**4. Materials and Methods**

#### *4.1. Bacterial Strains*

Twenty-two enterococci strains previously described [26,98,99] were evaluated in the present study. Briefly, the collection includes *Enterococcus* species isolated from fecal samples (cloacal/anal swabs or intestinal content) collected from 17 wild marine animals. These animals, including sea turtles (*n* = 3), seabirds (*n* = 8), and marine mammals (*n* = 6), were found along the North Coast of Rio Grande do Sul, Southern Brazil, from Torres Beach (29◦21 32.2 S; 49◦44 10.3 W) to Dunas Altas Beach, Palmares do Sul (30◦23 58.75 S; 50◦17 24.73 W), between July 2012 and April 2014 (Table 1). The enterococci collection was stored frozen at −20 ◦C in skim milk supplemented with 20% glycerol, and cultures were routinely grown in brain heart infusion (BHI) at 37 ◦C for 18 h.


*Solid-State Anaerobic Microbial Ensilage Pretreatment DOI: http://dx.doi.org/10.5772/intechopen.92571*

other places, calcium formate plus sodium sulfite was used for silage. You can also choose some other antifungal agents, such as sorbic acid and its potassium salt, as appropriate, but the price is higher. When using mold inhibitors, spray them evenly on the shredded silage raw materials as far as possible, compact them in layers, and seal them in good condition.


• Content

Thirteen percent of the MTurk participants and thirteen percent of Park and Rec participants found the content of the training to be informative and stated that it provided useful information. For example, "*I liked the PUSH training because it was very informative in teaching the workers about looking out for those around them and also for their own wellness too.*" [MTurk, Restaurant Cashier] and "*Yes because it was informative and useful for future reference.*" [Park and Rec].

A few participants (8%) compared PUSH to other safety trainings they had taken in the past. The MTurk participants preferred the PUSH training, "*Yeah, I really liked it. Most job trainings are really boring, but the PUSH training was engaging. I remember enjoying it.*" [MTurk, Service manager]; whereas the participants in the Park and Rec group mentioned that they had learned most of the safety information in the PUSH training from their prior onsite training, "*I thought it had good intentions but I learned more about safety procedures from my on-site training at work.*" [Park and Rec worker].

• Delivery format

The training is self-paced and divided into topics that include pictures and videos. Several participants (19%) felt the training was interactive and engaging and that they liked the training interface, "*I really enjoyed it. It presented information in an interesting, concise way. The time it took to take really went by quickly because the videos and interaction were so engaging.*" [MTurk, Childcare Provider] and "*I liked it because it was very interactive.*" [Park and Rec]. Participants' comments identified the engaging/interactive nature (36%), the humor in the training (29%), and reported that it was easy to understand (21%), "*I did. I really enjoyed the way that it was formatted and felt as though it was helpful and funny without being corny or boring.*" [MTurk, Admissions counselor] and "*I enjoy the PUSH program because it's both friendly and easy to understand.*" [MTurk, Office assistant].

• Specific skills

Several of the participants (17%) mentioned that they liked the training because it increased their knowledge and provided specific skills for their job. For example, *"* ... ... ... ... *.. I think it was useful, especially the parts about legal rights.*" [Park and Rec].

• Negative and Mixed Findings

In contrast to the MTurk group, some participants in the Park and Rec group felt that the training was repetitive and boring and they had learned this information in previous trainings, "*I didn't like it because it seemed repetitive to the safety training I underwent in order to get my job with PP&R.*" [Park and Rec] and "*It was fine, long but I understand why it was long*" [Park and Rec]. Other participants indicated mixed impressions of the training. They felt that although it was not useful to them, it could be useful to others, "*It was fine. I didn't learn a whole lot, but I can imagine it being useful for others.*" [Park and Rec]


*2.3. UAV-Based Hyperspectral Imaging*

UAV has become a popular platform in recent years for remote sensing data acquisition, especially for multispectral imaging using digital cameras or multispectral sensors. With the increased availability of lightweight hyperspectral sensors, researchers have experimented on mounting these sensors on UAVs to acquire high-spatial-resolution hyperspectral imagery [19,117]. Different types of UAVs, including multi-rotors, helicopters, and fixed wings, have been utilized in previous studies (Figure 3). Compared with manned airplanes and helicopters, UAVs are capable of acquiring highspatial-resolution images with a much lower cost and have high flexibility in terms of scheduling a flight mission [118]. Several specific agricultural applications of UAV-based hyperspectral imaging are summarized in Table 3.

**Figure 3.** Hyperspectral UAV systems used in previous agricultural studies. Figures were reproduced with permission from the corresponding publishers: (**a**) MDPI [119], (**b**) MDPI [120], (**c**) MDPI [121], and (**d**) SPIE [122].

**Table 3.** Example applications of UAV-based hyperspectral imaging in agriculture.

Various lightweight hyperspectral sensors have been developed in recent years and can be mounted on UAVs. Examples of sensors include the widely-used Headwall Micro- and Nano-Hyperspec VNIR [12,13,26,128], UHD 185-Firefly [53,130], the PIKA II sensor [19,32], and the HySpex VNIR [25,131]. These hyperspectral sensors contain more than 100 bands in the visible-near infrared spectral range (Table 2). These sensors are small and compact (1–2 kg), thus they can be deployed quickly on various manned or unmanned remote sensing platforms. Previous studies conducted by Adão et al. [11] and Lodhi et al. [52] also compared and summarized various lightweight hyperspectral sensors.

A large number of factors need to be considered in the application of UAV-based hyperspectral imaging, ranging from sensor setup and data collection, to image processing. Saari et al. [122] tested the feasibility of a UAV-based hyperspectral imaging system for agricultural and forest applications and discussed several challenges regarding the imaging technology (e.g., hardware requirements and system settings). Aasen et al. [132] focused on the calibration of images collected with a frame-based sensor and discussed several challenges related to the use of UAV-based hyperspectral imaging for vegetation and crop investigation (e.g., the payload of UAV, signal-to-noise ratio, and spectral calibration). Habib et al. [120] attempted to perform orthorectification of UAV-acquired pushbroom-based hyperspectral imagery with frame-based RGB images over an agricultural field. Adão et al. [11] reviewed applications of UAV-based hyperspectral imaging in agriculture and forestry and listed several hyperspectral sensors that can be mounted on UAVs. The authors also discussed several challenges in collecting and analyzing UAV-based hyperspectral imagery, such as radiometric noise, the low quality of UAV georeferencing, and a low signal-to-noise ratio.

UAV-based hyperspectral imaging has become more popular in recent years; therefore, it is critical to review its strengths and limitations. To explore more features of this technology, this section of the review is not limited to agricultural applications alone. Different types of UAVs have been used as hyperspectral imaging platforms, with the two most widely used as multi-rotors [130,133,134] and fixed-wing planes [33,120,135]. Slow flights at low altitudes are preferred to achieve high-spatial-resolution hyperspectral imagery with a high signal-to-noise ratio. Thus, a multi-rotor is more competitive than

fixed-wing planes for hyperspectral imaging in terms of flight operation. Specifically, the multi-rotor allows for a low flight altitude, flexible flight speed, and vertical takeoff and landing, while the fixed wing requires a minimum flight altitude, speed, and, sometimes, accessories for takeoff and landing (e.g., runway, launcher, and parachute). A hyperspectral imaging system, which consists of a hyperspectral sensor, a data processing unit, a GPS, and an IMU, has a considerable weight (e.g., 1–3 kg), thus bringing challenges to the payload capacity of the UAV system and its battery endurance. The multi-rotors are generally powered by high-performance batteries (e.g., LiPo), and most have a short endurance (e.g., less than 20 min). The endurance can be as short as 3 min [12]. In contrast, many fixed-wing UAVs are powered by fuel, thus having a much longer endurance (e.g., 1–10 h) [19,135]. However, these fixed-wing planes are mostly large and heavy (e.g.,a5m wingspan and 14 kg take-off weight) [135], and thus bring challenges to the flight operation. Using UAV, researchers need to consider the UAV SWaP (size, weight, and power), geographical coverage, time aloft, altitude, and other variables. In addition to the challenges in building a UAV system and performing flight operations, researchers likely need to apply for flight permission from an aviation authority (e.g., Special Flight Operations Certificate (SFOC) from Transport Canada), and purchase suitable UAV flight insurance [136]. UAV size and weight are essential parameters to consider in these processes. Furthermore, the UAVs are required to be visible during flight missions, so that the pilot can maintain constant visual contact with the aircraft. This could create a major challenge when flying over a large area, a hilly area, or an area with forests.