Medicolegal Implications of Deaths due to Agricultural Accidents.

ABSTRACT: Agriculture encompasses a variety of activities that carry with them a variety of different risks. The unsafe use of vehicles, machinery, and tools as well as animal husbandry, working at heights, and exposure to chemical, biological, and weather events may result in the deaths of agricultural workers. Inexperienced operators and/or their inappropriate conduct may lead to avoidable fatalities. Forensic pathologists operating with the support of agricultural engineers or other professionals must evaluate the death scene, the case background and circumstances, the autopsy findings, and the toxicological data to establish the factors and dynamics responsible for such accidents and deaths.The aim of this review is to focus on the diagnostic approach required, by means of an interdisciplinary approach, to identify the cause of some typical agricultural fatalities, to confirm that death was accidental, and to help exclude the possibility of homicide or suicide.

Evaluation of Warning Methods for Remotely Supervised Autonomous Agricultural Machines.

HIGHLIGHTS: Humans who supervise autonomous agricultural machines require some type of warning to perceive abnormal conditions in the machine or its environment. Visual and tactile warnings were the most suitable warning methods for in-field and close-to-field remote supervision. This study will help improve the performance of remote supervisors and minimize unexpected incidents or liabilities during operation of autonomous machines. ABSTRACT: As agricultural machinery moves toward full autonomy, human supervisors will need to monitor the autonomous machines during operation and minimize system failures or malfunctions. However, to intervene in an emergency, the supervisor must first recognize the emergency in a timely manner. Existing warning devices rely on the human visual, auditory, and tactile senses. However, these warning methods vary in their ability to attract attention. Hence, it is important to determine which warning method is best suited to draw the attention of a remote supervisor of an autonomous machine in an emergency. To achieve this objective, participants were recruited and asked to interact with a simulation of an autonomous sprayer. Seven warning methods (presented alone or in combinations of visual, auditory, and tactile sensory cues) and four remote supervision scenarios (in-field, close-to-field, farm office, outside the farmland) were considered in this study. The findings revealed that a combination of tactile and visual methods was most suitable for in-field and close-to-field remote supervision, in comparison to the other warning methods. However, there was insufficient evidence to recommend the best warning methods for supervisors at the farm office or outside the farmland. This study will help improve the performance of remote supervisors and minimize unexpected incidents during field operations with autonomous agricultural machines.

Novel cattail fiber composites: converting waste biomass into reinforcement for composites.

Vacuum-assisted resin transfer molding (VARTM), used in manufacturing medium to large-sized composites for transportation industries, requires non-woven mats. While non-woven glass mats used in these applications are optimized for resin impregnation and properties, such optimized mats for natural fibers are not available. In the current research, cattail fibers were extracted from plants (18-30% yield) using alkali retting and non-woven cattail fiber mat was manufactured. The extracted fibers exhibited a normal distribution in diameter (d avg. = 32.1 µm); the modulus and strength varied inversely with diameter, and their average values were 19.1 GPa and 172.3 MPa, respectively. The cattail fiber composites were manufactured using non-woven mats, Stypol polyester resin, VARTM pressure (101 kPa) and compression molding pressures (260 and 560 kPa) and tested. Out-of-plane permeability changed with the fiber volume fraction (V f) of the mats, which was influenced by areal density, thickness, and fiber packing in the mat. The cattail fibers reinforced the Stypol resin significantly. The modulus and the strength increased with consolidation pressures due to the increase in V f, with maximum values of 7.4 GPa and 48 MPa, respectively, demonstrating the utility of cattail fibers from waste biomass as reinforcements.

DESIGN AND EVALUATION OF INDIVIDUAL ELEMENTS OF THE INTERFACE FOR AN AGRICULTURAL MACHINE.

If a user-centered approach is not used to design information displays, the quantity and quality of information presented to the user may not match the needs of the user, or it may exceed the capability of the human operator for processing and using that information. The result may be an excessive mental workload and reduced situation awareness of the operator, which can negatively affect the machine performance and operational outcomes. The increasing use of technology in agricultural machines may expose the human operator to excessive and undesirable information if the operator's information needs and information processing capabilities are ignored. In this study, a user-centered approach was used to design specific interface elements for an agricultural air seeder. Designs of the interface elements were evaluated in a laboratory environment by developing high-fidelity prototypes. Evaluations of the user interface elements yielded significant improvement in situation awareness (up to 11%; overall mean difference = 5.0 (4.8%), 95% CI (6.4728, 3.5939), p 0.0001). Mental workload was reduced by up to 19.7% (overall mean difference = -5.2 (-7.9%), n = 30, a = 0.05). Study participants rated the overall performance of the newly designed user-centered interface elements higher in comparison to the previous designs (overall mean difference = 27.3 (189.8%), 99% CI (35.150, 19.384), p 0.0001.

Sensitivity and diagnosticity of NASA-TLX and simplified SWAT to assess the mental workload associated with operating an agricultural sprayer.

The objectives of the present study were: a) to investigate three continuous variants of the NASA-Task Load Index (TLX) (standard NASA (CNASA), average NASA (C1NASA) and principal component NASA (PCNASA)) and five different variants of the simplified subjective workload assessment technique (SSWAT) (continuous standard SSWAT (CSSWAT), continuous average SSWAT (C1SSWAT), continuous principal component SSWAT (PCSSWAT), discrete event-based SSWAT (D1SSWAT) and discrete standard SSWAT (DSSWAT)) in terms of their sensitivity and diagnosticity to assess the mental workload associated with agricultural spraying; b) to compare and select the best variants of NASA-TLX and SSWAT for future mental workload research in the agricultural domain. A total of 16 male university students (mean 30.4 +/- 12.5 years) participated in this study. All the participants were trained to drive an agricultural spraying simulator. Sensitivity was assessed by the ability of the scales to report the maximum change in workload ratings due to the change in illumination and difficulty levels. In addition, the factor loading method was used to quantify sensitivity. The diagnosticity was assessed by the ability of the scale to diagnose the change in task levels from single to dual. Among all the variants of NASA-TLX and SSWAT, PCNASA and discrete variants of SSWAT showed the highest sensitivity and diagnosticity. Moreover, among all the variants of NASA and SSWAT, the discrete variants of SSWAT showed the highest sensitivity and diagnosticity but also high between-subject variability. The continuous variants of both scales had relatively low sensitivity and diagnosticity and also low between-subject variability. Hence, when selecting a scale for future mental workload research in the agricultural domain, a researcher should decide what to compromise: 1) between-subject variability or 2) sensitivity and diagnosticity. STATEMENT OF RELEVANCE: The use of subjective workload scales is very popular in mental workload research. The present study investigated the different variants of two popular workload rating scales (i.e. NASA-TLX and SSWAT) in terms of their sensitivity and diagnositicity and selected the best variants of each scale for future mental workload research.