Propiedades de equivalencia (posición y rol)

In this study, data were collected seven times for maize plants and six times for sorghum plants between 28 and 47 days after sowing. Both sides of the plants were sensed and processed separately. Total 106 traits were taken for maize plants and 87 traits were taken for sorghum plants. In this section, analyses were presented between the experiment results and manual measurements.

The system’s performance was evaluated using the plant detection rate and plant location accuracy. The plant detection rate was defined as

Estimated plant population

Plant Detection Rate = × 100% (3.2) Ground truth of the plant population

The estimated plant was defined as over-counted when no actual plant appeared within ±0.01 m of the estimated one. Similarly, when no estimated plant appeared within ±0.01 m of the actual plant, one under-counted plant was defined. If the estimated plant appeared within ±0.01 m of the actual plant, the estimated one was defined as a true plant. The experimental results are shown in Table 3.3, where NT is the ground truth value of total plant population, NE is the true estimated

Table 3.3: Estimated plant population compared to ground truth Row # NT NE NO NU RD SDD

1 94 83.2 10.0 9.9 0.86 0.06 2 80 71.7 11.2 7.4 0.90 0.06 Overall for Maize 0.89 0.06 3 103 86.9 6.6 15.4 0.84 0.06 4 (grain) 72 61.0 4.5 10.4 0.85 0.10 Overall for Grain Sorghum 0.85 0.08 4 (grass) 77 33.5 0.4 43.4 0.41 0.19

plant population (true positive), NO is the number of over-counted plants (false positive), NU is the

number of under-counted plants (false negative), RD is the detection rate of the true plants, SDD is the

standard deviation of estimated plant population rate.

The system achieved the average detection rate of 89% and a standard deviation of 0.06 for maize plants and no significant difference between rows. The average detection rate for sorghum was 85% and the standard deviation was 0.08.

Figure 3.11: Maize and grain sorghum plant detection rate

Figure 3.12: An Example of missing count for ’double plants’ in three continuous point cloud images

Figure 3.13: Four continuous intensity images affected by dropping leaves

Plant under-counted errors were mainly caused by ’double plants’, in which two plants are too close to each other. By checking the ground truth measurements, 13 pairs of plants in row 1, 8 pair of plants in row 2, 17 pairs of plants in row 3, and 11 pairs of grain sorghum plants in row 4 had a

distance less than 0.02 m in between. If two plants were too close at the bottom, the system might misidentify them as one plant as shown in Figure 3.12. Even if the plants appeared in multiple frames, none of them can be used to clearly separate the ’double plants’. Moreover, not all the frames can be used due to the dropping leaves as shown in Figure 3.13. The camera lens was blocked by three dropping leaves and four continuous images were not able to be used.

Figure 3.14: Grass sorghum plant detection rate

Figure 3.15: ANOVA table for grass sorghum plant detection rate

For unexpected reasons, the germination rate of grass sorghum in the first two planters of row 4 was lower than 10%. Replanting was done on the 15th day after the first sowing. The density of the

re-sowed sorghum was much higher than the original plants, which led to the higher total plant population and higher pairs of ’double plants’ within single planter compared with grain sorghum. As shown in Figure 3.14, the detection rate of row 4 was increasing sharply as the resowed sorghum growing. The low p-value (less than 0.05) (Figure 3.15) indicates that the plant detection rate is significantly related to time.

The system limitations are contributing from two factors: sensor and materials. Since the data was collected by Swift ToF camera, the motion blur can affect the image quality. Also, the quality of the depth image was limited. Secondly, as the experiments were set in the greenhouse, the soil, the planters, and the plant growth conditions were all different from those in the field. The soil used in the greenhouse has perlite rocks mixed in which has a high reflection rate and brighter than norm soil. The bottom 0.02 − 0.03 m of the plant stem were not able to be used due to the noise. Even if the planters were covered by the light absorbing materials, the effects cannot be removed completely. Random noise appeared behind the bottom plant stems which affects the Euclidean clustering method in the plant candidate extraction algorithm. In addition, the experiment was carried out during the wintertime, the light source in the greenhouse was not strong enough to support the normal plant growth. Both maize and sorghum were too slim than what they should be in the field which makes it challenge for stem identification.

3.4 Conclusions

In this study, an automated sensing system using a state-of-the-art Time-of-Flight camera was developed for estimating maize and sorghum plant population at their early growth stages under a greenhouse condition. For maize plants, the system achieved the average detection rate of 0.89 and a standard deviation of 0.06. For grain sorghum plants, the average detection rate was 0.85 and the standard deviation was 0.08. The detection rate and standard deviation for grass sorghum were 0.41 and 0.19, respectively. The results show that this system is capable of estimating maize and grain sorghum population and potential to be used for grass sorghum at growth stage later than V4.

Because this Swift ToF camera was advertised to be operable under direct sunlight, the data collection process was completed without any shade. But the coating of the greenhouse glass should have blocked NIR wavelengths, making this setting not truthfully representing the natural outdoor lighting conditions. However, the limitations of this ToF camera in signal quality and resolution were examined in this targeted application.

In the future, the system performance should be evaluated further over maize and grain sorghum plants in field experiments. The robustness of the system should be enhanced for processing both maize and sorghum plants in various growth stages. In addition, with the success of many deep learning neural networks based sensing system, continued investigation in applying the cutting-edge machine learning techniques for plant detection should be conducted. It was also observed that the intensity images produced under the active NIR lighting of the camera were of favorable consistency and clarity, which may lead to improved plant detection accuracy if they were used in conjunction with the machine learning algorithms.

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