Nondestructive measurement technology of phenotype can provide substantial phenotypic data support for applications such as seedling breeding,management,and quality testing.The current method of measuring seedling phe...Nondestructive measurement technology of phenotype can provide substantial phenotypic data support for applications such as seedling breeding,management,and quality testing.The current method of measuring seedling phenotypes mainly relies on manual measurement which is inefficient,subjective and destroys samples.Therefore,the paper proposes a nondestructive measurement method for the canopy phenotype of the watermelon plug seedlings based on deep learning.The Azure Kinect was used to shoot canopy color images,depth images,and RGB-D images of the watermelon plug seedlings.The Mask-RCNN network was used to classify,segment,and count the canopy leaves of the watermelon plug seedlings.To reduce the error of leaf area measurement caused by mutual occlusion of leaves,the leaves were repaired by CycleGAN,and the depth images were restored by image processing.Then,the Delaunay triangulation was adopted to measure the leaf area in the leaf point cloud.The YOLOX target detection network was used to identify the growing point position of each seedling on the plug tray.Then the depth differences between the growing point and the upper surface of the plug tray were calculated to obtain plant height.The experiment results show that the nondestructive measurement algorithm proposed in this paper achieves good measurement performance for the watermelon plug seedlings from the 1 true-leaf to 3 true-leaf stages.The average relative error of measurement is 2.33%for the number of true leaves,4.59%for the number of cotyledons,8.37%for the leaf area,and 3.27%for the plant height.The experiment results demonstrate that the proposed algorithm in this paper provides an effective solution for the nondestructive measurement of the canopy phenotype of the plug seedlings.展开更多
为筛选适宜日光温室东西垄向宜机化栽培的小型西瓜品种,选择了16个西瓜品种作为供试材料,对西瓜植株形态指标、果实综合品质指标进行调查分析,采用相关分析、层次分析(analytic hierarchy process,AHP)和动态时序逼近理想解排序法(dynam...为筛选适宜日光温室东西垄向宜机化栽培的小型西瓜品种,选择了16个西瓜品种作为供试材料,对西瓜植株形态指标、果实综合品质指标进行调查分析,采用相关分析、层次分析(analytic hierarchy process,AHP)和动态时序逼近理想解排序法(dynamic technique for order preference by similarity to ideal solution,DTOPSIS)对西瓜品种进行综合评价。结果表明:植株茎粗、叶片宽、叶长宽比、叶周长、总节位数5个形态指标与产量和品质显著相关;综合考虑形态指标、风味品质和经济品质指标,采用层次分析确定评价指标最终权重排名前5的是产量、单果变异系数、单果质量、可溶性固形物含量和可溶性糖含量。基于DTOPSIS分析法得出综合排序前3的品种分别是宝冠、洪新和京阑,推荐作为日光温室东西垄向宜机化栽培西瓜品种。展开更多
基金funded by the National Key Research and Development Program of China(Grant No.2019YFD1001900)the HZAU-AGIS Cooperation Fund(Grant No.SZYJY2022006).
文摘Nondestructive measurement technology of phenotype can provide substantial phenotypic data support for applications such as seedling breeding,management,and quality testing.The current method of measuring seedling phenotypes mainly relies on manual measurement which is inefficient,subjective and destroys samples.Therefore,the paper proposes a nondestructive measurement method for the canopy phenotype of the watermelon plug seedlings based on deep learning.The Azure Kinect was used to shoot canopy color images,depth images,and RGB-D images of the watermelon plug seedlings.The Mask-RCNN network was used to classify,segment,and count the canopy leaves of the watermelon plug seedlings.To reduce the error of leaf area measurement caused by mutual occlusion of leaves,the leaves were repaired by CycleGAN,and the depth images were restored by image processing.Then,the Delaunay triangulation was adopted to measure the leaf area in the leaf point cloud.The YOLOX target detection network was used to identify the growing point position of each seedling on the plug tray.Then the depth differences between the growing point and the upper surface of the plug tray were calculated to obtain plant height.The experiment results show that the nondestructive measurement algorithm proposed in this paper achieves good measurement performance for the watermelon plug seedlings from the 1 true-leaf to 3 true-leaf stages.The average relative error of measurement is 2.33%for the number of true leaves,4.59%for the number of cotyledons,8.37%for the leaf area,and 3.27%for the plant height.The experiment results demonstrate that the proposed algorithm in this paper provides an effective solution for the nondestructive measurement of the canopy phenotype of the plug seedlings.
文摘为筛选适宜日光温室东西垄向宜机化栽培的小型西瓜品种,选择了16个西瓜品种作为供试材料,对西瓜植株形态指标、果实综合品质指标进行调查分析,采用相关分析、层次分析(analytic hierarchy process,AHP)和动态时序逼近理想解排序法(dynamic technique for order preference by similarity to ideal solution,DTOPSIS)对西瓜品种进行综合评价。结果表明:植株茎粗、叶片宽、叶长宽比、叶周长、总节位数5个形态指标与产量和品质显著相关;综合考虑形态指标、风味品质和经济品质指标,采用层次分析确定评价指标最终权重排名前5的是产量、单果变异系数、单果质量、可溶性固形物含量和可溶性糖含量。基于DTOPSIS分析法得出综合排序前3的品种分别是宝冠、洪新和京阑,推荐作为日光温室东西垄向宜机化栽培西瓜品种。
