Scientia Agricultura Sinica ›› 2020, Vol. 53 ›› Issue (17): 3496-3508.doi: 10.3864/j.issn.0578-1752.2020.17.007

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

A Method for the Automatic Determination of Scale Parameter During Segmenting Agricultural Drone Images

SHI YaJiao1,2(),CHEN PengFei1,3()   

  1. 1Institute of Geographical Science and Natural Resources Research, Chinese Academy of Sciences/State Key Laboratory of Resources and Environment Information System, Beijing 100101
    2University of Chinese Academy of Sciences, Beijing 100049
    3Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023
  • Received:2019-11-05 Accepted:2020-02-09 Online:2020-09-01 Published:2020-09-11
  • Contact: PengFei CHEN E-mail:shiyj.17s@igsnrr.ac.cn;pengfeichen@igsnrr.ac.cn

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Abstract:

【Objective】The automatic extraction of crop planting area in the image is of great significance for promoting the application of high-resolution drone images in precision agriculture. At present, the method based on designing segmentation evaluation function is most commonly used in the study of automatically determining segmentation scale parameters. In order to meet the needs of high-precision automatic segmentation of agricultural drone images, an improved evaluation function was proposed to solve the defects of the Weighted Local Variance (WLV) method in this study, and the proposed method was verified based on field experiments of different crops.【Method】With considering that WLV method does not consider the internal homogeneity of segmented objects, this study designed the Improved Weighted Local Variance (IWLV) method with adding the calculation of inter-object homogeneity on the basis of WLV formula. The nitrogen experiments of corn and water and nitrogen coupling experiment of wheat were designed. During corn and wheat growing season, drone images were obtained in different growth stages with crop in different vigor. Based on the obtained UAV images, different scenarios were set. The mainstream segmentation algorithm was combined with WLV method and IWLV method to perform image segmentation, respectively. Their segmentation results were compared with human-machine interactive segmentation results, and evaluated based on Single-scale Object Accuracy (SOA).【Result】The selected scale parameter by the WLV method tended too large, which resulted in under-segmentation during segmenting images. While, based on the selected scale parameter by IWLV method, the segmentation result was correspond well with human-machine interactive segmentation results. The IWLV method had higher SOA values for all designed scenarios.【Conclusion】Compared with the WLV method, the proposed IWLV method in this study had higher accuracy when determining the segmentation scale parameter.

Key words: scale parameter, image segmentation, drone image, improved weighted local variance method

Fig. 1

Schematic diagram of the nitrogen experiment of corn N1-N5 indicate applied nitrogen at 0, 70, 140, 210, 280 kg·hm-2"

Fig. 2

Schematic diagram of winter wheat water and nitrogen coupling experiment W1: 90 mm irrigation amount, W2: 60 mm irrigation amount. N1: No fertilizer; N2: 15 000 kg·hm-2 of farmyard manure; N3: 15 000 kg·hm-2 of farmyard manure and 100 kg·hm-2 of nitrogen fertilizer; N4: 15 000 kg·hm-2 of farmyard manure and 200 kg·hm-2 of nitrogen fertilizer; N5: 15 000 kg·hm-2 of farmyard manure and 300 kg·hm-2 of nitrogen fertilizer"

Fig. 3

Schematic diagram of corn experiment (a) and corn drone images of bellmouth growth stage (b) and before heading growth stage (c) N1-N5 represent applied nitrogen at 0, 70, 140, 210, 280 kg·hm-2"

Fig. 4

Schematic diagram of wheat experiment (a) and drone image of wheat jointing growth stage (b) W1: 90 mm irrigation amount, W2: 60 mm irrigation amount. N1: No fertilizer; N2: 15 000 kg·hm-2 of farmyard manure; N3: 15 000 kg·hm-2 of farmyard manure and 100 kg·hm-2 of nitrogen fertilizer; N4: 15 000 kg·hm-2 of farmyard manure and 200 kg·hm-2 of nitrogen fertilizer; N5: 15 000 kg·hm-2 of farmyard manure and 300 kg·hm-2 of nitrogen fertilizer"

Fig. 5

Curves of WLV and IWLV under different scale parameters using corn images Figure a-e denotes N1S1-N5S1 respectively, and figure f-j denotes N1S2-N5S2 respectively"

Table 1

Best selected scale for WLV and IWLV in each scenario using corn images"

方法 Method N1S1 N2S1 N3S1 N4S1 N5S1 N1S2 N2S2 N3S2 N4S2 N5S2
WLV 65 65 70 90 80 35 40 45 40 45
IWLV 40 60 40 60 45 25 30 30 30 30

Fig. 6

The distribution of verified zone using corn images"

Table 2

SOA values corresponding to the segmentation image based on the selected scale by WLV method and IWLV method in each scenario using corn image"

方法 Method N1S1 N2S1 N3S1 N4S1 N5S1 N1S2 N2S2 N3S2 N4S2 N5S2
WLV 0.626 0.693 0.675 0.654 0.722 0.747 0.755 0.682 0.726 0.711
IWLV 0.770 0.765 0.767 0.770 0.795 0.768 0.811 0.796 0.807 0.771

Fig. 7

Segmentation results using corn image Segmentation result by human-machine interactive method (a: N1S1 scenario; d: N3S2 scenario); segmentation result of selected optimal scale by the IWLV method (b: N1S1 scenario; e: N3S2 scenario); segmentation result of selected optimal scale by the WLV method (c: N1S1 scenario; f: N3S2 scenario)"

Fig. 8

Curves of WLV and IWLV under different scale parameters using wheat images"

Table 3

Selected scale based on WLV and IWLV method in each scenario using wheat images"

方法 Method N1W1 N2W1 N3W1 N4W1 N5W1 N1W2 N2W2 N3W2 N4W2 N5W2
WLV 20 35 65 85 75 20 90 85 75 75
IWLV 20 15 25 70 65 15 15 20 35 70

Fig. 9

The distribution of verified zone in wheat image"

Table 4

SOA values corresponding to the segmentation image based on the selected scale by WLV method and IWLV method in each scenario using wheat image"

方法 Method N1W1 N2W1 N3W1 N4W1 N5W1 N1W2 N2W2 N3W2 N4W2 N5W2
WLV 0.675 0.535 0.548 0.553 0.661 0.618 0.430 0.446 0.536 0.646
IWLV 0.675 0.660 0.717 0.602 0.705 0.667 0.716 0.708 0.685 0.648

Fig. 10

Segmentation results using wheat image Segmentation result by human-machine interactive method (a: N2W1 scenario;d: N3W2 scenario); segmentation result of selected optimal scale by the IWLV method (b: N2W1 scenario; e: N3W2 scenario); segmentation result of selected optimal scale by the WLV method (c: N2W1 scenario; f: N3W2 scenario)"

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