Search

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]

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]

An accurate and robust keypoint detection method is vital for autonomous harvesting systems. This paper proposed a dome-type planted pumpkin autonomous harvesting framework with keypoint (grasping and cutting points) detection method using instance segmentation architecture. To address the overlapping problem in agricultural environment and improve the segmenting precision, we proposed a pumpkin fruit and stem instance segmentation architecture by fusing transformer and point rendering. A transformer network is utilized as the architecture backbone to achieve a higher segmentation precision and point rendering is applied so that finer masks can be acquired especially at the boundary of overlapping areas. In addition, our keypoint detection algorithm can model the relationships among the fruit and stem instances as well as estimate grasping and cutting keypoints. To validate the effectiveness of our method, we created a pumpkin image dataset with manually annotated labels. Based on the dataset, we have carried out plenty of experiments on instance segmentation and keypoint detection. Pumpkin fruit and stem instance segmentation results show that the proposed method reaches the mask mAP of 70.8% and box mAP of 72.0%, which brings 4.9% and 2.5% gains over the state-of-the-art instance segmentation methods such as Cascade Mask R-CNN. Ablation study proves the effectiveness of each improved module in the instance segmentation architecture. Keypoint estimation results indicate that our method has a promising application prospect in fruit picking tasks. [ABSTRACT FROM AUTHOR]

Previous studies have demonstrated that grain loss in the harvest process accounts for a large loss in all aspects of the grain supply chain. This research extensively discusses the impact of farmland management scale on grain loss in the harvest process based on survey data on farmers' productivity in the Shandong and Hebei provinces of China. The findings revealed that the scale of farmland operation directly influenced the grain loss during harvest and that this effect is greatly reduced as the farmland operation scale increases. This study also constructed an intermediary model, investigated the influence mechanisms, and added agricultural capital as a variable in the harvest link. It was discovered that the scale of agricultural land management has an indirect effect on grain loss in the harvest link via the input of agricultural capital in the harvest link. The increase in agricultural capital investment in the harvest link considerably reduced this effect. In order to decrease grain losses during the harvest process, this paper suggests expanding the size of agricultural enterprises, developing new agricultural corporate organizations, and further playing the role of the rural land market. [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]

About 70% of India's economy is involved in the agriculture sector to live their lives and contributed to the GDP of the country. The Crop yield information along with the environmental change estimate will be useful for the agriculturalist to decide on price policies prior to harvesting the food source. It establishes a requirement for the prediction model, which precisely determines the harvest conditions, crop varieties, and agricultural yield. In literature, numerous crop prediction methods were devised to estimate crop production in the agricultural field & each technique has its potential in terms of yield forecasting. This review article provides a detailed analysis of the utilized approaches in the literature for the prediction of crop production as well as a discussion on the identification of concerns related to the yield gaps of crops. The discussed approaches were classified based on the application of different strategies, such as Machine learning methods, Deep learning methods, Data mining techniques, vegetative indices, fuzzy logic, and hybrid methods. The study was analyzed based on performance metrics, year of publication, datasets employed, software used for experimentation, and performance attained using various methods and highlights the research gaps of the respective method along with the future direction. [ABSTRACT FROM AUTHOR]

A few plant species of the Thymelaeceae family are highly valuable and demanding because it contains resinous oil in their stem, branches, and roots. Agarwood (Aquilaria malaccensis) is one of them, which is found primarily in Assam and adjoining region of North Eastern parts of India as well as in other countries of South and South East India. In Assam, the plant is profusely cultivated in Golaghat, Jorhat and Sivasagar districts. Naturally, stem of the older plant is infected by fungal consortia via the holes made by stem borer. The blackish infection appears inside the stem along the line of the borer tunnel and valuable resinous oil can be extracted through water distillation from the black, infected wood. The agar oil has great demand in international market and is used in manufacturing perfume, incense stick, fragrant smoke, and pharmaceuticals industry. Since ancient time, people of this region have been cultivating Aquilaria with the methods adopted from traditional knowledge and found better success of infection and oil yield. Documentation of this traditional knowledge of Aquilaria malaccensis is of significant importance for promoting its cultivation among the people of this region before the knowledge is lost forever. The knowledge will also be useful for developing scientific method of commercial cultivation of this perennial tree. In this paper, we are discussing the traditional method of cultivation of Aquilaria malaccensis including seedling preparation, cultivation, intercropping and management practices. Agar is propagated through seeds for growing healthy seedlings; and cultivation practices of the plant are of utmost concern for harvesting valuable oil. Extensive field survey has been conducted at Golaghat district of Assam, India to document the method of cultivation and management practices of the plant. The results obtained from the field study were analyzed and interpreted for understanding the importance of this traditional cultivation practice. [ABSTRACT FROM AUTHOR]

Women are recognized as proactive catalysts for societal change in rural Bangladesh because of their significant socio-economic contributions to agricultural practices. Historically, they have been closely associated with various agricultural activities ranging from seed preservation to crop harvesting. Despite their vital role in the rural economy, women farmers have encountered a number of obstacles resulting from the adaptation of neoliberal agrarian policies. In recent decades, women's participation in the agricultural sector has declined, which can be attributed to the influence of capitalist patriarchy on agricultural practices. Grounded on this argument, this present study aims to investigate a research question: What are the major challenges to women's participation in agricultural practices in Bangladesh? In order to answer this question, data has been collected through in-depth interviews and household surveys from three villages of Jaldhaka Upazila in the Nilphamari district of northwestern Bangladesh. The findings of this paper argue that women's participation in agriculture is challenged by the effects of the neoliberal policy perspective, such as the mechanization of agriculture, commercial land use, and land inequality, which continue to ratify patriarchy in agriculture. In such a situation, this study suggests women's empowerment could be effectively facilitated by adopting agricultural policies that are specifically tailored to address gender dynamics, emphasizing the active engagement and meaningful representation of women in the processes of policy formulation and execution. [ABSTRACT FROM AUTHOR]

People living in arid and semi-arid areas with highly variable rainfall often face droughts and floods that affect water availability. Surface runoff harvesting is a historical water delivery system utilized in times of water scarcity to fulfill the ever-increasing demand for water, address climate change, and prevent desertification. However, the study of snowmelt and flood water harvesting in steppe areas with cold and semi-arid climates are understudied in the international literature. This paper, combining remote sensing (RS) and a geographic information systems (GIS)-based analytical hierarchy process (AHP), provides a cost-efficient and reliable tool for assessing potential farm pond sites in the steppe region of Northern Kazakhstan. The research is based on six weighted thematic layers: hydrogeology (5%), slope (10%), drainage density (25.5%), land use/land cover (25.5%), soil (5%), and snow water equivalent (29%), which mainly influence the availability, runoff, infiltration, and accumulation of snowmelt and flood water, in order to identify potential farm pond sites in the Akkayin district (North Kazakhstan). As a result, 3.3% of the study area had a very high potential, 35.5% high, 56.5% medium, 4.6% low, and only 0.1% was recognized as the least preferred. The most suitable sites had medium drainage density, low slope, high snow water equivalent, and were located on flooded vegetation. The accuracy of our model was assessed using the existing farm pond sites from the Soviet era, which showed 82% coincidence. Furthermore, by collecting meltwater from 30% of the study area (135,000 ha), one-fourth of the cultivated land in the Akkayin district (i.e., 54,000 ha) could be transferred to deficit irrigation. This would reduce floods, stabilize farmers' income in dry years, and open up the possibility of cultivating other highly profitable crops. Overall, the study provides evidence of the great potential of the Akkayin district in snow meltwater harvesting in farm ponds as a response to agricultural drought and spring floods. [ABSTRACT FROM AUTHOR]

Fruit counting and ripeness detection are computer vision applications that have gained strength in recent years due to the advancement of new algorithms, especially those based on artificial neural networks (ANNs), better known as deep learning. In agriculture, those algorithms capable of fruit counting, including information about their ripeness, are mainly applied to make production forecasts or plan different activities such as fertilization or crop harvest. This paper presents the RipSetCocoaCNCH12 dataset of cocoa pods labeled at four different ripeness stages: stage 1 (0–2 months), stage 2 (2–4 months), stage 3 (4–6 months), and harvest stage (>6 months). An additional class was also included for pods aborted by plants in the early stage of development. A total of 4116 images were labeled to train algorithms that mainly perform semantic and instance segmentation. The labeling was carried out with CVAT (Computer Vision Annotation Tool). The dataset, therefore, includes labeling in two formats: COCO 1.0 and segmentation mask 1.1. The images were taken with different mobile devices (smartphones), in field conditions, during the harvest season at different times of the day, which could allow the algorithms to be trained with data that includes many variations in lighting, colors, textures, and sizes of the cocoa pods. As far as we know, this is the first openly available dataset for cocoa pod detection with semantic segmentation for five classes, 4116 images, and 7917 instances, comprising RGB images and two different formats for labels. With the publication of this dataset, we expect that researchers in smart farming, especially in cocoa cultivation, can benefit from the quantity and variety of images it contains. Dataset: https://doi.org/10.5281/zenodo.7968315. Dataset License: Licensed under Creative Commons Attribution 4.0 International. [ABSTRACT FROM AUTHOR]

Approximately one-third of the global food supply is lost or wasted each year. Given that the harvesting process is the initial stage following food production, minimizing losses in this crucial phase holds paramount significance in augmenting the food supply and ensuring food security. The 1959–1961 famine in China was one of the most catastrophic events in history and had long-term effects on human beings, particularly farmers. This paper aims to provide a new perspective on the variations in harvest losses across age cohorts by examining the impact of famine experiences. Using survey data from the 2016 Postproduction Food Loss and Waste Survey conducted by China Agricultural University and the Rural Economic Research Center, which involved 3538 farming households across 28 provinces, we construct a cohort difference-in-difference (DID) model to investigate the impact of famine experience on household harvest losses. The standard cohort DID estimation results indicate that in areas with severe famine, a 1% increase in excess mortality would reduce the rate of harvest loss by 3%, suggesting that farmers who have experienced extreme famine have a deeper memory of the event, which subsequently helps them reduce harvest losses. Moreover, the results of the heterogeneity test reveal that the more serious the famine that the household head experienced in early life was, the less harvest losses there were, particularly for those who were adolescents during the famine. The findings elucidate the importance of historical events in shaping current behaviors and contribute to a better understanding of the variation in harvest losses across age cohorts. [ABSTRACT FROM AUTHOR]

Seaweed farming in Europe is growing and may provide environmental benefits, including habitat provisioning, coastal protection, and bioremediation. Habitat provisioning by seaweed farms remains largely unquantified, with previous research focused primarily on the detrimental effects of epibionts, rather than their roles in ecological functioning and ecosystem service provision. We monitored the development and diversity of epibiont assemblages on cultivated sugar kelp (Saccharina latissima) at a farm in Cornwall, southwest UK, and compared the effects of different harvesting techniques on epibiont assemblage structure. Increases in epibiont abundance (PERMANOVA, F4,25 = 100.56, p < 0.001) and diversity (PERMANOVA, F4,25 = 27.25, p < 0.001) were found on cultivated kelps over and beyond the growing season, reaching an average abundance of >6000 individuals per kelp plant with a taxonomic richness of ~9 phyla per kelp by late summer (August). Assemblages were dominated by crustaceans (mainly amphipods), molluscs (principally bivalves) and bryozoans, which provide important ecological roles, despite reducing crop quality. Partial harvesting techniques maintained, or increased, epibiont abundance and diversity beyond the farming season; however, these kelp plants were significantly fouled and would not be commercially viable in most markets. This paper improves understanding of epibiont assemblage development at European kelp farms, which can inform sustainable, ecosystem-based approaches to aquaculture. [ABSTRACT FROM AUTHOR]

Robotics has been increasingly relevant over the years. The ever-increasing demand for productivity, the reduction of tedious labor, and safety for the operator and the environment have brought robotics to the forefront of technological innovation. The same principle applies to agricultural robots, where such solutions can aid in making farming easier for the farmers, safer, and with greater margins for profit, while at the same time offering higher quality products with minimal environmental impact. This paper focuses on reviewing the existing state of the art for vision-based perception in agricultural robots across a variety of field operations; specifically: weed detection, crop scouting, phenotyping, disease detection, vision-based navigation, harvesting, and spraying. The review revealed a large interest in the uptake of vision-based solutions in agricultural robotics, with RGB cameras being the most popular sensor of choice. It also outlined that AI can achieve promising results and that there is not a single algorithm that outperforms all others; instead, different artificial intelligence techniques offer their unique advantages to address specific agronomic problems. [ABSTRACT FROM AUTHOR]

The paper goal was to analyze the technical endowment in terms of machinery in Romania's agriculture in the period 2007-2015 and to identify the main trends and problems which affect performance and competitiveness. In the analyzed period, the park of tractors and machinery increased as follows: tractors by 14.5%, ploughs by 13.9%, cultivators by 11.3%, seeders by 14.6%, combines for cereals by 11.4 and for fodder by 17%, machines for potatoes harvesting by 43.5%, presses for hay and straw by 121.7%, only windrowers and sprayers and dusters declined by 1.2%, respectively, by 2.6%. Despite this slight increase, the structure of agricultural machinery is still an uncorresponding one for keeping pace with new technologies. The utilized agricultural area (UAA) increased and as a result the UAA per tractor declined from 44.5 ha to 41.3, which is also a good aspect. Despite that agricultural production increase, its performance is not supported by mechanization mainly in the small sized holdings. The replacement of the existing old machinery with a high usage degree, whose repairs are costing, is obviously needed and requires substantial financial resources. the continuous growth of farm inputs price and the low price at farm gate for agricultural products do not assure farmers enough income to modernize mechanization. The Romanian Government should allocate a higher percentage from budget to agriculture for improving the park of tractors and agricultural machinery. Also, the financial aid per ha coming from the EU should be equitably divided among member states. Farmers need a better endowment in their farms to apply modern technologies and increase production, quality of agricultural products, economic efficiency and competitiveness. [ABSTRACT FROM AUTHOR]

In the present paper was carried out radiography of the rice market in Romania, during 2007-2015. Romanian culinary tradition has a strong impact on the evolution of the rice market in our country. Rice is an important food for about 50% of the planet's population and its consumption has beneficial effects on the health of the population. In order to highlight the evolution of the rice market in Romania and the changes that have taken place on this market, a series of relevant indicators have been used, such as: rice areas cultivated at national and macro-regional level; Total rice production at national and macro-regional level; Average rice production at national and macroregional level; Rice prices; Consumption of rice at national level; Imports and exports of rice. During the analyzed period there was a positive evolution of this market because, on the one hand, we are witnessing an increase of rice surfaces and production, and on the other hand, we have identified changes in the consumption behavior of rice. Consumers in our country are aware of the rice-specific nutritional properties and have begun to use a range of varieties of this product. Rice consumption in Romania is about 4 kg rice / year /per capita. The statistical data used in the paper was provided by the National Institute of Statistics. [ABSTRACT FROM AUTHOR]

A precise measurement of evapotranspiration (ET) and soil water storage (SWS) is necessary for crop management and understanding hydrological processes in agricultural catchments. In this study, we extracted the vegetative indices (VIs, including normalised difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), and enhanced vegetation index (EVI)) from satellite images of the Nučice catchment. We found a consistent seasonal pattern of VIs across the catchment with higher values and variation ranges during spring and summer and lower values and variation ranges during autumn and winter. Spatial variation of VIs also followed a seasonal trend, decreasing during crop growth and increasing after crop harvesting. Seasonal correlations were observed between monthly average ET and SWS with VIs throughout one crop season, which can be expressed mathematically as exponential functions. We propose that VIs can be used as a surrogate measure for ET and SWS in catchments with poor monitoring capabilities. Further studies are required to investigate the spatial distribution of ET and SWS throughout the watershed and their relationship with VIs. Furthermore, our research emphasises the importance of subsurface recharge in the water balance of the investigated fields. It suggests that subsurface flow may be influenced by potential gradients of the water table, driving its seasonal behaviour in response to bedrock morphology. [ABSTRACT FROM AUTHOR]

As climate change and population growth intensify, the agricultural sector's need for sustainable solutions is paramount. This paper presents an overview of smart agriculture, a strategy leveraging technology and data analytics, and its potential to advance sustainability socially, economically, and environmentally. Drawing on recent research, we examine the integration of smart technologies in agriculture, their impact on sustainability, and the architectural design and key factors influencing their adoption. We critically review the literature on the effectiveness of smart agriculture, illuminating how smart farming strategies can curb environmental harm, stimulate economic growth, and promote social inclusivity. We identify existing research gaps and propose directions for future investigation, emphasising the need for rigorous studies on implementation strategies and long-term impacts of smart agriculture on rural landscapes. Our comprehensive overview aims to engage and guide diverse stakeholders, enhancing understanding of sustainable agriculture practices and encouraging more sustainable farming implementations. This paper contributes to global efforts towards harvesting a sustainable future. • Climate change and population growth drive the need for sustainable solutions in agriculture. • Smart tech in agriculture lessens environmental impact and boosts economic and social inclusivity. • Existing literature reveals the effectiveness of smart agriculture in achieving sustainability goals. • Guides stakeholders towards more sustainable farming practices and contributes to global sustainability efforts. [ABSTRACT FROM AUTHOR]

1. Many studies of adaptive harvest management already exist in the literature, but most (if not all) use long, sometimes infinite, time horizons. Such long-term objectives provide an opportunity to manage experimentally, so that poorly understood dynamics are learned and any returns sacrificed for experimentation are repaid by improved management over the remaining time horizon. 2. However, a manager is unlikely to weight outcomes in the distant future equally against outcomes in the present. Furthermore, the most appropriate model of system dynamics may not remain constant over the time-frame required to experiment, learn and improve management. In these cases the use of discounting and/or a finite time horizon fit the manager's assumptions and goals more effectively, and the value of experimentation is likely to be diminished. 3. In this paper we construct a simple model of a hypothetical population and compare optimal passive and active adaptive harvest strategies over a range of time horizons. This allows us to determine the optimal level of experimentation for short-, medium- and long-term goals. 4. We discover that the optimal active adaptive harvest strategy may be precautionary over short to medium time horizons, rather than experimental. That is, an action with known moderate benefits is preferred over an action with uncertain but marginally larger expected benefits. This runs counter to the widespread assumption in the adaptive management literature that incorporating learning into an optimization of management will encourage experimentation. 5. Synthesis and applications. The general results of this paper have potential application to any environmental management problem where adaptive management might be applied; for example, conservation, pest control, harvesting and management of water flows. We examine adaptive management over a range of finite time horizons to reflect a variety of possible management goals and assumptions. Our simple example demonstrates that in the face of model uncertainty, the management strategy that maximizes benefits does not necessarily include deliberate experimentation and learning. Optimal active adaptive management weighs experimentation against all its potential consequences, and this can yield a precautionary approach. [ABSTRACT FROM AUTHOR]

Precision agriculture is a crucial way to achieve greater yields by utilizing the natural deposits in a diverse environment. The yield of a crop may vary from year to year depending on the variations in climate, soil parameters and fertilizers used. Automation in the agricultural industry moderates the usage of resources and can increase the quality of food in the post-pandemic world. Agricultural robots have been developed for crop seeding, monitoring, weed control, pest management and harvesting. Physical counting of fruitlets, flowers or fruits at various phases of growth is labour intensive as well as an expensive procedure for crop yield estimation. Remote sensing technologies offer accuracy and reliability in crop yield prediction and estimation. The automation in image analysis with computer vision and deep learning models provides precise field and yield maps. In this review, it has been observed that the application of deep learning techniques has provided a better accuracy for smart farming. The crops taken for the study are fruits such as grapes, apples, citrus, tomatoes and vegetables such as sugarcane, corn, soybean, cucumber, maize, wheat. The research works which are carried out in this research paper are available as products for applications such as robot harvesting, weed detection and pest infestation. The methods which made use of conventional deep learning techniques have provided an average accuracy of 92.51%. This paper elucidates the diverse automation approaches for crop yield detection techniques with virtual analysis and classifier approaches. Technical hitches in the deep learning techniques have progressed with limitations and future investigations are also surveyed. This work highlights the machine vision and deep learning models which need to be explored for improving automated precision farming expressly during this pandemic. [ABSTRACT FROM AUTHOR]

The aim of this study is to evaluate the hazards in operating post-harvest lines in an agricultural organization, with emphasis on pre-cleaning and drying of grain. The paper deals with a description of technology of pre-cleaning and drying grain. Risk assessments at post-harvest line were processed by using the point method. The point method is used to review the hazards for working positions in terms of safety at work. This work identifies threats, proposes measures and solutions to eliminate or minimize the consequences of each risk. Evaluations of the hazards are processed before and after the measures were taken. Based on the results, it can be stated that the risk was reduced below the level of acceptable risk after the measures were taken. [ABSTRACT FROM AUTHOR]

Introduction: With continuously increasing labor costs, an urgent need for automated apple- Qpicking equipment has emerged in the agricultural sector. Prior to apple harvesting, it is imperative that the equipment not only accurately locates the apples, but also discerns the graspability of the fruit. While numerous studies on apple detection have been conducted, the challenges related to determining apple graspability remain unresolved. Methods: This study introduces a method for detecting multi-occluded apples based on an enhanced YOLOv5s model, with the aim of identifying the type of apple occlusion in complex orchard environments and determining apple graspability. Using bootstrap your own atent(BYOL) and knowledge transfer(KT) strategies, we effectively enhance the classification accuracy for multi-occluded apples while reducing data production costs. A selective kernel (SK) module is also incorporated, enabling the network model to more precisely identify various apple occlusion types. To evaluate the performance of our network model, we define three key metrics: APGA, APTUGA, and APUGA, representing the average detection accuracy for graspable, temporarily ungraspable, and ungraspable apples, respectively. Results: Experimental results indicate that the improved YOLOv5s model performs exceptionally well, achieving detection accuracies of 94.78%, 93.86%, and 94.98% for APGA, APTUGA, and APUGA, respectively. Discussion: Compared to current lightweight network models such as YOLOX-s and YOLOv7s, our proposed method demonstrates significant advantages across multiple evaluation metrics. In future research, we intend to integrate fruit posture and occlusion detection to f]urther enhance the visual perception capabilities of apple-picking equipment. [ABSTRACT FROM AUTHOR]

The article explores multiple attribute decision making problems through the use of the Pythagorean neutrosophic vague normal set (PyNVNS). The PyNVNS can be generalized to the Pythagorean neutrosophic interval valued normal set (PyNIVNS) and vague set. This study discusses q-rung log Pythagorean neutrosophic vague normal weighted averaging (q-rung log PyNVNWA), q-rung logarithmic Pythagorean neutrosophic vague normal weighted geometric (qrung log PyNVNWG), q-rung log generalized Pythagorean neutrosophic vague normal weighted averaging (q-rung log GPyNVNWA), and q-rung log generalized Pythagorean neutrosophic vague normal weighted geometric (q-rung log GPyNVNWG) sets. The properties of q-rung log PyNVNSs are discussed based on algebraic operations. The field of agricultural robotics can be described as a fusion of computer science and machine tool technology. In addition to crop harvesting, other agricultural uses are weeding, aerial photography with seed planting, autonomous robot tractors and soil sterilization robots. This study entailed selecting five types of agricultural robotics at random. There are four types of criteria to consider when choosing a robotics system: robot controller features, cheap off-line programming software, safety codes and manufacturer experience and reputation. By comparing expert judgments with the criteria, this study narrows the options down to the most suitable one. Consequently, q has a significant effect on the results of the models. [ABSTRACT FROM AUTHOR]

This work proposes a Learning by Demonstration framework based on Dynamic Movement Primitives (DMPs) that could be effectively adopted to plan complex activities in robotics such as the ones to be performed in agricultural domains and avoid orientation discontinuity during motion learning. The approach resorts to Lie theory and integrates into the DMP equations the exponential and logarithmic map, which converts any element of the Lie group S O (3) into an element of the tangent space s o (3) and vice versa. Moreover, it includes a dynamic parameterization for the tangent space elements to manage the discontinuity of the logarithmic map. The proposed approach was tested on the Tiago robot during the fulfillment of four agricultural activities, such as digging, seeding, irrigation and harvesting. The obtained results were compared to the one achieved by using the original formulation of the DMPs and demonstrated the high capability of the proposed method to manage orientation discontinuity (the success rate was 100 % for all the tested poses). [ABSTRACT FROM AUTHOR]

Image feature detection serves as the cornerstone for numerous vision applications, and it has found extensive use in agricultural harvesting. Nevertheless, determining the optimal feature extraction technique for a specific situation proves challenging, as the Ground Truth correlation between images is exceedingly elusive in harsh agricultural harvesting environments. In this study, we assemble and make publicly available the inaugural agricultural harvesting dataset, encompassing four crops: rice, corn and soybean, wheat, and rape. We develop an innovative Ground Truth-independent feature detector assessment approach that amalgamates efficiency, repeatability, and feature distribution. We examine eight distinct feature detectors and conduct a thorough evaluation using the amassed dataset. The empirical findings indicate that the FAST detector and ASLFeat yield the most exceptional performance in agricultural harvesting contexts. This evaluation establishes a trustworthy bedrock for the astute identification and application of feature extraction techniques in diverse crop reaping situations. [ABSTRACT FROM AUTHOR]

Copyright of Economics of Agriculture / Ekonomika Poljoprivrede is the property of Institute of Agricultural Economics (IAE) / Belgrade and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Forest ecosystems where periodical tree bark harvesting is a major economic activity may be particularly vulnerable to disturbances such as fire, since debarking usually reduces tree vigour and protection against external agents. In this paper we asked how cork oak Quercus suber trees respond after wildfires and, in particular, how bark harvesting affects post-fire tree survival and resprouting. We gathered data from 22 wildfires (4585 trees) that occurred in three southern European countries (Portugal, Spain and France), covering a wide range of conditions characteristic of Q. suber ecosystems. Post-fire tree responses (tree mortality, stem mortality and crown resprouting) were examined in relation to management and ecological factors using generalized linear mixed-effects models. Results showed that bark thickness and bark harvesting are major factors affecting resistance of Q. suber to fire. Fire vulnerability was higher for trees with thin bark (young or recently debarked individuals) and decreased with increasing bark thickness until cork was 3-4 cm thick. This bark thickness corresponds to the moment when exploited trees are debarked again, meaning that exploited trees are vulnerable to fire during a longer period. Exploited trees were also more likely to be top-killed than unexploited trees, even for the same bark thickness. Additionally, vulnerability to fire increased with burn severity and with tree diameter, and was higher in trees burned in early summer or located in drier south-facing aspects. We provided tree response models useful to help estimating the impact of fire and to support management decisions. The results suggested that an appropriate management of surface fuels and changes in the bark harvesting regime (e.g. debarking coexisting trees in different years or increasing the harvesting cycle) would decrease vulnerability to fire and contribute to the conservation of cork oak ecosystems. [ABSTRACT FROM AUTHOR]

Copyright of Acta Scientiarum: Agronomy is the property of Universidade Estadual de Maringa and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Economic Botany is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract  Spatially explicit harvest scheduling models that can promote the development of dynamic mature forest patches have been proposed in the past. This paper introduces a formulation that extends these models by allowing the total perimeter of the patches to be constrained or minimized. Test run results suggest that the proposed model can produce solutions with fewer, larger, and more compact patches. In addition, patches are more likely to be temporally connected with this formulation. Methods for identifying the tradeoffs between the net present value of the forest and the size and perimeter of the evolving patches are demonstrated for a hypothetical forest. [ABSTRACT FROM AUTHOR]

Abstract: This paper analyses how different management schemes influence the exploitation and economics of a wildlife population—the moose (Alces alces)—that is both a value (harvesting income) and a pest (forestry damage). Two regimes are explored; the unified management scheme where the wildlife manager aims to find harvesting quotas that maximise the overall benefit of the moose population, and the market solution where the landowners follow their narrow self-interests and maximise their private profit. Because the moose is partly a migratory species, these regimes will differ both with respect to harvesting income and browsing damage, and the landowners will experience different profit. The unified scheme is very similar to the actual Scandinavian management, while the market solution is closer to the management policy one finds in North America. In the first part of the paper it is shown how the harvesting quotas and browsing damage under these two regimes are influenced by dispersal as well as other ecological and economic factors. In the last part of the paper it is demonstrated that under the unified management regime the present practice of neglecting migration may lead to sub-optimally sized populations of migrating moose and an overall economic loss. It is also shown that neglecting migration leads to a substantial profit transfer among the landowners. The model is supported by a real life numerical example. [Copyright &y& Elsevier]

The ratio-dependent predator-prey model exhibits rich interesting dynamics due to the singularity of the origin. The objective of this paper is to study the dynamical properties of the ratio-dependent predator-prey model with nonzero constant rate harvesting. For this model, the origin is not an equilibrium. It is shown that numerous kinds of bifurcation occur for the model, such as the saddle-node bifurcation, the subcritical and supercritical Hopf bifurcations, Bogdanov-Takens bifurcation, the homoclinic bifurcation, and the heteroclinic bifurcation, as the values of parameters of the model vary. Hence, there are different parameter values for which the model has a limit cycle, or a homoclinic loop, or a heteroclinic orbit, or a separatrix connecting a saddle and a saddle-node. These results reveal far richer dynamics compared to the model with no harvesting. [ABSTRACT FROM AUTHOR]

Goji berry (Lycium barbarum L.) is described as a food with several health benefits. This valuable fruit has been processed into fruit leather, which is one of the important traditional products. This study allowed us to determine the effect of changing formulations (starch and flour) and cooking times (10, 15, and 20 min) on quality criteria during goji berry leather production. The goji berry leather samples exhibited the following characteristics: pH values ranging from 4.96 to 4.99, titration acidity values spanning from 2.97% to 3.23%, dry matter content within the range of 84.78% to 87.69%, ash content between 3.12%, and 4.27%, water activity (aw) values ranging from 0.37 to 0.38, protein content between 14.71%, and 15.66%, HMF content varying from 17.33 mg/kg to 34.51 mg/kg, total phenolic content ranging from 7.14 µg GAE/g to 7.89 µg GAE/g, total sugar content falling between 61.18%, and 63.89%, reducing sugar content within the range of 60.13% to 61.89%, sucrose content ranging from 0.91% to 2.25%, thickness values ranging from 0.74 mm to 0.89 mm, hardness values varying between 17.09, and 33.27, stickiness values within the range of 4.10 to 23.34, cohesiveness values from 0.85 to 1.00, elasticity values ranging from 0.88 to 0.97, chewability values spanning from 12.85 to 30.85, L* values ranging from 27.27 to 32.66, a* values between 8.62, and 10.32, b*values varying from 7.06 to 8.59, C* values within the range of 11.15 to 12.81, and H° values falling between 39.31, and 41.13. Following the sensory evaluation of leather samples, it was determined that the color score ranged from 3.80 to 4.27, the smell score fell between 3.53 and 4.00, the taste score varied from 2.93 to 4.00, the mouthfeel score ranged from 2.93 to 3.93, and the general acceptability score was found to be within the range of 3.27 to 4.20. It was determined that cooking time and additional additives affect some quality values of the goji berry leather. [ABSTRACT FROM AUTHOR]

This study aimed to examine whether different agricultural work patterns may cause MSDs in different body areas and assess their severity. In previous studies, musculoskeletal disorders (MSDs) have been recognized as an occupational disease in agricultural workers. However, not all MSDs resulting from agriculture occur in the same body areas or have similar severity. This study conducted a questionnaire survey to inquire about the body areas and severity of MSDs among fruit farmers. A total of 212 valid answers were collected. Subsequently, their main harvesting postures were classified into three groups to examine the differences in MSD occurrence and severity among those groups. The results indicate that the harvesting posture did result in different severities of MSDs in certain body areas. Raising hands above the shoulders caused significantly higher MSD severity in the neck, shoulder, upper back, and elbows despite the same standing posture. Additionally, those who squatted, bent, or kneeled had a significantly higher severity of MSDs in the shoulders compared to those who stood but did not raise their hands above their shoulders. This study confirmed that the different harvesting heights of fruits can affect farmers' posture, leading to differences in the body areas affected by, and severity of, MSDs. [ABSTRACT FROM AUTHOR]

Tappeh Sang-e Chakhmaq is the only Neolithic site in Northeastern Iran, characterised by aceramic and ceramic levels corresponding to an occupation of 1500 years from the eighth to the end of the sixth millennium BCE. The Western and Eastern Mounds represent the oldest and longest occupation among the sites identified East of the Zagros, providing a unique context to explore the origin and spread of farming outside the core area of the Eastern Fertile Crescent. We present data about the first harvesting activities in the Northeastern Iranian Central Plateau by applying usewear and microtexture analysis through confocal microscopy on sickle gloss blades. Our results indicate a community of pioneer farmers who settled down in the area carrying with them both domestic cereals as well as advanced techniques of cereal cultivation. We demonstrate that most of the tools were used for harvesting cereals in a fully ripened state collected near the ground, indicating a well-established cereal cultivation strategy. The use of straight shafts with parallel inserts in Tappeh Sang-e Chakhmaq, as known in some sites in the Zagros, suggests the dispersal of farming practices and technologies from the Eastern Fertile Crescent north-eastward across Iran. We observe an evolution in the degree of ripeness of harvested cereals along the first four levels of occupation of the Western Mound, where semi-ripe harvesting is relatively important, suggesting that domestic cereals to be harvested before full maturity were introduced into the village. From the topmost of the Western Mound and along the occupation of the Eastern Mound, ripe harvesting is dominant, showing a well-established cultivation strategy of fully mature cereal. This shift could indicate an in-situ evolution towards a better-established agricultural technology, including harvesting riper crops, that would have resulted in higher yields, as cereals were collected when the grain was fully formed. [ABSTRACT FROM AUTHOR]

The research was based on a field experiment carried out in the Agricultural Experimental Station in the climatic and soil conditions of north-eastern Poland. The factors of the experiment were: I–morphotypes of winter oilseed rape: population, restored hybrid with a traditional type of growth, restored hybrid with a semi-dwarf type of growth. II–methods of using preparations: variant (1)—control–without using preparations; variant (2)–an organic preparation containing microorganisms as well as micro and macro elements (Ugmax); variant (3)–a biostimulant containing 13.0% of P₂0₅ and 5.0% of potassium oxide (K₂O); variant (4)–a biostimulant containing silicon. The objectives of study was to determine the effect of preparations containing microorganisms as this well as micro and macro-elements, phosphorus and potassium and silicon on the morphometric features of plants, such as: the height of the first fruit-bearing lateral branching on the main shoot, the thickness of the stem at the base, number of productive branches and siliques on the plant, the length of the pods, plant height before harvesting. The organic preparation containing microorganisms as well as micro and macro-elements, applied in the autumn before sowing seeds and in the spring after the start of vegetation, had the most beneficial effect on the biometric characteristics of plants before harvesting. [ABSTRACT FROM AUTHOR]

The Peiligang culture (ca. 9000–7000 cal. BP) represents the first Neolithic settlements in the middle Yellow River Valley, marking the beginning of millet and rice farming in the region. While previous studies have focused primarily on identifying cultivated cereals, less attention has been given to plant harvesting and processing practices or environmental conditions. To address this gap, we present new phytolith data from the Peiligang site (8000–7600 cal. BP) to make three key contributions. First, we show that the Peiligang people utilized two microhabitats: hillslopes for dryland millet cultivation and alluvial plain for wetland resources. Second, we combine our findings with other archaeological evidence to reconstruct the environmental conditions of the Peiligang site, suggesting that it was a water-rich habitat. Finally, by analyzing phytolith remains of plant processing waste in middens, we reconstruct how people harvested and processed millets and rice at the site. This study sheds light on the plant-based subsistence strategies employed by the Peiligang people and offers insights into the environmental factors that contributed to the development of early farming in the middle Yellow River Valley. [ABSTRACT FROM AUTHOR]

Copyright of Alexandria Science Exchange Journal is the property of Alexandria Science Exchange Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Over the past 20 years, substantial progress has been made in improving feeds and feeding technologies for most aquaculture species. Notable improvements in feed conversion efficiency (through a better understanding of requirements and improved feed management) and ingredient sustainability (through increased capability to use a wider range of ingredients) have been achieved. While advances have been made in understanding the requirements of many of the main aquaculture species, there is still much to be done in defining requirements, especially for many of the species being farmed in the developing world. Gains in the efficiency of feeds are slowing for developed species, but potential gains are still appreciable for less developed species. There is a growing need to more precisely prescribe the required levels of essential nutrients and various additives in the diet based on age, genotype, environment, and immune status to deliver a "precision nutrition" approach to farming aquaculture species. There is still further need to diversify our ingredient options to provide greater resilience, as the sustainability of different feed ingredient sources, including possible climate change impacts, is becoming a growing issue. There is a growing demand for biocircularity in our feed ingredient supply chains. Ultimately, what is needed to sustain future feed ingredient needs are sustainable sources of cost‐effective protein, some essential amino acid additives, some omega‐3 fatty acid resources, and various minerals and vitamin additives. The increasing use of new and varied resources will ensure that food safety remains an important issue throughout the world. Feed manufacturing has evolved from a simplistic exercise to a highly complex science with state‐of‐the‐art engineering, but its application is not consistent across all sectors, as there is still widespread use of pelleting, mash, and trash fish feeding in the developing world. Similarly, feed management has also dichotomized between the developed and developing world, with a high reliance on manual skilled labor in the developing world, whereas more advanced aquaculture systems are becoming increasingly reliant on automated computer‐controlled feeding systems. [ABSTRACT FROM AUTHOR]

Freshwater resources are uncertain in Palestine and their uncertainty is expected to intensify due to climate change and the political situation. Yet, in this region, a stable freshwater supply is vital for domestic and agricultural uses. Rainwater harvesting could help to increase freshwater availability. This study investigates the economic feasibility of two rainwater harvesting applications in the West Bank, with eyebrow terracing in olive groves in rural areas and domestic rooftop harvesting in urban areas. Cost-effectiveness is estimated using a spatially explicit cost–benefit analysis. Three land zones varying in suitability for the implementation of eyebrow terracing in olive groves are analyzed. The potential increase in olive yield is estimated with a crop–water balance model. The potential amount of rainfall that can be harvested with domestic rooftop harvesting is calculated based on the average rooftop area for each of the 11 governorates individually. Costs and benefits are considered at the household level to calculate the economic feasibility of these two applications. Although eyebrow terracing enlarges soil moisture availability for olive trees and thereby increases olive yield by about 10–14%, construction costs are too high to make implementation cost-effective. Similarly, rooftop harvesting can harvest about 30% on average of the annual domestic water demand and is worthwhile in the northern and southern governorates. Yet, in this case, construction costs are generally too high to be cost-effective. This obstructs more widespread adoption of rainwater harvesting in the West Bank, which is urgently needed given the large impacts of climate change. Providing subsidies for rainwater harvesting could help to make adoption more attractive for households. [ABSTRACT FROM AUTHOR]

This work brings a refined estimation of the land use change and derived CO2 emissions associated with sugarcane cultivation, including changes in management practices and refined land-use carbon stocks, over the last two decades for Brazil's center–south and north regions. The analysis was carried out at the rural property level, considering spatially explicit land conversion data. With the refinements, we found a net carbon removal of 9.8 TgCO2∙yr−1 in sugarcane cultivation areas in the 2000–2020 period, which was due to the expansion of sugarcane over poor quality pastures (55% of the gross removals), croplands (15%) and mosaic (14%) areas, and the transition from the conventional burned harvesting to unburned (16%). Moreover, 98.4% of expansion was over existent agricultural areas. Considering all the land use changes within sugarcane-producing rural properties, the net removal is even larger, of 17 TgCO2∙yr−1, which is due to vegetation recovery. This suggests that public policies and private control mechanisms might have been effective not only to control deforestation but also to induce carbon removals associated with sugarcane cultivation. These results indicate sugarcane production system and derived products as contributors to net carbon removals in the land sector in Brazil and should be considered for both bioenergy and agricultural sustainability evaluation. [ABSTRACT FROM AUTHOR]

Farmers may promote the cultivation of under-sown cover crops (CCs) in various ways without jeopardizing the yield of a cash crop. With this survey, we aimed to understand how Finnish farmers manage possible challenges with under-sown CCs. A farmer survey was carried out in 2021. We invited 6493 farmers who had selected CCs as a registered measure to answer a questionnaire with 20 statements (a Likert scale), and 1130 responded (17.4%). A Cochran–Mantel–Haenszel test was used to measure the strength of the association between 11 farm/farmer characteristics of the respondents and 20 statements. Responses indicated that farmers often took under-sown CCs into account during the growing season. Sowing was considered an especially critical measure and the CC seeding rate was often assessed with a test run before sowing. Thirty-nine per cent of the respondents had made investments, most often to facilitate sowing. The farmers usually adjusted the fertilizer rate only according to the cash crop. Early harvesting of a cash crop was considered important by 58% of farmers to ensure that the CCs do not hamper the harvest. Farmers harvested cash crops as soon as they matured and were harvestable, though they had mixed views on whether CCs impacted the quality of the cash crop. Subsidized, investing farmers were likely to be oriented towards the benefits from the ecosystem services provided by CCs. Their experiences should be shared among the farming community to support the large-scale implementation of CCs. [ABSTRACT FROM AUTHOR]

Asiatic Black Bear attacks are reported rarely throughout the majority of their global range; however, this has not been the case in the Kashmir Valley where over the past 20-30 years attacks have been relatively common. There are several causes for the high number of attacks, though the foremost reason likely stems from the conversion of natural habitat to orchards and agricultural fields. Asiatic Black Bears actively crop raid orchards and agricultural areas putting them into close proximity to humans. The Jammu & Kashmir Wildlife Protection Department has collected data on 2,357 Asiatic Black Bear attacks in the Kashmir Valley between 2000 and 2020. A total of 2,243 (95.2%) resulted in injury and 114 (4.8%) resulted in death. The majority of injuries were reported as minor (57.4%, n=1126), 42.4% (n=832) as grievous, and 1.2% (n=21) caused permanent disability. The highest proportion of attacks occurred from July through November, coinciding with the harvesting season, and the least occurred from December through March, coinciding with when most Asiatic Black Bears are hibernating. Victims of bear attacks were most often working in farms or orchards, and were mostly between the ages of 31-40 years old. Most attacks occurred in the morning hours when people first entered the orchards or agricultural fields to work. The frequency of attacks has declined since 2016, which could be attributed to retaliation killings, better management by the wildlife department, and the engagement of NGO's with local people to create bear awareness and teach safety measures. [ABSTRACT FROM AUTHOR]

Currently, an agricultural method called SynecocultureTM has been receiving attention as a means for multiple crop production and recovering from environmental degradation; it helps in regreening the environment and establishing an augmented ecosystem with high biodiversity. In this method, several types of plants are grown densely, and their management relies mainly on manual labor, since conventional agricultural machines and robots cannot be applied in complex vegetation. To improve work efficiency and boost regreening by scaling-up Synecoculture, we developed a robot that can sow, prune, and harvest in dense and diverse vegetation that grows under solar panels, towards the achievement of compatibility between food and energy production on a large scale. We adopted a four-wheel mechanism with sufficient ability to move on uneven terrain, and a two orthogonal axes mechanism with adjusted tool positioning while performing management tasks. In the field experiment, the robot could move straight on shelving slopes and overcome obstacles, such as small steps and weeds, and succeeded in harvesting and weeding with human operation, using the tool maneuver mechanism based on the recognition of the field situation through camera image. [ABSTRACT FROM AUTHOR]

Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ) color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost. [ABSTRACT FROM AUTHOR]

Seed yield and weed infestation of pea as well as soil properties were evaluated in the systems of conventional (TA) and conservation (CA) agriculture. In both agricultural systems, pea was grown in crop rotation: potato - winter wheat - pea - winter barley. Shallow ploughing (10-12 cm) after previous crop harvest and pre-winter ploughing (20-25 cm) were performed, whereas a pre-sowing cultivation set was deployed in the springtime in TA. In CA, glyphosate was applied after previous crop harvest, and post-harvest residues were left on the filed surface (4.5 t ha-1). A cultivation-sowing set was used in the springtime, and pea was sown at the beginning of April. The study demonstrated that the agricultural systems tested had no significant effect on pea seed yield. A higher number and air-dry weight of weeds, and a higher weed species number were demonstrated in TA than in CA. Also, a higher number and air-dry weight of weeds were recorded in 2020 than in the other study years. Contents of organic C and total N in the soil and the number of earthworms were higher in CA than in TA. [ABSTRACT FROM AUTHOR]

The performance of agricultural machines depends on the reliability of the machinery used, the operating environment, the maintenance efficiency, the operating process, the technical expertise of the farmers, etc. As the size and complexity of farm equipment continue to increase, the implications of equipment failure become over more critical. Therefore, reliability analysis is required to identify the bottlenecks in the system and to find the components or subsystems with low reliability for a given designed performance. It is important to select a suitable method for data collection as well as for reliability analysis. This paper presents a case study describing reliability and availability analysis of the sugarcane 7000 series chopper harvester at Hakim Farabi agro- industry in Iran. In this study, the harvester is divided into nine subsystems. The parameters of some probability distributions, such as weibull, exponential and lognormal distributions have been estimated by using ReliaSoft Weibull++6 software. The results of the analysis show that feed rollers and hydraulic subsystems are critical in reliability point, and the wheels subsystem and hydraulic subsystem are critical in an availability point of view. The study also shows that the reliability analysis is very useful for deciding maintenance intervals. [ABSTRACT FROM AUTHOR]

This paper studied the effects of food extruder die dimensions on the extrudate expansion indices using twin-screw extruder. The extruder has 59.6 mm screw diameter and screw L/D ratio of 20. The die dimensions considered are die length, die diameter and die temperature. The feed material used is yellow corn flour. Dies with diameter ranging from 2.5 - 5.0 mm were used to determine the effect of die nozzle diameter on extrudate expansion. Results obtained showed that radial expansion of the extrudate decreased with increasing die diameter. An average reduction of about 42 % in radial expansion was recorded for a difference of 2 mm in die diameter. The overall expansion of the extrudate was not affected by changes in die diameter for all the diameters and lengths of die studied. On the contrary, increase in die diameter caused increase in the longitudinal expansion of the extrudate. The longitudinal (axial) expansion obtained with a 3-mm die diameter was 0.6, which increased to 1.2 for a 4-mm die diameter. The regression model shows that specific mechanical energy [SME] decreased with increasing die diameter. Dies with length ranging from 16 to 54 mm with a constant nozzle diameter of 3 mm were used to determine the effects of die nozzle length on extrudate expansion. The die length significantly influenced axial expansion of extrudate. Axial expansion increased with increase in die length. A linear regression model was developed to relate die length to extruder SME. The quadratic effect is negligible in this case and as such was not included in the regression equation. Effects of die nozzle L/D on radial expansion of extrudate during extrusion were also studied. There was an initial sharp increase in the radial expansion of the extrudate from 8.9 to 13 as the nozzle L/D ratio increased from 5.33 to 6.4. The value then dropped to 10.5. At higher L/D ratio (above 6.4), the expansion decreased. At this high L/D ratio corresponding to a high extrusion pressure, the residence times and shear rates were high enough to cleave the starch molecules and thus reduce expansion. Experimental results on the effects of die diameter and die length on flow characteristics revealed that when all other extruder conditions are kept constant, increasing the diameter of the die nozzle causes increase in the volumetric flow rate of extrudate. Flow rate decreased proportionally with increasing die length. [ABSTRACT FROM AUTHOR]

The present study focused on the development, optimization, and performance evaluation of a harvesting robot for heavyweight agricultural products. The main objective of developing this system is to improve the harvesting process of the mentioned crops. The pumpkin was selected as a heavyweight target crop for this study. The main components of the robot consist of mobile platforms (the main robot tractor and a parallel robot tractor), a manipulation system and its end-effector, and an integrated control unit. The development procedure was divided into four stages: stage I (designed system using Solidworks), stage II (installation of the developed system on a temporary platform), stage III (developed system on an RT-1 (Yanmar EG453)), and stage IV (developed system on an RT-2 (Yanmar YT5113)). Various indicators related to the performance of the robot were evaluated. The accuracy of 5.8 and 4.78 mm in x and y directions and repeatability of 5.11 mm were observed. The harvesting success rate of 87~92%, and damage rate of 5% resulted in the evaluation of the final version. The average cycle time was 35.1 s, 42.6 s, and 43.2 s for stages II, III, and IV, respectively. The performance evaluations showed that the system's indicators are good enough to harvest big-sized and heavy-weighted crops. Development of the unique and unified system, including a mobile platform, a manipulation system, an end-effector, and an integrated algorithm, completed the targeted harvesting process appropriately. The system can increase the speed and improve the harvesting process because it can work all day long, has a precise robotic manipulation and end-effector, and a programmable controlling system that can work autonomously. [ABSTRACT FROM AUTHOR]