping and cutting, vacuum suction plucking systems, twisting and plucking mechanisms, and shaking and catching. Meanwhile, robotic system components include the mobile platform, manipulators, and end effectors, sensing and localization, and path planning and navigation. The robotic system must be cost-effective and safe. The findings of this research could contribute to the design process of developing a harvesting robot or developing a harvesting module that can be retrofitted to a commercially available mobile platform. This paper provides an overview of the most recent harvesting robots' different concept designs and system components. In particular, this paper will highlight different agricultural ground mobile platforms and their associated mechanical design, principles, challenges, and limitations to characterize the crop environment relevant to robotic harvesting and to formulate directions for future research and development for cotton harvesting platforms. [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]

Abstract: Once again Swedish forest industries are supposed to face a new threatening shortage of wood. Therefore, demonstration of new ideas of producing wood for pulp and fuels (heat, ethanol, hydrogen gas, dimethylether, electricity) in the most southern part of Sweden was very welcome. One example of new ideas is that some farmers planted hybrid poplars on abandoned farming land at Sångletorp (33ha), Johannesholm (15ha), Kadesjö (11ha) and Näsbyholm (2ha) in the southernmost part of Sweden in 1991. Some of these plantations were harvested in 2004, but some are still growing. The harvested wood had been used for pulp and fuel. The results of the harvest and of the plantations that are still growing are analysed here from an economic, ecologic and energy point of view. [Copyright &y& Elsevier]

Lipophilic wood extractives commonly referred to as pitch, cause significant problems for the pulp and paper industries. The reduction of these extractives is an important aspect that concerns industries around the world. In the present work the change in the amount and chemical composition of lipophilic extractives from Eucalyptus spp. stored for 20, 40, 60, 100, 140 and 180 days after harvesting was investigated. The results showed a decrease in extractives content with storage time, with the most significant decrease occurring 60 days after harvesting. In addition, fatty acids and sterols were the main classes of compounds responsible for the significant decrease in extractive content. Data were analyzed by principal component analysis. PC1 explains approximately 99% of the total variance, and β-sitosterol was the major compound responsible for the differentiation. These studies demonstrate that in terms of economical aspects, quality of the pulp and paper and minimization of pitch formation, the best period of wood storage is 60 days. [Copyright &y& Elsevier]

In this essay, we introduce a bioeconomic predator–prey model which incorporates the square root functional response and nonlinear prey harvesting. Due to the introduction of nonlinear prey harvesting, the model demonstrates intricate dynamic behaviors in the predator–prey plane. Economic profit serves as a bifurcation parameter for the system. The stability and Hopf bifurcation of the model are discussed through normal forms and bifurcation theory. These results reveal richer dynamic features of the bioeconomic predator–prey model which incorporates the square root functional response and nonlinear prey harvesting, and provides guidance for realistic harvesting. A feedback controller is introduced in this paper to move the system from instability to stability. Moreover, we discuss the biological implications and interpretations of the findings. Finally, the results are validated by numerical simulations. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Things (IoTs), numerous distributed wide‐area low‐power electronic devices have been utilized in various fields, such as wireless monitoring sensors and wearable electronics. Due to the dispersion and mobility of microelectronic devices, their energy supply faces serious challenges. The inconvenience and non‐environmental friendliness of using traditional centralized low entropy energy and chemical batteries to power distributed microelectronic devices are becoming increasingly prominent. Environmental energy harvesting technology with high entropy characteristics is considered an effective solution for low‐power electronic devices. This paper comprehensively reviews the recent progress in microelectronic technologies based on energy harvesting and signal sensing. First, state‐of‐the‐art micro‐power electronic devices in humans, animals, and the environment are introduced. Secondly, the available micro‐energy sources in the environmentare elaborated and summarized. Then, the principles and characteristics of ambient microenergy harvesting technologies based on different mechanisms are classified, summarized, and analyzed. In addition, this work comprehensively summarizes the applications of self‐powered micro‐electronics technology in 11 different fields, including human, animal, and environment. Finally, research challenges, technical difficulties, and research gaps in self‐powered microelectronics based on micro‐energy harvesting technology are discussed and summarized. [ABSTRACT FROM AUTHOR]

Automatic detection of defects in mature litchi plays a vital role in the classification of fruit grades. The existing method mainly relies on manual, it is difficult to meet the needs of different varieties of litchi various types of commodity packaging, and there are problems such as low efficiency, high cost and poor quality of goods. To address the above problems, this paper proposes an improved You Only Look Once(YOLO)v7 algorithm for the automatic detection of post-harvest mature litchi epidermal defects. First, a dataset of litchi defects (black spot, fall off, crack) was constructed, in which the train and test sets had 4133 and 516; Next, A Simple Parameter-Free Attention(SimAM) mechanism is introduced into the original YOLOv7 backbone network, while GSconv is used in the neck instead of convolution, and the shallow network is used instead of the deep network for lateral linking, finally, the Mish function is used as the activation function. Experimental results show the precious and mAP of the original YOLOv7 are 87.66% and 88.98%, and those of the improved YOLOv7 are 91.56% and 93.42%, improvements of 3.9% and 4.44%. A good foundation is laid for the automated classification of ripe litchi after harvesting. [ABSTRACT FROM AUTHOR]

Duckweed species (Lemnaceae) are suitable for remediation and valorization of agri‐feed industry wastewaters and therefore can contribute to a more sustainable, circular economy where waste is a resource. Industrial applications will, however, require space efficient cultivation methods that are not affected by prevailing weather conditions. Here, the development and operation of a multi‐tiered duckweed bioreactor is described. The developed prototype bioreactor depicted in this paper is composed of four cultivation layers (1 m2 each) with integrated LED lighting (generating up to 150 μmol m−2 s−1), a system of pumps and valves to manage the recirculatory flow (2.5 L min−1) of wastewater, and an automatic harvesting system. Using a nutrient poor medium, good growth of the duckweed species Lemna minor was achieved in the bioreactor, and this was matched by strong nutrient depletion from the medium, especially for phosphorus (45‐mg total phosphorus [TP] removed per m−2 day−1). A fully automatic harvesting arm reliably captured similar amounts of duckweed biomass across multiple harvesting cycles, revealing a future scenario whereby labor and interventions by human operators are minimized. Further developments to advance the system towards fully automated operation will include, for example, the use of specific nutrient sensors to monitor and control medium composition. It is envisaged that multi‐tiered, indoor bioreactors can be employed in the agri‐feed industry where wastewaters are, in many cases, continuously generated throughout the year and need remediating immediately to avoid costly storage. Given the extensive use of automation technology in conventional wastewater treatment plants, multi‐tiered duckweed bioreactors can be realistically integrated within the operating environment of such treatment plants. Practitioner Points: Duckweed is suitable for remediation and valorization of agri‐feed wastewater.Industrial duckweed applications require space efficient cultivation methods.Development and operation of a multi‐tiered duckweed bioreactor is detailed.Flow dynamics and automatic harvesting in the bioreactor are optimized.It is concluded that a multi‐tiered bioreactor can be used in industry. [ABSTRACT FROM AUTHOR]

In recent years, solar-powered, passive adsorption-based air–water harvesting has shown tremendous potential in addressing freshwater shortages in arid regions. Although remarkable progress has been witnessed in unlocking the potential of new adsorbents in the laboratory, the productivity of freshwater is still limited by the slow adsorption kinetic, the large latent heat of water evaporation, and the efficiency of condensation. In this work, superhygroscopic porous gels consisting of titanium nitride, hydroxypropyl methylcellulose, and LiCl (THL) were developed and demonstrated to have a unique high water uptake of 1.18–6.43 gwatergsorbent−1 at 25 °C and 15%–90% relative humidity. To validate the feasibility of THL for moisture extraction, reasonable energy management of the water harvester was carried out, and the potential daytime outdoor water collection in summer and winter reached 3.82 and 2.98 lwater kgsorbent−1 day−1, respectively, at relative humidity of ∼60% and ∼30%. The implementation strategy proposed in this paper provides a reliable path for solar-driven AWH, confirming the adaptability and possibility of achieving high yield freshwater production in real scenarios of practical significance. [ABSTRACT FROM AUTHOR]

In this paper, we investigated the dynamics of the interaction between Microcystis aeruginosa and filter-feeding fish in a new aquatic ecological model and considered the effects of aggregation and harvesting and focused on studying the critical threshold conditions through the analysis of saddle-node bifurcation, Hopf bifurcation, and Bogdanov–Takens bifurcation. We also conducted numerical simulations to illustrate our findings and provided biological interpretations. The results obtained indicate that the aggregation effect or harvesting can disrupt the coexistence of Microcystis aeruginosa and filter-feeding fish. The filter-feeding fish population may go extinct while the Microcystis aeruginosa population could survive. We identified the importance of finding an appropriate timing for harvesting Microcystis aeruginosa in order to promote the growth of the filter-feeding fish population. This optimal timing may be influenced by the carrying capacity of Microcystis aeruginosa. Taken together, our study sheds light on the dynamics of Microcystis aeruginosa and filter-feeding fish in an aquatic ecosystem, highlighting the critical role of aggregation, harvesting, and timing in determining the coexistence and survival of these species. [ABSTRACT FROM AUTHOR]

Promotion of research and development in advanced technology must be implemented in agriculture to increase production in the current challenging environment where the demand for manual farming is decreasing due to the unavailability of skilled labor, high cost, and shortage of labor. In the last two decades, the demand for fruit harvester technologies, i.e., mechanized harvesting, manned and unmanned aerial systems, and robotics, has increased. However, several industries are working on the development of industrial-scale production of advanced harvesting technologies at low cost, but to date, no commercial robotic arm has been developed for selective harvesting of valuable fruits and vegetables, especially within controlled strictures, i.e., greenhouse and hydroponic contexts. This research article focused on all the parameters that are responsible for the development of automated robotic arms. A broad review of the related research works from the past two decades (2000 to 2022) is discussed, including their limitations and performance. In this study, data are obtained from various sources depending on the topic and scope of the review. Some common sources of data for writing this review paper are peer-reviewed journals, book chapters, and conference proceedings from Google Scholar. The entire requirement for a fruit harvester contains a manipulator for mechanical movement, a vision system for localizing and recognizing fruit, and an end-effector for detachment purposes. Performance, in terms of harvesting time, harvesting accuracy, and detection efficiency of several developments, has been summarized in this work. It is observed that improvement in harvesting efficiency and custom design of end-effectors is the main area of interest for researchers. The harvesting efficiency of the system is increased by the implementation of optimal techniques in its vision system that can acquire low recognition error rates. [ABSTRACT FROM AUTHOR]

Rhubarb is a popular food in Europe due to the purgative action. Cultivation is the key way to ensure sustainable development and utilization of this herbal plant. In this paper, standardized cultivation of Rheum tanguticum (a kind of rhubarb) was explored through a three-year field experiment which investigated the effect of phosphorus (P) and potassium (K) fertilizer on growth indexes, sennoside content of R. tanguticum at different harvesting stages. Results showed that P and K fertilizer significantly increased root length, root diameter and root fresh weight, the maximum root fresh weight was 894.22 g/plant. Sennoside content of three-year-old R. tanguticum applied K fertilizer was over 5.87% at each harvesting stage in the third growth cycle and it increased along with growing year. Sennoside content has no obviously increment at the same harvesting stage, it also has no positive correlation with different harvesting stages and the wilting stage > green stage > growth stage in the same year. This study demonstrated that P and K fertilizer had accelerated effect on growth of R. tanguticum at the same harvesting stage, but had no obvious influence on the sennoside content. This will provide experimental data and guidance for the standardized cultivation of R. tanguticum. The effect of different fertilizer (P, K) doses, harvesting stages and growth years on growth indexes and sennosides contents of R. tanguticum was firstly explored. A modified HPLC detective method was developed to quantify the sennosides content of R. tanguticum. The effect of different fertilizer doses on growth indexes of R. tanguticum was firstly explored. Variation of sennoside content after applying fertilizers was firstly explored. The effect of harvesting stages on growth indexes and sennosides contents was firstly explored. [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]

RF harvesting has attracted the attention to be used as a power source for low-power systems like IoT, wireless sensor nodes, and wearable devices. The RF rectifier has been the bottleneck in achieving efficient RF-DC systems. This paper presents a new CMOS RF-DC converter design based on a body-control approach to control the ON & OFF states of the transistors. The design is constructed using the cross-coupled differential-drive (CCDD) rectifier. The design uses less number of capacitors compared to some reported designs in the literature, which saves more active area. The proposed design has been implemented using 0.18 μ m TSMC CMOS technology and verified using Cadence Virtuoso EDA. Thanks to the body-control scheme, the design achieves wide power dynamic range (PDR) of 20 dBm and a peak power conversion efficiency (PCE) of 71.6% for one stage of the proposed design. In addition, the proposed design obtains an output voltage of 0.47 V for 10 k Ω and −15 dBm input power. It occupies an area of 0.0054 mm 2 . The proposed structure is also implemented using three stages to achieve higher output voltage. The 3-stage rectifier achieves a PDR of 17.4 dBm and a peak PCE of 71.2%, and an output voltage of 1.6 V for 100 k Ω load and −14 dBm input power. Finally, the proposed RF-DC rectifier is compared with the state-of-the-art designs reported in the literature. [ABSTRACT FROM AUTHOR]

Forest biomass harvest has the potential to provide feedstocks for energy production to offset fossil fuel consumption. However, concerns have been raised regarding the ecological sustainability of removing additional biomass from forests, in particular the impacts on biodiversity. In this paper, we used a suite of ground-dwelling arthropod taxa (ground beetles, spiders, and rove beetles) to measure community compositional changes along a gradient of biomass removal treatments 7 years postharvest, and compared against reference. Based on multivariate regression trees, changes in species composition reflected the intensity gradient of the biomass removal treatments or stand attributes associated with the level of forest floor disturbance across all arthropod groups. For each arthropod group, changes in composition were defined primarily by reductions or loss of abundant forest associated species and increases in the number and abundance of species associated with more xeric conditions and increased disturbance intensity. There were no differences between full-tree and tree-length treatments. Overall, results indicated a strong arthropod response to the removal of overstory, forest floor disturbance, and reductions in understory cover mostly resulting from the glyphosate applications. Arthropod recovery would benefit from overstory retention, reduction in forest floor disturbance, and judicious use of glyphosate. [ABSTRACT FROM AUTHOR]

Seas, marine, and coastal regions are integral and essential parts of our ecosystem. Many scientific approaches have been taken to ensure the sustainable use of marine resources. Artificial intelligence (AI) plays a vital role in harvesting resources so that the system regenerates itself for the long term. This paper develops a two-input and two-output fuzzy logic-based model to predict the fisheries' remaining biomass after harvesting and maintaining a high revenue level in the Bangladesh Sundarbans region. Fishing & tourism are taken as input parameters, and revenue & biomass are taken as output parameters. A total of 20 rules (IF-THEN type) have been generated in the fuzzy rule editor of Fuzzy Inference System (FIS), considering all possible combinations between input–output parameters. The data which we obtained from the real ecosystem exactly corresponds to the results that we got from our proposed model. Our fuzzy logic model yields valid predictions of the remaining biomass level without compromising profit, only by controlling the harvesting and tourist entry. [ABSTRACT FROM AUTHOR]

In the viticulture sector, robots are being employed more frequently to increase productivity and accuracy in operations such as vineyard mapping, pruning, and harvesting, especially in locations where human labor is in short supply or expensive. This paper presents the development of an algorithm for grape maturity estimation in the framework of vineyard management. An object detection algorithm is proposed based on You Only Look Once (YOLO) v7 and its extensions in order to detect grape maturity in a white variety of grape (Assyrtiko grape variety). The proposed algorithm was trained using images received over a period of six weeks from grapevines in Drama, Greece. Tests on high-quality images have demonstrated that the detection of five grape maturity stages is possible. Furthermore, the proposed approach has been compared against alternative object detection algorithms. The results showed that YOLO v7 outperforms other architectures both in precision and accuracy. This work paves the way for the development of an autonomous robot for grapevine management. [ABSTRACT FROM AUTHOR]

Researchers have shown that even an individual can produce the wisdom of the crowds, called "the wisdom of the inner crowd." However, the previous methods leave room for improvements in terms of efficacy and response time. This paper proposes a more efficient method, which required a short time, based on findings from cognitive and social psychology. The procedure is to ask participants to give two answers to the same question: first, their own estimate and, second, their estimate of public opinion. Experiments using this method showed that the averages of the two estimates were more accurate than the participants' first estimates. That is, the wisdom of the inner crowd elicited. In addition, we found that the method could be superior to other methods in terms of efficacy and convenience. Moreover, we identified the conditions where our method worked better. We further clarify the availability and limitations of using the wisdom of the inner crowd. Overall, this paper proposes an effective and short-time method for harvesting the wisdom of the inner crowd. [ABSTRACT FROM AUTHOR]

Vibration harvesting is a promising method for walnut production owing to its low cost and high efficiency. However, current research focuses on simulation analysis and lacks a theoretical model explaining the walnuts' specific vibration response. This affects the design of the input vibration parameters during harvesting and thus reduces the harvesting efficiency. In this paper, a novel theoretical model of walnuts during vibration harvesting was established to analyze the vibration response, including the motion morphology (motion trajectory and dropping position) and detachment force. A field test was then carried out to verify the theoretical model. The theoretical and experimental results showed that the motion trajectory of the walnuts during vibration harvesting is similar to an ellipse, and the dropping position is at either of the two end points of the trajectory. The detachment force was found to be proportional to the vibration amplitude and the square of the vibration frequency theoretically. This paper provides a theoretical reference for designing the optimal input vibration parameters of a harvesting device to improve the harvesting rate of walnuts. [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]

Pedoclimatic elements and smart technology act simultaneously on the biological processes of agricultural crops and on the level of harvest, which requires the study of their correlation to determine the continuous influence of these factors in obtaining the planned levels of production. In Romania, annually, over 2.1 million hectares of wheat are cultivated, with annual productions of over 10 million tons, placing wheat crops in the first place as economic importance. Knowing the management of natural resources in quantitative terms is especially valuable when we talk about their sustainable use. Our evaluation includes in this paper the relationship between the pedoclimatic resources of South-Eastern Romania and the zoning of wheat varieties in order to obtain superior quantitative and qualitative productions. The last 11 agricultural years are analyzed, both in terms of the influence of global warming and in terms of wheat zoning to obtain sustainable production to highlight the genetic potential of the varieties under the conditions of the Brailei Plain. A stability of the varieties Glosa, Miranda, Litera (varieties over 15 years old) was observed, but not least of the new ones created by the National Research and Development Institute from Fundulea such as: Pitar, Ursita, Adelina, Izvor. [ABSTRACT FROM AUTHOR]

The main goal of this paper is to investigate the existence of positive solutions of a competition model with nonlocal diffusion and nonhomogeneous harvesting. Comparison arguments and sub‐super solutions method play a significant role in our main proof. [ABSTRACT FROM AUTHOR]

In this paper, a novel bidirectional multi-input energy harvesting converter (MIEC) for portable device charger is presented. Switches that are separately regulated using different duty ratios are used in the design of such an MIEC power converter. The peak power of the photovoltaic sources may be tracked using such duty ratios, and the battery power can also be managed. The switch with two directions is also a part of this converter. If necessary, the battery can be charged via a USB charger but it is also capable of supplying power to the portable charging devices using sun and vibration harvesting. A battery receives the remaining energy for recharging while the charging current level can be checked. The entire system built using the MATLAB/SIMULINK environment, performances are monitored by the simulation results and validated experimentally. [ABSTRACT FROM AUTHOR]

Water will be a major limitation to food production in the 21st century, and drought issues already prevail in many parts of the world. Finding solutions to ensure that farmers harvest profitable crops, and secure food supplies for families and feed for animals that will provide for them through to the next season are urgent necessities. The Interdrought community has been addressing this issue for almost 30 years in a series of international conferences, characterized by a multi-disciplinary approach across the domains of molecular biology, physiology, genetics, agronomy, breeding, environmental and social sciences, policy, and systems modeling. This special issue presents papers from the 7th edition of the conference, the first to be held in Africa, that paid special attention to drought in a smallholder context, adding a 'system' dimension to the crop focus from the previous Interdrought events (Varshney et al. , 2018 ; Hammer et al. , 2021). [ABSTRACT FROM AUTHOR]

This paper proposes and investigates a nonselective harvesting Lotka-Volterra predator-prey system that incorporates population closure and the fear effect of the prey. The boundary equilibrium and positive equilibrium are studied in terms of their local and global stability characteristics. Our research indicates that the proportion of commodities designated for harvesting has a significant impact on the dynamic behavior of the system. Meanwhile, dynamic behavior of the system is not affected by the fear effect of the prey species. To demonstrate the viability of the key findings, numerical simulations are performed. [ABSTRACT FROM AUTHOR]