Shortage Of Agricultural Labour Research Articles

Key Technologies for Autonomous Fruit- and Vegetable-Picking Robots: A Review

With the rapid pace of urbanization, a significant number of rural laborers are migrating to cities, leading to a severe shortage of agricultural labor. Consequently, the modernization of agriculture has become a priority. Autonomous picking robots represent a crucial component of agricultural technological innovation, and their development drives progress across the entire agricultural sector. This paper reviews the current state of research on fruit- and vegetable-picking robots, focusing on key aspects such as the vision system sensors, target detection, localization, and the design of end-effectors. Commonly used target recognition algorithms, including image segmentation and deep learning-based neural networks, are introduced. The challenges of target recognition and localization in complex environments, such as those caused by branch and leaf obstruction, fruit overlap, and oscillation in natural settings, are analyzed. Additionally, the characteristics of the three main types of end-effectors—clamping, suction, and cutting—are discussed, along with an analysis of the advantages and disadvantages of each design. The limitations of current agricultural picking robots are summarized, taking into account the complexity of operation, research and development costs, as well as the efficiency and speed of picking. Finally, the paper offers a perspective on the future of picking robots, addressing aspects such as environmental adaptability, functional diversity, innovation and technological convergence, as well as policy and farm management.

Efficient occlusion avoidance based on active deep sensing for harvesting robots

With the increasing shortage of agricultural labor, the development of harvesting robots is becoming more and more urgent. Most of them require vision to locate the target, however, occlusion is common in agricultural environment, which restricts the accuracy of visual target recognition, and even leads to failure in serious cases. The active perception method is an effective means, but how to efficiently find the best observation position remains difficult to avoid the waste of time caused by repeated invalid motion. Targeting these problems, an active deep sensing method is proposed for harvesting clustered and single fruits. First, the region of interest of the target is extracted by a segmentation network, and then the occlusion status of it is obtained by image processing methods. Taking the current observation position as the starting point, the camera is moved within a matrix to form confidence and occlusion rate distribution maps. After establishing a series of occlusion rate and confidence matrix datasets, a designed deep network has been trained, which is used to predict the maximum confidence/minimum occlusion rate position after the current occlusion status is estimated. To verify the reliability of the method, laboratory and field experiments were carried out for apples and clustered tomatoes. After 1000 times of verification, results show that the successful pick/recognition rate is increased by 38.7 %, and the average successful recognition time is 5.2 s, which is 63.1 % and 46.4 % faster than that of a fixed movement method and a simple heuristic method.

Factors Affecting Landowners’ Willingness to Sustain Hiring Foreign Farmworkers: The Case of Banana Producers in Mersin Province, Turkey

Turkey is a country with a migration route throughout history and has provided protection to more refugees than any other country. But, the secure deportation of refugees is stated by government officials. This research aims to determine the socio-economic characteristics of landowners who hire foreign farmworkers and the factors affecting their willingness to employ foreign farmworkers. In order to meet the objective, a survey study based on face-to-face interviews was conducted with 200 landowners producing bananas and employing foreign farmworkers in Mersin. The level of willingness to hire foreign farmworkers was calculated, and the ordinal logistic regression (OLR) model was executed to determine affective factors. According to the results, the landowners’ average age was 40.5 years, and their agricultural experience was 11.07 years, with 47% of them having a primary school degree. A total of 50% of the landowner farmers had a high willingness level, 29.5% had a medium willingness level, and 20.5% had a low willingness level. As for OLR, the factors of land size, continuity status of farmworkers, and the reasons to hire foreign farmworkers were significant (p < 0.05). Contrary to the general opinion, which is that the reason for the willingness to hire foreign farmworkers is to use them as a cheap labor force, this research found that the reason with the greatest impact is that landowner farmers have difficulty in finding local farmworkers. The shortage of agricultural labor has been filled with foreign farmworkers in Mersin province. Every foreign farmworker may be registered with one or more agricultural holdings so that the deportation of refugees does not cause a labor supply problem in agriculture; also, citizens seeking employment from other regions of Turkey may be supported to be farmworkers in regions with labor shortages. In order to avoid supply security problems in agricultural production, policymakers should urgently develop policies based on field data.

Understanding cropland abandonment from economics within a representative village and its empirical analysis in Chinese mountainous areas

China has been experiencing "Lewis Turning-Point", which means rural labor has been continuously transmitting to manufacturing sector and urban areas, ended up with old or female labor left in agricultural sector. One immediate result from agricultural labor shortage, some pieces of farmland will be abandoned, especially in mountainous area where large-farming-machinery cannot be applied due to terrain restrictions, and labor loss from agriculture cannot be supplemented. In this context, understanding land abandonment is critical for land planning and management. Specifically, it is necessary to figure out which cropland is more likely to be abandoned? To what scale of the land will be abandoned in the future? This article aims to figure out the basic rules behind land use or abandonment decision from a perspective of economics. In China, village is the basic unit for land use and land use rights transferring, thus we introduced a representative village, featured by an average farmland size with distribution pattern following questionnaire survey carried out in Wulong district, Chongqing city. We developed a theoretical land use decision-making model with its objective of maximizing land use incomes among ‘representative village’. This model is employed to simulate land abandonment rate in the year of 2011 and 2020 by changing the external variables given by the market survey, with a result of 9.25% and 33.40% respectively. The simulated land abandonment rate shows only 3.84 percentage point difference with the official data, which certified that this model is reasonable and applicable for land use decision in mountain areas in China. The model also shows a rise of labor wage will lead to huge land abandonment. To counter land abandonment, government should take some measures, such as rise of agricultural product sale price or decline the chemical fertilizer price, to alleviate land abandonment effectively.

Design and Implementation of an Artificial Intelligence of Things-Based Autonomous Mobile Robot System for Pitaya Harvesting

Since aging in agriculture has currently become a problem in many advanced countries worldwide, in response to the growing shortage of agricultural labor, many of these countries have invested in the development of agricultural robots to solve the agricultural labor shortage problem. Automatic equipment to support agricultural harvesting can reduce the labor demand. As a result, this article proposes an artificial intelligence of things (AIoT)-based autonomous mobile robot (AMR) system for pitaya harvesting. The proposed system uses an artificial intelligence (AI) edge computing-based development board (NVIDIA Jetson Nano development board) and combines a 2-D simultaneous localization and mapping (SLAM) algorithm and an AI object recognition module. The SLAM algorithm is used for environmental detection in an unknown environment, and surrounding environment information can be detected by the sensor for map construction to facilitate robot navigation in pitaya orchards. In addition, this article describes an AI object recognition module used for pitaya recognition to facilitate pitaya harvesting. The accuracy of the proposed pitaya recognition model can reach 96.7% on the adopted NVIDIA Jetson Nano development board. A pitaya orchard is simulated in the experimental environment discussed in this article. In the simulated experimental environment, the proposed AMR system can achieve efficient pitaya harvesting and realize intelligent farming.

확률 비용 맵을 이용한 수확 로봇의 경로 계획 알고리즘

Due to the increasing global demand for food and shortage of agricultural labor, researchers have focused on developing new technologies, capable of increasing production yields and farming efficiency. Since the repetitive harvesting task is labor-intensive, a robotic harvesting system is one of the promising solutions. To enable precise robotic harvesting, it is important to determine a path for the robot that avoids damaging the crops and other obstacles such as unripe crops, leaves, and stems. In this paper, we present a probabilistic costmap classified according to obstacle type and use it in an RRT-star path planning algorithm. The proposed path planning algorithm has two main features: a biased sampling method based on a probabilistic costmap and a costmap-based collision detector. We have validated the proposed path planning method in the scenario of cucumber harvesting tasks using a two-dimensional planar robot.

Tomato detection based on convolutional neural network for robotic application

AbstractThe development of agricultural robots and the promotion of agricultural production automation are important means to alleviate the shortage of agricultural labor. Fruit and vegetable detection is a prerequisite for accurate harvesting by robots. It directly determines the efficiency and quality of harvesting operations. In order to meet the requirements of target positioning and recognition of tomato harvesting robots, this paper studies tomato recognition technology based on YOLOv3 convolutional neural network algorithm. And the tomato detection process of the YOLOv3 model is presented. The YOLOv3 model training dataset is constructed based on greenhouse tomato plants. The test results show that the model based on the YOLOv3 convolutional neural network has a better detection effect.

Development of smart aquaculture farm management system using IoT and AI-based surrogate models

Due to low labor participation by young adults and an aging agricultural population, Taiwan and the rest of the world are facing labor shortages in agriculture, which will affect aquaculture production. The proposed system is intended primarily for solving the problems faced by the aquaculture farming sector in Taiwan by designing a smart IoT-based fish monitoring and control system equipped with different IoT devices to enable real-time data collection; so that fishpond water-quality conditions and other system parameters can be readily monitored, adjusted, and assessed remotely. To predict the growth of the California Bass fish, this study also develops a deep learning model (DL) that correlates the different parameters of the smart aquaculture system. Bayesian optimization-based hyper-parameter tuning was employed to find the optimal DL model configuration to produce accurate predictions on the given experimental data set. The optimal model produces an R 2 value of 0.94 and a mean square error of 0.0015, demonstrating the applicability of the model to predict the desired output. Based on the results of the experiments, the DL model can be incorporated into the autonomous feeding system, reducing the amount of leftover feed. Thus, aquaculture based on the artificial intelligence of things (AIOT) can assist fish farmers in intelligently controlling and managing different fishpond equipment remotely and assist aquaculture operators in performing professional aquaculture, lowering the industry's entry barrier, and promoting aquaculture. • Developed an AIoT-based smart aquaculture farm management system that enables remote control and monitoring. • A DL model is developed to correlate the different parameters of a fish-feeding system and predict the growth of fish. • Bayesian optimization is utilized to find the most suitable DL model that will produce the highest R2 and mean square error.

Labor Structure, Land Fragmentation, and Land-Use Efficiency from the Perspective of Mediation Effect: Based on a Survey of Garlic Growers in Lanling, China

In the context of China’s agricultural labor shortage and the pressure of aging, this paper uses the land fragmentation index and the intermediary efficiency model to measure the degree of land fragmentation based on farmer-level data from the main garlic producing area in Lanling County in Shandong Province in 2020. The direct effect of labor structure on land-use efficiency and the mediating effect through land fragmentation are analyzed. The research results show that: (1) the average land-use efficiency of the sample farmers is relatively low; (2) the change in labor structure has an “inverted U”-shaped direct effect on land-use efficiency; and (3) the change in land fragmentation in the labor structure has a direct effect on land-use efficiency. The influence of land-use efficiency played a nonlinear mediating effect. The change in labor structure with the degree of land fragmentation showed an “inverted U”-shaped relationship, and the degree of land fragmentation and land-use efficiency had a “U”-shaped relationship. In order to improve land-use efficiency, two aspects of policy support should be increased: encouraging farmers to integrate land and supporting specialized and diversified planting.

CES 2022: Less is More

AbstractScaled‐down show still delivers top‐notch exhibits and innovation aplenty.

Rural Population Decline, Cultivated Land Expansion, and the Role of Land Transfers in the Farming-Pastoral Ecotone: A Case Study of Taibus, China

The decline and aging of the rural population in China has been an increasingly conspicuous problem in the past few decades and has been one of the fundamental drivers of cultivated land abandonment and loss. However, although they have endured similar rural demographic changes, some regions have experienced cultivated land expansion and the farming-pastoral ecotone is a typical one. Using Taibus as a case, this study aims to reveal the phenomenon of cultivated land expansion in the context of rural population decline and explore its underlying mechanism by addressing the role of cultivated land protection and land transfer policies. This study will also reveal the possible negative impacts and risks of cultivated land expansion. We found that 64.3% of the rural population in Taibus have migrated to other regions in 2020; however, cultivated land has increased by more than 10% in the past five years. Land transfer policies have helped to solve the agricultural labor shortage problem and increase household income, which encouraged the reclamation activities by rural households. However, under China’s land protection system, the central and the local governments have not enough incentives to prevent these reclamation activities. Cultivated land expansion in the farming-pastoral ecotone may lead to a series of negative impacts or risks, especially the overuse of groundwater resources and land desertification. Thus, we suggest that governments pay more attention to the phenomenon of cultivated land expansion and re-assess the cultivated land use policies in the farming-pastoral ecotone and other regions with similar contexts.

Farm labor productivity and the impact of mechanization

AbstractThere is a chronic shortage of agricultural labor in the US. Although growers increasingly turn to guest‐worker programs to meet their labor needs, few regard immigrant workers as a viable long‐term solution. Many producers of labor‐intensive agricultural commodities regard mechanization as a clear long‐term solution, making the slow rate of adoption of mechanized harvesting equipment in the US an empirical puzzle. In this article, we demonstrate that wage‐setting farmers have an incentive to “overmechanize,” or employ more than the cost‐minimizing level of capital when capital and labor are substitutes, but “undermechanize” when labor and capital are technical complements. This outcome can cause agricultural labor problems to persist under complementarity. To assess the potential role of farm under investment in labor augmenting capital equipment, we examine labor market outcomes following the adoption of non‐autonomous harvesting aids on a large strawberry farm in Central California. We develop an econometric model of peer‐affected productivity that controls for the group performance of farm workers operating in crews and find that mechanical aids complement labor in strawberry production, a finding that helps explain not only the relative lack of mechanized harvesting in strawberry production but, more generally, the persistent productivity gap in agricultural industries. We examine the broader implications of our theory for the slow rate of adoption of mechanical harvesting technologies in US agriculture by comparing general wage trends across several labor‐intensive and non‐labor‐intensive industries in California.

Manufacturing agricultural working knowledge: the scientific study of agricultural work in industrial Europe, 1920s–60s

AbstractAfter the First World War, agricultural work became a subject of intense interdisciplinary scientific inquiry. The shortage of agricultural labour, the nutritional and agricultural crises and the increasing significance of the movement for the rationalisation of work contributed to the creation of new scientific institutions that focused on the study and improvement of agricultural work. This contribution sketches the emergence and development of the science of agricultural work in Europe from the 1920s to the 1960s and explores the intellectual controversies sparked by the attempts to shape farm work along the model of industrial manufacturing. The frictions and tensions between industrial ideals and agricultural idiosyncrasies not only led to a new scientific treatment of agricultural work, but also created a field of contestation between different conceptual approaches to perceiving, analysing and transforming agricultural work in the age of industrial capitalism.

Strip planted mechanical seeding of mustard and mungbean with crop residue retention is more profitable than conventional practice

Smallholder agriculture of the Asian continent faces a significant challenge of declining soil productivity and an acute shortage of ag-ricultural labor. Reduced tillage and crop residue mulch application is an integrated approach to preserving soil health and addressing the labor crisis to maintain farm sustainability. We undertook this study to evaluate the effect of strip planting and increased residue mulching on mustard and mungbean's productivity and profitability in northern Bangladesh during 2015 and 2016. Mustard cv. BARI Sharisha 14 and mungbean cv. BARI Mungbean 6 was grown follow-ing (i) Conventionally Tilled broadcasting method (CT) and (ii) Strip Planted line seeding (SP) with two levels of crop mulch (i) no-mulch and (ii) 50% mulch. The CT was done by a two-wheel tractor with four plowings and cross plowing followed by leveling. In SP, single tillage, seeding, fertigation, and field leveling were done simultane-ously by a Versatile Multi-crop Planter machine. Results reveal that mechanized seeding of mustard and mungbean in SP with 50% resi-due mulching fetched 62% higher profit than broadcasted CT with-out residue through producing 24% higher seed yield. This practice reduced the land preparation costs by 68%, in association with re-ducing the labor and fuel requirements by 30%. Hence, it could be concluded that the mechanized seeding of mustard and mungbean with the retention of 50% of crop residue is profitable to the con-ventionally broadcasted seeding process.

From Smart Farming towards Unmanned Farms: A New Mode of Agricultural Production

Agriculture is the most important industry for human survival and solving the hunger problem worldwide. With the growth of the global population, the demand for food is increasing, which needs more agriculture labor. However, the number of people willing to engage in agricultural work is decreasing, causing a severe shortage of agricultural labor. Therefore, it is necessary to study the mode of agricultural production without labor force participation. With the rapid development of the Internet of Things, Big Data, artificial intelligence, robotics and fifth-generation (5G) communication technology, robots can replace humans in agricultural operations, thus enabling the establishment of unmanned farms in the near future. In this review, we have defined unmanned farms, introduced the framework of unmanned farms, analyzed the current state of the technology and how these technologies can be used in unmanned farms, and finally discuss all the technical challenges. We believe that this review will provide guidance for the development of unmanned farms and provide ideas for further investigation of these farms.

ТРЕНДИ СОЦІАЛЬНО-ЕКОНОМІЧНОГО РОЗВИТКУ СІЛЬСЬКОГО ГОСПОДАРСТВА В КОНТЕКСТІ ТЕОРІЇ БАГАТОФУНКЦІОНАЛЬНОСТІ

The article analyzes the current state and main trends of socio-economic development of agriculture in 1990-2018. In particular, the analysis of gross agricultural output of Ukraine (at constant prices in 2010) in all categories of farms, including agricultural enterprises, the dynamics of gross crop and livestock production in agricultural enterprises and its structure are studied. The main trends of agricultural development are identified and analyzed in the context of the theory of multifunctionality in conditions of uncertainty. The reasons for the reduction of the livestock industry, which is one of the components of sustainable development and allows a comprehensive solution to a number of economic, environmental and social problems. A thorough analysis of crop production by its main types in all categories of farms, the structure of sown areas and the level of crop yields in agricultural enterprises, analyzed the performance of its operation. As a result of low profitability, high material consumption of growing and harvesting, companies lost interest in the production of sugar beets and gradually began to prefer the cultivation of crops that are in demand in the market and profitable. This applies to sunflower and cereals and legumes. The level of staffing of enterprises is investigated. In particular, the dynamics of the population in Ukraine in 1990-2019, the employed population and employees in agriculture, forestry and fisheries of Ukraine are analyzed. Among the main factors that provoke labor shortages in agriculture are: the elimination of the livestock industry, which reduces the level of seasonality; lack of motivation, which is manifested mainly in low wages; remoteness of agricultural enterprises from large cities; migration abroad and others. Based on the analysis, it is determined that the increase in agricultural production at enterprises should be due to: comprehensive intensification of the industry, introduction of modern innovative technologies, adherence to scientifically sound crop rotations, livestock development, wage growth, increasing the prestige of the profession. This will not only contribute to the socio-economic development of agriculture, but also allow agricultural producers to become competitive and take a worthy place in the world market.

Reduction of soil weedseedbank with increased yield in dry direct-seeded rice through weed management

Rice cultivation always have a significant role in food and livelihood security. The predictions of increasing water deficiency under a changing climate and overcoming labor shortages in agriculture have brought a change in rice cultivation from conventionally flooded transplanting to direct-seeded rice (DSR) but weeds are the major production constrain in direct-seeded rice. Keeping these facts in view a field study was conducted during rainy season (Kharif), 2016 and 2017 at the Main Research Station, Hebbal, Bengaluru, India to study the effect of different weed management treatments in dry direct-seeded rice (upland condition) on growth, yield and soil weed seedbank as measured by emergence of weed seedlings. Among various weed management treatments, hand weeding at 20, 40 and 60 DAS recorded significantly highest paddy grain and straw yield in hand weeding at 20, 40 and 60 DAS (5.50 and 7.22 t/ha, respectively) and found at par with application of bensulfuron-methyl + pretilachlor as pre-emergence fb bispyribac-sodium (5.39 and 7.16 t/ha, respectively). Weedy check recorded significantly lowest yield (1.40 and 2.32 t/ha, respectively). At different intervals significantly the lowest weed seedlings emergence was noticed from the soils collected from different depths in hand weeded plots during both the years. Among various herbicide combinations, pre-emergence application of bensulfuron-methyl + pretilachlor followed by bispyribac-sodium recorded the lowest weed seedbank, as measured by germination of weed seeds and weed seedling emergence, followed by bensulfuron-methyl + pretilachlor and triafamone + ethoxysulfuron. Significantly the highest weed seedbank was noticed from soil collected from weedy check.

Welfare Employment and Its Impact on the Agricultural Sector Workforce in Trinidad, West Indies

The agricultural sector in Trinidad and Tobago is characterized by a labor shortage. A qualitative research design was used to investigate the impact of a national welfare employment program, on the agriculture labor sector. The study recruited n = 19 Community-Based Environmental Protection and Enhancement Program (CEPEP) employees, n = 10 farmers and n = 7 agricultural professionals for in-depth interviews and focus group sessions. A review was conducted of newspaper articles and national budget statements for content related to CEPEP and agriculture. A thematic analysis was conducted to establish themes from the data gathered from the participants and from the media review. The themes emerged were “CEPEP’s benefits to agriculture”, “Labor shortages in Agriculture”, “Convenience Employment” and “Challenges to CEPEP in Agriculture”. The study concludes that welfare employment can be incorporated into the development agenda for agriculture in Trinidad and Tobago once the issues of capacity building, retooling and mentorship, wage adjustments are factored into a structured program.

Performance Evaluation and Viability of a Pedestrian-type Low Land Cabbage Transplanter

This article reports on the performance and costs of owning and operating a pedestrian-type low land cabbage transplanting machine. The tests were carried out on tin-tailing soil at MARDI Kundang Research Station. They were undertaken in view of the need to mechanize low land cabbage transplanting operation due to shortage of agricultural labor. The machine performed satisfactorily when operated on planting bed with or without the existence of the plastic mulch. Cabbage seedlings required bigger holes on the planting bed which is layered with plastic mulch in order to avoid mortality of seedlings due to the heat absorbed by the plastic. Based on the performances test, the results showed that the field efficiency were 91.36% and 92.21% for with and without plastic mulch respectively on the planting bed. On average, 407s were recorded for the machine to transplant the seedlings along 100m planting bed. Compared with the traditional method, the transplanter could give 82-85% saving in labor required to plant the seedlings. The calculated break-even annual use for the implement was 33 ha/year. The implement was projected to be viable to use for 1600 h/year.Keywords: low land cabbage; transplanter; machine performance; work rate, viability

Weed Management in Dry Direct-Seeded Rice: A Review on Challenges and Opportunities for Sustainable Rice Production

Rice cultivation always remains significant for food and livelihood security. The predictions of increasing water deficiency under a changing climate and escalating labor shortages in agriculture have brought a paradigm swing in rice cultivation from conventionally flooded transplanting to direct-seeded rice (DSR). DSR cultivation can potentially address the concerns of diminishing natural resources and mounting production costs in the establishment of transplanted rice. The transition towards DSR saves water, reduces duration to maturity as well as labor required, and reduces negative environmental footprints. Despite all these recompenses, the potential yield losses through enormous weed menaces under DSR remains a challenge and may reduce yield by up to 50%. In this review, we examine the extent of weed infestation, weed shift and the losses in dry DSR (DDSR). Various regional and global scientific efforts made under DDSR have been assessed in the present and the smart weed-management strategies suggested can be adopted after scrutiny. Integration of different weed management approaches, namely prevention, cultural, mechanical, and chemical, have been discussed, which can pave the way for worldwide adoption of DDSR, especially in South Asia. In Asia, 22% of the acreage of total rice cultivation is under DSR and the region-specific integration of these weed-management approaches might reduce herbicide use in these areas by up to 50%.