A Review on Ergonomics in Agriculture. Part II: Mechanized Operations

Effects of an eight-week physical exercise program on low back pain and function in fruit workers: A randomized controlled trial

BACKGROUND

Low back pain is prevalent in workers' health and functional performance.

OBJECTIVE

To evaluate the effects of a physical exercise program on low back pain and disability in fruit workers.

METHODS

This randomized controlled trial assigned 44 workers (37 ± 9 years) to two groups. The experimental group consisted of 10 men and 12 women with an average age of 38 (± 9) years, and the control group consisted of 8 men and 14 women with an average age of 36 (± 10) years. The experimental group (EG) performed a program of strength and flexibility exercises for eight weeks, twice a week. The control group (CG) received minimal care, with a booklet with guidelines for performing exercises. The primary outcomes included changes in perceived disability and the intensity of pain evaluated by the Rolland-Morris questionnaire and the Numerical Pain Scale, respectively. All outcomes were measured at baseline and after eight weeks of intervention.

RESULTS

A significant difference was observed in the within-group analysis, with a mean reduction in pain intensity in the EG and CG of -4.55 (95%CI -7.01 to -2.09) and -3.81 (95%CI 1.72-5.90), respectively. For disability, a reduction of -4.45 (95% CI -8.89 to -0.02) was observed in the EG and of -4.43 (-7.38 to -1.48) in the CG. There were no significant differences in the between-groups analysis.

CONCLUSIONS

The exercise program was not superior to using the educational booklet. However, both interventions showed substantial decreases in pain and disability levels.

Sensors and Robotics for Digital Agriculture

The latest advances in innovative sensing and data technologies have led to an increasing implementation of autonomous systems in agricultural production processes [...].

Human-Robot Interaction in Agriculture: A Systematic Review

In the pursuit of optimizing the efficiency, flexibility, and adaptability of agricultural practices, human-robot interaction (HRI) has emerged in agriculture. Enabled by the ongoing advancement in information and communication technologies, this approach aspires to overcome the challenges originating from the inherent complex agricultural environments. Τhis paper systematically reviews the scholarly literature to capture the current progress and trends in this promising field as well as identify future research directions. It can be inferred that there is a growing interest in this field, which relies on combining perspectives from several disciplines to obtain a holistic understanding. The subject of the selected papers is mainly synergistic target detection, while simulation was the main methodology. Furthermore, melons, grapes, and strawberries were the crops with the highest interest for HRI applications. Finally, collaboration and cooperation were the most preferred interaction modes, with various levels of automation being examined. On all occasions, the synergy of humans and robots demonstrated the best results in terms of system performance, physical workload of workers, and time needed to execute the performed tasks. However, despite the associated progress, there is still a long way to go towards establishing viable, functional, and safe human-robot interactive systems.

Workload Assessment of Tractor Operations with Ergonomic Transducers and Machine Learning Techniques

Dynamic muscular workload assessments of tractor operators are rarely studied or documented, which is critical to improving their performance efficiency and safety. A study was conducted to assess and model dynamic load on muscles, physiological variations, and discomfort of the tractor operators arriving from the repeated clutch and brake operations using wearable non-invasive ergonomic transducers and data-run techniques. Nineteen licensed tractor operators operated three different tractor types of varying power ranges at three operating speeds (4-5 km/h), and on two common operating surfaces (tarmacadam and farm roads). During these operations, ergonomic transducers were utilized to capture the load on foot muscles (gastrocnemius right [GR] and soleus right [SR] for brake operation and gastrocnemius left [GL], and soleus left [SL] for clutch operation) using electromyography (EMG). Forces exerted by the feet during brake and clutch operations were measured using a custom-developed foot transducer. During the process, heart rate (HR) and oxygen consumption rates (OCR) were also measured using HR monitor and K4b2 systems, and energy expenditure rate (EER) was determined using empirical equation. Post-tractor operation cycle, an overall discomfort rating (ODR) for that operation was manually recorded on a 10-point psychophysical scale. EMG-based maximum volumetric contraction (%MVC) measurements revealed higher strain on GR (%MVC = 43%), GL (%MVC = 38%), and SR (%MVC = 41%) muscles which in normal conditions should be below 30%. The clutch and brake actuation forces were recorded in the ranges of 90-312 N and 105-332 N, respectively and were significantly affected by the operating speed, tractor type, and operating surface (p < 0.05). EERs of the operators were measured in the moderate-heavy to heavy ranges (9-24 kJ/min) during the course of trials, suggesting the need to refine existing clutch and brake system designs. Average operator ODR responses indicated 7.8% operations in light, 48.5% in light-moderate, 25.2% in moderate, 10.7% in moderate-high, and 4.9% operations in high discomfort categories. When evaluated for the possibility of minimizing the number of transducers for physical workload assessment, EER showed moderate-high correlations with the EMG signals (r_GR = 0.78, r_GL = 0.75, r_SR = 0.68, r_SL = 0.66). Similarly, actuation forces had higher correlations with EMG signals for all the selected muscles (r = 0.70-0.87), suggesting the use of simpler transducers for effective operator workload assessment. As a means to minimize subjectivity in ODR responses, machine learning algorithms, including K-nearest neighbor (KNN), random forest classifier (RFC), and support vector machine (SVM), predicted the ODR using body mass index (BMI), HR, EER, and EMG at high accuracies of 87-97%, with RFC being the most accurate. Such high-throughput and data-run ergonomic evaluations can be instrumental in reconsidering workplace designs and better fits for end-users in terms of agricultural tractors and machinery systems.

Musculoskeletal Symptoms and Assessment of Ergonomic Risk Factors on a Coffee Farm

In Honduras, some coffee farms must comply with strict standards of social, economic, and environmental sustainability, due to their organic, gender and fair-trade certifications. The principal research aim is to evaluate the musculoskeletal risks in occupations in a Honduran coffee farm certified in sustainable environments and to know the status of its workers within the farm. Musculoskeletal symptom perception during the last twelve months was consulted, assessing exposure to risk factors for work-related musculoskeletal disorders using the Quick Exposure Check method. Data regarding 48 workers were analyzed to provide the results. Within the body regions where discomfort is concentrated, the back, shoulders, wrists, knees, and feet stand out, and the highest risk exposures are presented for the coffee cutters at the neck level and in the wrist/hand segment, in the coffee pickers at the back, shoulder–arm segment, and wrist/hand segment, and in the processors in the back area and shoulder–arm segment. It is concluded that, in all the coffee fruit harvesting processes, the people who work in these jobs are exposed to ergonomic risks.

Prevalence and Factors Associated with Musculoskeletal Disorders among Thai Burley Tobacco Farmers

This cross-sectional analysis study aimed to identify the prevalence and factors associated with musculoskeletal disorders (MSDs) among Thai Burley tobacco farmers. Subjects included 603 burley tobacco farmers from Sukhothai province. Farmers were interviewed twice, (during planting and harvesting seasons), with a questionnaire consisting of demographic and health characteristics, musculoskeletal symptoms, and ergonomic exposure questions. The subjects average age was 49.5 years, more were female (58.5%), most had only a primary education (74.3%), 38% were overweight or obese. Farmers had a significantly higher prevalence of MSDs in the lower back (37.1%), knee (28.7%), shoulder (22.9%), wrist (19.9%), and hip (8.3%) during the harvesting season than in the planting season (p < 0.05). Models found that factors influencing MSDs prevalence during planting included long work hours in seedling, tasks such as topping tobacco plants, and using machine tools, after controlling for age, gender, and body mass index (BMI). While in the harvesting season, models found tasks conducted as a group had lower MSDs prevalence than individual work when carrying fresh tobacco to the barn, piercing/threading and curing the leaves, baling the bundles, and transporting the finished goods. We recommended working in groups to reduce workload and MSDs, especially during harvesting, in burley tobacco farming.

Proposing UGV and UAV Systems for 3D Mapping of Orchard Environments

During the last decades, consumer-grade RGB-D (red green blue-depth) cameras have gained popularity for several applications in agricultural environments. Interestingly, these cameras are used for spatial mapping that can serve for robot localization and navigation. Mapping the environment for targeted robotic applications in agricultural fields is a particularly challenging task, owing to the high spatial and temporal variability, the possible unfavorable light conditions, and the unpredictable nature of these environments. The aim of the present study was to investigate the use of RGB-D cameras and unmanned ground vehicle (UGV) for autonomously mapping the environment of commercial orchards as well as providing information about the tree height and canopy volume. The results from the ground-based mapping system were compared with the three-dimensional (3D) orthomosaics acquired by an unmanned aerial vehicle (UAV). Overall, both sensing methods led to similar height measurements, while the tree volume was more accurately calculated by RGB-D cameras, as the 3D point cloud captured by the ground system was far more detailed. Finally, fusion of the two datasets provided the most precise representation of the trees.

Impact of Epidemic-Affected Labor Shortage on Food Safety: A Chinese Scenario Analysis Using the CGE Model

Human food safety should be given priority during a major public health crisis. As the primary element of agricultural production, labor tends to suffer the most during a period of public health concern. Studying the impact of epidemic-affected labor shortages on agricultural production, trade, and prices has important implications for food security. This study used a calculable general equilibrium model to study the changes in agricultural production, trade, and prices under different labor damage scenarios. The results showed that agricultural production was less affected under a scenario where the epidemic was controlled locally. The output of agricultural products decreased by about 2.19%, and the prices of agricultural products increased slightly. However, the nationwide output of agricultural products decreased by only 0.1%, and the prices remained largely stable. In the case of the spread of the epidemic, the output of agricultural products in the epidemic area decreased by 2.11%, and the prices of certain agricultural products increased significantly. For example, the price of vegetables increased by 0.78%, the price of pork increased by about 0.7%, and those of agricultural products in other parts of the country also increased slightly. Compared with the national spread scenario, the local outbreak scenario had a smaller impact on Chinese food security, indicating Chinese effective policy against the epidemic. Although the impact of labor shortage under the influence of the epidemic on China was relatively limited, and considering its stable food security, we should pay attention to the increase in the process of agricultural products and changes in agricultural trade in the epidemic area. The residents in the epidemic areas could not effectively obtain nutritious food, which affected their health. Thus, the government should also completely mobilize agricultural resources to ensure the nutrition safety of residents during major public health incidents.

Evaluation of tractor driving vibration fatigue based on multiple physiological parameters

The vibration generated by tractor field operations will seriously affect the comfort and health of the driver. The low frequency vibration generated by the engine and ground excitation is similar to the natural frequency of human organs. Long term operation in this environment will resonate with the organs and affect drivers’ health. To investigate this possibility, in this paper we carried out a collection experiment of human physiological indicators relevant to vibration fatigue. Four physiological signals of surface electromyography, skin electricity, skin temperature, and photoplethysmography signal were collected while the subjects experienced vibration. Several features of physiological signals as well as the law of signal features changing with fatigue are studied. The test results show that with the increase of human fatigue, the overall physiological parameters show the following trends: The median frequency of the human body surface electromyography and the slope of skin surface temperature decreases, the value of skin conductivity and the mean value of the photoplethysmography signal increases. Furthermore, this paper proposes a vibration comfort evaluation method based on multiple physiological parameters of the human body. An artificial neural network model is trained with test samples, and the prediction accuracy rate reaches 88.9%. Finally, the vibration conditions are changed by the shock-absorbing suspension of a tractor, verifying the effectiveness of the physiological signal changing with the vibration of the human body. The established prediction model can also be used to objectively reflect the discomfort of the human body under different working conditions and provide a basis for structural design optimization.

Machine Learning in Agriculture: A Comprehensive Updated Review

The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords' combinations of "machine learning" along with "crop management", "water management", "soil management", and "livestock management", and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018-2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.

Human Activity Recognition through Recurrent Neural Networks for Human–Robot Interaction in Agriculture

The present study deals with human awareness, which is a very important aspect of human–robot interaction. This feature is particularly essential in agricultural environments, owing to the information-rich setup that they provide. The objective of this investigation was to recognize human activities associated with an envisioned synergistic task. In order to attain this goal, a data collection field experiment was designed that derived data from twenty healthy participants using five wearable sensors (embedded with tri-axial accelerometers, gyroscopes, and magnetometers) attached to them. The above task involved several sub-activities, which were carried out by agricultural workers in real field conditions, concerning load lifting and carrying. Subsequently, the obtained signals from on-body sensors were processed for noise-removal purposes and fed into a Long Short-Term Memory neural network, which is widely used in deep learning for feature recognition in time-dependent data sequences. The proposed methodology demonstrated considerable efficacy in predicting the defined sub-activities with an average accuracy of 85.6%. Moreover, the trained model properly classified the defined sub-activities in a range of 74.1–90.4% for precision and 71.0–96.9% for recall. It can be inferred that the combination of all sensors can achieve the highest accuracy in human activity recognition, as concluded from a comparative analysis for each sensor’s impact on the model’s performance. These results confirm the applicability of the proposed methodology for human awareness purposes in agricultural environments, while the dataset was made publicly available for future research.

Analysis of Metrological Requirements in Occupational Health and Safety Regulations Related to the Emerging Risk of Exposure to Vibrations

In occupational exposure to vibration, the risk assessment process is defined through a regulatory framework that presents some relevant metrological problems. This framework considers methods based on estimation and on measurements. Estimation methods could employ existing information that is provided for each manufacturer to each individual tool or application to carry out such estimation. The use of estimation methods has some problems, such as substantial uncertainty. When using measurement methods, some metrological aspects are not fully defined. Therefore, a new and emerging risk appears due to certain methodologic limitations. Consequently, the variation between the estimated and the actual values could overestimate the level of occupational exposure to vibrations. Thus, with this paper, a critical analysis of this emerging metrological problem is provided. For this, a critical analysis of the metrological requirements regarding European standards is developed. To this end, the estimation method and measure method are investigated, considering, in both cases, the main factors related to uncertainty, reliability, and traceability. With this structure, a set of metrological limitations have been identified, thus pointing towards future lines of research that allow the improvement of the process of assessing the level of occupational exposure to vibrations.

Agricultural Workforce Crisis in Light of the COVID-19 Pandemic

COVID-19 and the restrictive measures towards containing the spread of its infections have seriously affected the agricultural workforce and jeopardized food security. The present study aims at assessing the COVID-19 pandemic impacts on agricultural labor and suggesting strategies to mitigate them. To this end, after an introduction to the pandemic background, the negative consequences on agriculture and the existing mitigation policies, risks to the agricultural workers were benchmarked across the United States’ Standard Occupational Classification system. The individual tasks associated with each occupation in agricultural production were evaluated on the basis of potential COVID-19 infection risk. As criteria, the most prevalent virus transmission mechanisms were considered, namely the possibility of touching contaminated surfaces and the close proximity of workers. The higher risk occupations within the sector were identified, which facilitates the allocation of worker protection resources to the occupations where they are most needed. In particular, the results demonstrated that 50% of the agricultural workforce and 54% of the workers’ annual income are at moderate to high risk. As a consequence, a series of control measures need to be adopted so as to enhance the resilience and sustainability of the sector as well as protect farmers including physical distancing, hygiene practices, and personal protection equipment.