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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]

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

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]

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]

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]

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]

Copyright of INMATEH - Agricultural Engineering is the property of INMATEH - Agricultural Engineering 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.)

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]

Copyright of Energy Policy Journal / Polityka Energetyczna is the property of Mineral & Energy Economy Research Institute of the Polish Academy of Sciences 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.)

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]

An editorial is presented on the theoretical and methodological contribution to the field of writing for scholarly publication. Topics include the Covid-19 pandemic and has already changed many aspects of our social life; the digitalization of research, writing, and publishing provides academics with various affordances in terms of data harvesting and management, innovative and alternative genres that can materialize disciplinary discourses.

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]

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]

In the last few years, robotics has been increasingly adopted in agriculture to improve productivity and efficiency. This paper presents recent and current work at the Politecnico of Bari, in collaboration with the University of Lecce, in the field of agriculture robotics. A cost effective robotic arm is introduced for the harvesting of radicchio, which employs visual localization of the plants in the field. The proposed harvester is composed of a double four-bar linkage manipulator and a special gripper, which fulfills the requirement for a plant cut approximately 10 mm underground. Both manipulator and end-effector are pneumatically actuated, and the gripper works with flexible pneumatic muscles. The system employs computer vision to localize the plants in the field based on intelligent color filtering and morphological operations; we call this algorithm the radicchio visual localization (RVL). Details are provided for the functional and executive design of the robotic arm and its control system. Experimental results are discussed; obtained with a prototype operating in a laboratory testbed showing the feasibility of the system in localizing and harvesting radicchio plants. The performance of the RVL is analyzed in terms of accuracy, robustness to noises, and variations in lighting, and is also validated in field experiments. © 2006 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Purpose -- The purpose of this paper is to analyze the efficiency of temperature-based weather derivatives (WD) in reducing risk exposure for Chinese agriculture industry. Therefore, a put option with cumulated growing degree days as its underlying index is assumed to be bought by farmers as a risk management instrument to prevent income fluctuations from adverse temperature conditions. Design/methodology/approach -- The objective of this paper is to analyze the efficiency of temperature-based WD in reducing risk exposure for Chinese agriculture industry. Therefore, a put option with cumulated growing degree days as its underlying index is assumed to be bought by farmers as a risk management instrument to prevent income fluctuations from adverse temperature conditions. Findings -- The results of the efficiency tests show that temperature-based put options are efficient in offsetting yield shortfalls for rice and wheat in China. The weather-yield models have a high prediction power in explaining yield variation by temperature. Research limitations/implications -- The de-trending procedure for the weather-yield model should be improved to distinguish better between technology progress, human activities and influence of weather. Further, more advanced models could be used for the pricing. Practical implications -- The findings of the paper support the launch of WD as an efficient risk management tool for agriculture in China. Compared with traditional damage-based insurance, WD are more flexible, have lower transactions costs and avoid moral hazard or adverse selection. Originality/value -- The efficiency problem of WD has not been analyzed sufficiently worldwide and especially not for developing countries like China where a large proportion of the population works as farmers. This paper supports to fill this gap. [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]

Agriculture provides an unique opportunity for the development of robotic systems; robots must be developed which can operate in harsh conditions and in highly uncertain and unknown environments. One particular challenge is performing manipulation for autonomous robotic harvesting. This paper describes recent and current work to automate the harvesting of iceberg lettuce. Unlike many other produce, iceberg is challenging to harvest as the crop is easily damaged by handling and is very hard to detect visually. A platform called Vegebot has been developed to enable the iterative development and field testing of the solution, which comprises of a vision system, custom end effector and software. To address the harvesting challenges posed by iceberg lettuce a bespoke vision and learning system has been developed which uses two integrated convolutional neural networks to achieve classification and localization. A custom end effector has been developed to allow damage free harvesting. To allow this end effector to achieve repeatable and consistent harvesting, a control method using force feedback allows detection of the ground. The system has been tested in the field, with experimental evidence gained which demonstrates the success of the vision system to localize and classify the lettuce, and the full integrated system to harvest lettuce. This study demonstrates how existing state‐of‐the art vision approaches can be applied to agricultural robotics, and mechanical systems can be developed which leverage the environmental constraints imposed in such environments. [ABSTRACT FROM AUTHOR]

This paper considers the first steps in some of the production chains of kenaf and fiber sorghum. Effects of agronomic techniques (such as crop density, sowing and harvesting period, and variety) are evaluated. Their influence on biometric characteristics of the crops is studied on the basis of harvesting and first transformation machines. Effects of the interaction between agronomic techniques and machines are considered with respect to the qualitative and quantitative characteristics of the product obtained. [ABSTRACT FROM AUTHOR]

• This study proposed detection and localization methods based on deep learning and active sensing for harvesting robots in real-world environments with occlusion and varying lighting conditions. • To solve the picking problem caused by the peduncle's random tilting, this study proposed a method to calculate the peduncle's inclination angle for controlling the end-effector to make the corresponding rotation. • Experimental results indicated the error between the estimated occlusion ratio and the genuine value is 16% in average, and the active sensing method improved the confidence score in occluded situations by over 50%. And the overall successful picking rate of 300 trials is 90%. • The proposed active methods have a 33% increase in precision and a 43% increase in efficiency compared to constant methods. The predominance of branch and leaf shade in agricultural environments presents a barrier for accurate target recognition. Particularly for picking robots, precise localization of the picking object is essential. For this purpose, this paper proposes detection and localization methods based on deep learning and active sensing for harvesting robots in real-world environments with occlusion and varying lighting conditions. Using a deep learning network, the detection method firstly extracts the peduncle and fruit regions; the fruit region is then used to calculate the occlusion rate and the offset distance of the peduncle relative to the fruit. With such information, the robot arm adjusts the camera's field of view to perform multiple recognitions until the confidence is satisfied. Furthermore, to solve the picking problem caused by the peduncle's random tilting, this paper proposes a method to calculate the peduncle's tilt angle for controlling the end-effector to make the corresponding angle rotation. The robot arm and its end-effector are directed to complete the harvesting with the picking point location and tilt angle. In this study, data collection, detection and picking tests were implemented in the field, the results indicated that the method obtained an average successful picking rate of 90% after 300 trials, the error between the estimated occlusion ratio and the genuine value is 16% in average, and the active sensing method has improved the confidence score in occluded situations by over 50%. The proposed active methods have a 33% increase in precision and a 43% increase in efficiency compared to constant methods. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering 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.)

The agriculture sector still lacks the tools and models to enhance the utilization of different resources. This paper addresses the vineyard harvesting problem in developing countries, with the objective of optimizing the wine quality and minimizing the operational costs. Heuristics were introduced to better assign the harvesting days to the different grape blocks that exist in the vineyard's field. The quality of the grapes was a key target as it can transform production from a pinnacle wine to a bulk one. We solved several numerical examples for verification and demonstrative purposes and found that our proposed approach finds solutions that significantly reduce the harvesting costs in the vineyard and considerably outperform Branch and Bound algorithm especially for large problems. [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]

The harvesting of renewable resources from an operations scheduling viewpoint is introduced, and a harvesting scenario arising in the agricultural context involving a commercial contracting enterprise that travels from farm-to-farm harvesting crops is discussed. This paper is an extension of previous work by two of the authors from the one-farm to the multi-farm case. In both cases, the duration of each operation is dependent upon the combination of constrained resources allocated to it, equipment and worker allocation is restricted, and minimum or maximum time lags on the start and completion of operations may be imposed. The present case incorporates harvesting at more than one farm and thus the sequence in which the farms are visited and the inter-farm travel times must be taken into account. We report on a harvesting scheduling model and solution procedures designed specifically for large-scale versions of the multi-farm case. The computational times experienced in solving general instances of the model of small-to-medium practical size by a commercial integer programming package are encouraging. Greedy and tabu search heuristics, which are capable of solving problems of relatively large dimensions in reasonable computational time are also included. The authors believe that the model and the solution techniques developed represent a useful addition to the farm crop contractor's tool kit. [ABSTRACT FROM AUTHOR]

The expected growth of hemp cultivation world wide for industrial purposes will clearly cause a growing demand for sowing material. Hence, there is a need for the development of a new, more efficient method for harvesting hemp seeds. The technology described in this paper, elaborated at the Institute of Natural Fibres (INF), presents the new concept of a panicle cutting device that involves mowing and cutting stems into smaller sections. The cut-off panicles are collected on a trailer hooked behind the mower. The panicles harvested during the flowering phase are used for the production of essential oils, while those harvested in full maturity are the source for sowing seed. In the latter case, the panicles—before ginning—must be initially dried in conditions allowing for preservation of good germination capacity. The seed must be cleaned immediately and dried further to 12% moisture content and protected with fungicidal treatments. After mowing and cutting stems are swathed in the field and left for retting or collected—after drying— using agricultural presses and then decorticated. [ABSTRACT FROM PUBLISHER]

Modern computerized energy systems are necessary in economically sustainable wood procurement. Previous studies have shown that productivity variation increases for larger stump diameters of the lifted stumps and the changes can be either negative or positive. For this reason, new studies on stump harvesting should focus on robust productivity models for the computerized energy systems, which will provide economically sustainable stump procurement. This paper presents an estimation of two different productivity models using polynomial regression based on the machine, time, soil, stand, and biomass data. An unconstrained polynomial model yielded slightly decreasing productivity for diameters above 50 cm. Therefore, we fitted a constrained model that provided increased productivity for larger stumps up to 70 cm diameter. It is better justified by the theory, and the evidence against the constrained model was only slight (p > .05) within the diameter range of our data. Based on our comparison of the productivity measurements, the best practice model is discussed to enable an efficient computerized energy system for sustainable stump procurement. [ABSTRACT FROM PUBLISHER]

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]

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]

In the article the technique, which in practice will solve the problem of vector optimization process of harvesting and transport complex in a limited number of statistics and to identify key relationships and regularities process for collecting and transporting the crop. In Article proposals technique kotoraja pozvolyt in practice Reshat task vektornoy process optimization work uborochno transport complex at ohranychennom Quantity statystycheskyh data, as well as vыyavyt Basic vzaymosvyazy and zakonomernosty tehnolohycheskoho process for Other cleaning and transportyrovke harvest. The paper proposes a method that allows in practice to solve the problem of vector optimization process work harvesting and transport complex with a limited amount of statistical data and to identify the basic relationships and patterns of the process for harvesting and transporting crops. [ABSTRACT FROM AUTHOR]

The UK has had a Temporary Migrant Worker Programme (TMWP) for agricultural ‘guestworkers’ since 1943. Most recently referred to as the Seasonal Agricultural Workers Scheme (SAWS), SAWS accommodated 25,000 workers per annum by its 2004 peak. However, the UK government then announced the scheme's closure (initially for 2011, but then delayed until 2014). This paper examines employers' response to this closure and, specifically, juxtaposes the academic critiques of TMWPs with the very strong employer preference for them. This preference, the paper concludes, is about the way in which TMWPs allow labour to be more readily and more extensively controlled, and, also allow employers access to ‘better quality’ workers. Considering these benefits of quality and control, alongside the academic critiques, the paper concludes that SAWS should be retained, but with major changes and safeguards. [ABSTRACT FROM AUTHOR]

Burning is one of the most widely used methods for removing crop residues during harvest seasons. It cleans fields faster and costs less in comparison to other residue removal methods. Agricultural burning, however, has been recently limited or banned during harvest seasons in China, mainly due to the air quality and human health concerns raised from its use. This paper reviews recent studies on the burning of agricultural landscapes in China to understand the natural (environmental and ecological) and human (economic and social) impacts and identifies uncertainties, gaps, and future research needs. The total annual crop straw output in China is more than 600 billon kg, with about 110, 130, and 230 billion kg coming from rice, wheat, and corn, respectively. Agricultural burning removes about one-fourth of total crop straw and emits about 140-240, 1.6-2.2, and 0.5-0.14 billion kg of CO, PM, and black carbon, respectively. Agricultural burning accounts for upto half of the total PM concentrations in the major burning regions during harvesting periods. Burning emissions contribute to regional haze and smog events. Therefore, limiting or banning agricultural burning is a necessary measure for reducing air pollution in China. The estimations of total burned crop straw amounts and emission factors are the major uncertainty sources for emission estimates. More studies are needed to better describe the smoke plume rise, dispersion, and interactions with weather and climate and to simulate the ecological impacts of agricultural burning. Effective alternatives need to be explored in order to provide solutions for farmers to remove agricultural residues in the wake of the burning ban. [ABSTRACT FROM AUTHOR]

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• Novel tendon-driven continuum manipulator (TDCM) is presented, that manipulate distal tip in 3D using planar geometry. • The proposed design uses decoupled tendon routing and avoids tendon interference. • The proposed manipulator offers an expanded workspace (∼ 22 % more) than traditional TDCMs. • Kinematic and Kineto-static models are presented considering external uncertain forces. • Calibration and experimental results validated the proposed design. Continuum Manipulators (CMs) have gained popularity as a promising solution for exploring unstructured environments due to their dexterity, compliance, and redundancy. Studies on CMs have focused individually on planar and spatial configurations, ranging from single to multi-section constructions. Planar CMs are easier to design, have simpler kinematics equations and possesses less number of tendons with reduced dexterity, redundancy, and reach. Whereas spatial CMs, offer a larger workspace with improved dexterity and redundancy while computationally expensive. This paper introduces a novel two-section tendon-driven continuum manipulator (TDCM) design that uses two planar sections to manipulate the distal end of the manipulator in three-dimensional space to handle external interaction from the environment. The proposed solution offers improved performance in terms of handling payloads and tip deflection under external interactions. This design is intended for agricultural applications such as vegetable harvesting and requires larger workspace and fewer actuators. We demonstrate the effectiveness of our design through kinematic and static analysis. The kineto-static model estimates the profile of the proposed TDCM under external interaction acting at the manipulator's distal end. Finally, the proposed design is experimentally validated by an in-housed prototype of an two-section TDCM, actuated by two tendons/section. [ABSTRACT FROM AUTHOR]

• Systematically summarize the hardware platform technology of different robot vision systems and commercial application. • Analyze the development of target recognition and positioning technology from two aspects: sensors and algorithms. • Discuss the challenges encountered by robots in harvesting operations, and describe the future trends. Fruit harvesting robot operations in unstructured agricultural environments have been facing both accuracy and speed challenges, and vision-based control plays an important role in autonomous harvesting. Visual recognition and harvest positioning technology determine whether the target information can be obtained quickly and accurately and whether fruit picking can be carried out efficiently and orderly. It includes not only the hardware platform technology of the vision system, but also different sensor technologies and the optimization of recognition and positioning algorithms. This paper reviews the application of vision recognition and harvesting localization technologies in fruit harvesting robots, starting with an overview of hardware platform technologies for different vision systems of robots, followed by an analysis of the development of target recognition and positioning technologies in terms of sensor hardware and algorithm design, respectively. In addition, the review discusses the difficulties and challenges encountered by robots in harvesting operations and describes the future trends of vision localization technologies. [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]