Using a High-Resolution Digital Camera

To count canola seedpods in the lab setting, images of thirty individual canola plants were taken by the Sony A58 with different view angles. As seen in the workflow introduced in Chapter 3, each color image was converted into a grayscale image and then tube-like structures (vessels) were distinguished by applying the Frangi 2D Vesselness filter, as shown in Figure 5-16. By using the result after applying the Frangi filter, the skeleton of the plant was extracted. Finally, the skeleton was refined before the endpoints in the skeleton of the canola were detected. With these endpoints, the seedpods were detected, and then the number of seedpods was estimated. This process is shown in Figure 5-17.

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Figure 5-16. The original image (a), the grayscale image (b), and the filtered image

Figure 5-17. The skeleton and the results of counting canola seedpods

Similar to the algorithm for detecting and counting canola branches, a methodology of quantitative performance evaluation for counting canola seedpods was examined. Thirty individual canola plants with three different angle view images were captured and automatically processed. To provide referenced numbers of canola seedpods, manual counting the canola seedpods was applied. To express the accuracy of these experiment results, a relative error rate was used:

𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝑒𝑟𝑟𝑜𝑟 = |𝑀−𝐴𝑣𝑔|

𝑀 ∗ 100% (5.2)

where M is the manual counting result, Avg is the average of the automatic counting result. In this formula, the average of automatic counting results was calculated by:

𝐴𝑣𝑔 = ∑ 𝐴𝑢𝑡𝑜𝑚𝑎𝑡𝑖𝑐 𝑐𝑜𝑢𝑛𝑡𝑖𝑛𝑔 𝑟𝑒𝑠𝑢𝑙𝑡31

3 (5.3)

The comparisons of the automatic and manual counting of canola seedpods are presented in Table 5-3. The manual results and estimated results are very close, with an average of 91.4%

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accuracy. Because the algorithm detected and counted all canola ‘spikes’ and seedpods, the number of canola ‘spikes’ contributes to the relative errors in counting. Spikes are the sharp points located at the end of the stems and branches, but they not are the seedpods. Since the percentage of canola spikes is quite high, about 9.1 %, the average error rate reaches 8.6 %. The results of counting canola seedpods and the percentage of canola spikes and seedpods are shown in Table 5-3 and Table 5-4, respectively.

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Table 5-4. Percentage of canola spikes

The accuracy of the proposed algorithm is presented in Table 5-5. It can be seen that the accuracy of the proposed counting algorithm reached about 92%. To deal with this drawback, an improvement to the algorithm was proposed. To reduce these error rates in counting canola seedpods, the number of canola spikes must be eliminated. It is possible to hypothesize that the result of each measurement for counting canola seedpods was estimated by:

𝑅 = {𝐴 − 𝑆𝑝, 𝑖𝑓 𝐴 > 𝑀

𝐴 + 𝑆𝑝, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 (5.4)

where R is the result of estimating a number of seedpods after eliminating spikes, A is the average result of the automatic counting of canola seedpods, M is the result of manual counting number of canola seedpods, and Sp is the estimated number of spikes based on the relationship between A and the percentage of canola spikes. Sp was calculated by:

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𝑆𝑝 = 𝐴 ∗ 𝑛 (5.5)

where n is the percentage of canola spikes.

Table 5-5. The accuracy of the automatic counting of canola seedpods

As a result, the error rates of the proposed algorithm for counting seedpods are significantly decreased after eliminating the number of spikes. The error rate is lowered to 3.2% on average after applying Equations (5.4) and (5.5), as presented in Table 5-6. The outputs of this algorithm are shown in Figure 5-18. The results of these experiments support the idea that the proposed algorithms have the potential to highly and accurately detect and count canola seedpods.

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Table 5-6. The refined results of automatic counting the canola seedpods

83 5.3.2 Limitations

The method for counting seedpods was limited by the quality of the source images (e.g., a clear image, clear seedpods, and a clear background level), the overlapping seedpods, and computational costs. The experiment’s results show that the error rate increased for several reasons, such as unclear canola plant images, a complex background, and overlapping seedpods. The clearer the back-ground and seedpods, and the less the seedpods overlap, the fewer the errors. To deal with these problems, a clear image and clear background are required and each canola plant must be captured from different views. A promising solution to the problems might be to capture the plant with different focuses and then to use an image fusion technique, which will be introduced in the next section. The images this technique produces would be fused to obtain clear seedpods.

5.4

Monitoring Canola Growth Stages

This section described a non-contact method of canola plant growth measurement using the proposed mobile platform. The proposed system measures canola plant growth parameters, such as plant height. If plant growth parameters are precisely measured on each plant growth cycle, a detailed model of plant growth can be developed. As well, the system can accurately predict and control the plants for high yield. This section describes how a calibration of the Argos3D camera was first performed before the canola plant heights’ were measured. The depth camera must be calibrated to examine the canola plants’-growth stages using distance information. The distance information depends on several Argos3D’s parameters, such as a data frame rate and integration time.