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Revista Ingeniantes 2021 Año 8 No. 1 Vol. 1
To verify whether the robot could detect temperatu- Although in this study, AP had only a single activation
re and relative humidity and simultaneously could be temperature (25°C), the AP increased the total clusters
activated in an automatic way and also perform the vi- per plant 1.2-fold higher compared to the MP (130 vs
bration, the activity of the robot was recorded for six 108) (p<0.05) (Figure 5A). The total number of tomato
months (January-July). Figure 4A, shows the tempera- fruits per plant was also higher for AP in 1.35-fold ti-
ture kinetics in three consecutive days, as we can see mes than the AP (963 vs 714) (p<0.05) compared with
the min-max temperature ranges from 10 to 37 °C, and MP (Figure 5B). The AP pollinated 90% of the flowers
every day at least two activations could be reached one within the first 150 days vs the 75% for the MP (Figure
for the morning at 9:45 am and four hours later at 13:46 5C). Hence, extrapolating these results we can assume
pm and the vibration was performed only when the an approximated total tomato yield of 64000 tomato-
greenhouse reached the set point parameter (25 ± 0.9 es per gutter, i.e., approximately 256000 tomatoes in
°C), all the measures were corroborated with external the whole greenhouse using the AP vs approximated
instruments (data not shown). 190000 for MP. Even though in the Pearson correlation,
Figure 4B, shows the temperature inside the greenhou- we found a moderate relationship between the days
se with a minimum and maximum temperature range passed and the pollination method, we did not find a
oscillating between 12°C and 42°C during all the studied significant difference among models (NS) (Figure 5C).
variables measurement. This was an extra point to our The R2 indicates that the adjusted model explains 53%
robot due to this robot could be activated not only in and 61% of the variability in pollinated flowers either for
determinant temperature but also in determinant humi- AP or MP, respectively. The correlation coefficient of
dity inside the greenhouse and therefore, could perform 0.73 (AP) and 0.78 (MP) indicates a moderately strong
several activations. Moreover, the robot has the ability relationship among variables.
to store great amounts of data (depending on the sto-
ring devices), and the performing and activation of the
AP is very precise and did not require any previous or
constant calibration during all the study.
Figure 5. Manual pollination vs automatic pollination. A. Clus-
ter production per plant (n=20). B. Tomato production per plant
(n=20). C. Pearson´s correlation between pollinated-plants and
days. * p<0.05.
Figure 4. Effect of temperature on pollination. A. Three consecu- DISCUSSION
tive day recording and its respective pollination hour. B. Three Pollination is the sexual reproduction in plants, this ha-
consecutive months (February, blue triangles; March, black rec- ppens through pollen transfer from the anther to a stig-
tangles; April, pale-red circles) recording and its respective po- ma. In this sense, pollination is carried out to produce
llination hour. seeds and, in some cases, fruits. Hence, pollination is an
important input during production of marketable goods
of many crops, in some crops such as tomato, supple-
mentary pollination is needed; however, under green-
house conditions this practice is expensive, laborious
and unhealthy in some cases. For that reason, is neces-
sary to develop robots to help human labor in agricultu-
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