1. Introduction
Protected cultivation using systems such as greenhouses and, most recently, plant factories has served many purposes to communities dating back to early human civilization. The Korean Agriculture History Association recorded the use of protected cultivation systems as early as the 1450s [
1]. Since that era, these structures were constructed to grow food during the freezing winters and were taken down at the end of the cold season. This has transformed and advanced over the centuries to include growing premium crops such as paprika with precisely controlled micro- and macroenvironments [
2]. Plant factories have been adopted to resolve the issues surrounding land resources. This is increasingly becoming a major source of income for rural dwellers [
3,
4,
5,
6], especially in the Republic of Korea, from as early as the 1980s [
7,
8], where premium crops such as paprika and strawberries are cultivated and exported to countries in the region.
Sustainable growth of food, especially vegetables, faces many challenges, including water and land resources [
9] and climate conditions in some countries [
10,
11], which limit the year-round production of crops required for healthy living, such as fruits and vegetables. Protected cultivation systems are crucial in global food security, as they offer a controlled environment for high-value crops, including medicinal plants, which are essential for the wellbeing of humans. Generally, crops grown using protected cultivation systems are healthy and have higher yields [
12]. Other benefits of protected cultivation systems are the ease in adopting and implementing technologies, such as wireless communication systems [
13], and their ability to save water, as water shortages are a global problem caused by droughts associated with climate change [
14,
15]. These protected cultivation systems use less water for irrigation than normal farming because moisture is trapped in the structures and prevented from evaporating. Protected cultivation delivers opportunities for sustainable global food production with precise environmental control [
16,
17].
However, protected cultivation systems have drawbacks, such as skilled labor shortages due to the migration of young people to urban areas and the aging of the current rural dwellers. This is a major challenge in the Republic of Korea and other Organization for Economic Co-operation and Development (OECD) countries [
18,
19,
20]. Generally, the availability of skilled labor to complete repetitive tasks in the harsh climate condition of greenhouses is rapidly decreasing [
21]. The environment in greenhouses is usually hazardous compared to the open-field cultivation systems because of the poor air circulation [
22]. Other authors [
23] studied the occupational risk factors in greenhouse workers, and concluded that there are possible adverse health effects among greenhouse workers that are exposed to biological agents, pesticides, and other factors of their specific work environment. Consequently, robotics is currently been explored worldwide, especially in OECD countries, to mitigate the risks and hazards associated with the use of protected cultivation systems. These systems are usually termed “smart farms,” which involve the fusion of information and communication technologies.
Protected cultivation systems are capital-intensive systems compared to open-field cultivation [
20]. Consequently, different factors determine the choices made before and during the operation of these systems. These include the availability of energy, which takes more than 50% of the operational cost [
24,
25,
26]; regional infrastructure and market opportunities; climate condition of proposed area and resources, such as water and soil quality in terms of topography and natural disasters; land resources and capital accessibility; and a more pertinent issue, which is availability and cost of skilled labor [
27].
As mentioned above, market factors, which are major drivers in the selection of crops for protected cultivation systems, led watermelons, cucumbers, tomatoes, zucchinis, paprika, and strawberries to be the choice crops in the Republic of Korea, with paprika, strawberries, and tomatoes being the most commonly grown crops in protected systems around the world.
In the Republic of Korea, the overall annual strawberry production is around 166,594.5 Mg, and its cultivation occupies around 6062 hectares (ha), which is the fourth-largest cultivation area in the country. The protected cultivation of strawberries occupies 5539 ha and produces 165,011.5 Mg [
28]. Thus, more than 99% of strawberries are cultivated in protected greenhouses rather than outdoors [
29]. The labor time required per 1000 m
2 at each step during strawberry cultivation is as follows: 238.5 h for picking, 122.2 h for sorting and packing, and 87.8 h for cutting off sprouts and thinning [
28,
30,
31,
32].
Various factors have limited the use of robots in protected cultivation systems. These include the efficiency of current technologies compared to human labor and the cost of purchase and implementation of robots. However, the necessity for automation has and is continuously increasing because of the abovementioned reasons.
The efficient adoption of automation requires a requisite model to compute the time required in the various units. A detailed representation of system characteristics is usually provided using a simulation model. This is analyzed by sequences in work operations. Delmia Quest software version 5 (Quest Software Inc., Aliso Viejo, CA, USA) [
33] has been adopted in different scenarios and has been demonstrated to be a powerful tool in assessing the required changes before recording improvements. Some of the applications of this software include simulations of the Hotayi Electronic production line [
34] and delivery planning control for an industrial raw material system inventory of product service [
35]. Others include analyzing and optimizing a mechanical parts machining sequence in a manufacturing cell [
36], simulating integrated total quality management [
37], analyzing immunoglobulin and T cell receptor [
38], designing a flexible manufacturing system [
39], and developing simulation strategies [
40].
Thus, we aimed to analyze and improve the work efficiency of robots and conduct a work efficiency comparative analysis between a robot and human workforce in a standard strawberry greenhouse in the Republic of Korea. We applied harvesting robot specifications as the basis for the unmanned greenhouse design, in which the maximum amount of robotic harvesting time was allocated to the strawberry crop with the maximum profit per unit area. A series of analytic processes in Delmia Quest software was applied to derive practical improvements to the application of robots for harvesting strawberries in protected cultivations to verify the improvements and to explore their real-world application in the field.
The objectives of this study were to: (1) find an optimized operation process, (2) design a virtual strawberry cultivation greenhouse by adopting three-dimensional (3D) simulation modeling and validation, and (3) analyze the work efficiency of robots compared to that of a human workforce.
4. Discussion
Plant production is becoming increasingly difficult because of global challenges caused by climate change, increasing population, and competition with other sectors for limited land resources. The adoption of protected cultivation systems can help overcome these issues [
45,
46]. Protected cultivation has been used for centuries and has helped communities grow essential and fragile crops. However, in the past century, the major focus has been on growing vegetables during freezing winters and dismantling the structures after the winter season. These structures have transformed to solve food production challenges caused by the irregular weather patterns due to climate change and the necessity to feed the growing global population with limited natural resources such as land and water. These global issues (climate change, human population increase, and limited natural resources) are intertwined. Food security will be most likely affected by climate change at the local, regional, and global levels. These global issues are expected to impact food production and quality.
In agronomy, changes in precipitation patterns, reductions in water availability, projected increases in temperatures, and changes in extreme weather events could all negatively affect agricultural productivity. However, as protected cultivation systems advance, occupational hazards and shortages of skilled labor to perform repetitive tasks are limiting the optimal and efficient adoption of these systems. Thus, robotics is currently being explored globally as a potential solution to issues associated with growing in protected systems. Implementation of robotics in protected cultivation would help with the efficient and safe production of crops where temperature can be controlled to the optimal, natural resources such as water could be optimally used, and plants could be grown in stacks to save resources and improve productivity. Additionally, losses and safety issues associated with the harvesting of crops such as strawberries mostly occur because of improper handling and lack of skilled labor, or accessibility to labor in general, such as during the current COVID-19 pandemic caused by the SARS-CoV-2 virus [
47] that has limited international travel for migrant workers.
Robotics in protected cultivation could help with strengthening agronomic practices where optimal growing conditions for different plants, which have been studied and documented over the years, could be easily controlled and safe handling of plants can be easily implemented in systems such as greenhouses. For example, a harvesting robot can work around the clock and prevent cross contamination and bruises from improper handling as robots are programmed to be precise compared to humans, especially in repetitive tasks. A pertinent issue with the adoption of robotics in agriculture is efficient deployment because of the huge investment cost.
Consequently, to demonstrate an application of robotics to solve the issues around safe harvesting of strawberries, we analyzed production in a single span 1000 m2 greenhouse, and the number of strawberries per cluster was calculated based on 450 branches per bed, amounting to 81,000 strawberries (450 branches × 30 beds × 6 strawberries (number of average strawberries per branch)).
A comparative analysis between robot and different levels of skilled human workforce showed approximately 19%, 78%, and 145% reductions in time required for the robot to complete the task compared to experienced, average, and beginner human workers, respectively. This is because the robots can work 24 h a day, whereas human workers can only work 4 h a day due to the hot weather in the greenhouse during the summer season. When the hourly average production was calculated based on this, the robot’s hourly production was 8.85 kg, whereas human’s hourly production was 42.84 kg, which is approximately five times higher (
Table 5). Considering daily output (
Figure 10), the harvesting robot produced a 20% improvement compared to the human workforce, which makes it economically feasible to use a robot in this case. This improvement is projected to increase with advances in technology.
However, if the harvesting time per strawberry was shortened to 3 s, the 6-day workload based on 1215 kg total production would be completed in 4 days, resulting in a daily average production improvement from 212 to 347 kg, which is about a 63% increase (
Table 6). If it were shortened to 1 s, 1215 kg of strawberries could be harvested in two days and the daily average production increased to 1021 kg (
Table 6).
The constant increases in labor cost and the projected demand for strawberries, and the associated increase in price, would increase the economic feasibility of robots [
48]. For example, an economic analysis using data from the Republic of Korea showed that human wages were increasing 10.1% annually, while the cost of operating robots in a greenhouse was declining 5% annually. This declining rate could increase as robot technology becomes better and more widespread. At the current rate (
Figure 15), the cost of operating robots in a small greenhouse (less than 1750 m
2) is more expensive than human labor, and this result does not change for at least five years. However, as the size of the greenhouse increases to commercial size (above 1750 m
2), the cost of operating robots starts decreasing. This shows a need for proper economic analyses before purchasing robots. Furthermore, with the constant decline in the availability of skilled labor as discussed earlier, greenhouse growers face the risk of losing all their product if they rely on human labor.
Batteries are a crucial factor in the use of robots as it affects the total worktime of the robot, including the time taken for the robot to travel to and from the charging station and the time required to complete charging.
Different scenarios were simulated by increasing and reducing the original 10 h battery capacity. This was conducted to investigate the impact of battery capacity on the improvement in the robot use time. The analyses showed that if the battery capacity was reduced to 8 h, the daily average production was 203 kg, which is about 5% lower than the 212 kg achieved with 10 h capacity. When the battery performance was increased to a 12 h capacity, the daily average production increased to 219 kg, which is about 5% more (
Table 7). However, battery replacement was not considered to be economically beneficial because of the high cost of batteries in relation to the improvement in performance. Thus, the battery capacity of 10 h per charge, which was used in the simulation, was found to be appropriate (
Figure 12). The effect of battery recharge time was also analyzed based on a 2 h recharge. The change in productivity was analyzed when 1 h recharge time was added or subtracted.
These results indicated that the battery recharge time is more closely correlated with production than battery capacity. Thus, a fast recharge would be more beneficial than increased battery capacity. Rapid charging and replaceable batteries would benefit this system more than increasing the battery capacity. Consequently, the battery recharge time needs to be improved.
The robots’ movement pace was analyzed by adding or subtracting 0.1 m/s to/from 0.3 m/s in the simulation. The results (
Table 9 and
Figure 14) showed an approximately 5% difference in the daily average production due to the difference in travel speed, but this relationship was not linear because the robot’s travel speed is a variable related to the precision of the harvesting work. The precision of the sensor that detects the ripened fruit needs to be improved as the speed of the harvesting robot increases, which would lead to an increase in the manufacturing cost of the robot. Thus, 0.3 m/s was considered to be an appropriate velocity for the harvesting robot at the current level of technical development. In the interim, robots and human workers can work simultaneously since the layout was not changed and the number of workers and robots is dependent on several factors such as the size of the greenhouse, the required task, expected production output, and the availability of skilled workers.
These findings will facilitate the efficient adoption of protected cultivation systems such as greenhouses in the production of crops that are vital to food security. This will help in resolving issues around the health concerns of workers in these systems and problems due to skilled labor shortages especially in OECD countries. Furthermore, the findings can be used to work toward the efficient use of scarce and limited resources, such as water and land, as production in these systems utilize fewer resources compared to open field cultivation.