Hyperspectral remote sensing for early detection of wild carrot in Carrot (Daucus carota) seed production : a feasibility study : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Horticultural Science at Massey University, Manawatū, New Zealand

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Carrot (Daucus carota) seed production is an important part of the NZ vegetable seed industry with exports of $33.4 million NZD in 2020. Most carrot seed production is based in the Canterbury region, but there is a desire by key stakeholders to expand carrot seed production in the Hawke’s Bay region of NZ. However, presence of weed wild carrot (Daucus carota subsp. carota) in the region acts as a significant constraint to carrot seed production. Wild carrot plants can crosspollinate with carrot crop plants, causing genetic contamination in a crop where genetic purity is of critical importance. The current weed management strategy of manual scouting and rouging is resource intensive and ineffective in achieving appropriate control of wild carrot in the region. Airborne hyperspectral remote sensing is a technology that has proven its ability in plant species identification and can do so at a high spatial scale in a short period of time. This makes the technology a promising candidate for a superior alternative weed control method. This project aimed to test the feasibility of the technology to identify wild carrot plants in carrot seed crop fields and nearby areas within the crop’s isolation distance (2000m). This involved creating spectral libraries of dominant plant species/materials, including wild carrot, in the area of interest. The methodology involved conducting a survey and collecting airborne hyperspectral data. Further, ground-based collection of GNSS enabled accurate GPS locations of wild carrot plants in the survey area, acted as training and validation data for subsequent classification analysis. The ground truth data was also used for a pixel composition analysis – which also helped understand the environmental context of wild carrot plants. The data was analysed in an image processing software (ENVI®, v5.6). The analysis involved two levels of classification algorithms. A first order classification – minimum distance classification (MDC) – was used to classify the data into broad land surface cover types. The classification was successful with an overall accuracy of 96%. The second order classification was a soft classification algorithm which employed spectral unmixing – mixture tuned matched-filtering (MTMF). This method allows sub-pixel classification when the target surface is smaller than pixel size, as in this case. MTMF helped create a model which predicted potential locations of wild carrot plants at a threshold level of 5% of pixel area (surface area - 0.05m²) and a producer’s accuracy of 70% (Omission error rate – 30%) for patches above the threshold surface area. These predicted locations were projected on appropriate RGB base layers to create wild carrot weed maps. The biggest limitation was likely the 1m² spatial resolution of the hyperspectral camera employed in the study, which dictated the 5% pixel threshold level. These detection threshold and accuracy levels are lower than in other similar studies, however they are likely acceptable in the current context and can help mitigate wild carrot damage in carrot seed production in the Hawke’s Bay. The study has helped identify areas of future research to further improve the detection threshold and accuracy levels. These include identifying relationships between environmental context related parameters and wild carrot manifestation, acquiring higher spatial resolution data (lower altitude flights, unmanned aerial vehicle (UAV) mounted cameras, deploying of image fusion techniques using separate high spectral (hyperspectral) and spatial resolution (RGB/multispectral) imagery.
Figure 2-1 is reproduced with permission. Figure 2-3 is reused under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. Figure 2-7 is reused under a Creative Commons Attribution 3.0 Unported (CC BY 3.0) license. Figure 2-11 (= Gibson et al., 2000b Fig 2.7 (a) & (b) p. 21) was removed for copyright reasons.