Automated image-processing for counting seedlings in a wheat field

Wheat field seedling density has a significant impact on the yield and quality of grains. Accurate and timely estimates of wheat field seedling density can guide cultivation to ensure high yield. The objective of this study was to develop an image-processing based, automatic counting method for wheat field seedlings, to investigate the principle of automatic counting of wheat emergence in the field, and to validate the newly developed method in various conditions. Digital images of the wheat fields at seedling stages with five cultivars and five seedling densities were acquired directly from above the fields. The wheat seedlings information was extracted from the background using excessive green and Otsu’s method. By analyzing the characteristic parameters of the overlapping regions (Overlapping region is a number of overlapping wheat seedlings in the image) of the fields, a chain code-based skeleton optimization method and corresponding equation were established for automatic counting of wheat seedlings in the overlapping regions. The results showed that the newly developed method can effectively count the number of wheat seedlings, with an average accuracy rate of 89.94 % and a highest accuracy rate of 99.21 %. The results also indicated that the accuracy of counting was not affected by different cultivars. However, the seedling density had significant impact on the counting accuracy (P < 0.05). When the seedling density was between 120 × 104 and 240 × 104 ha−1, high counting accuracy (>92 %) could be obtained. The study demonstrated that the newly developed method is reliable for automatic wheat seedlings counting, and also provides a theoretical perspective for automatic seedling counting in the wheat field.