Smart Agriculture ›› 2023, Vol. 5 ›› Issue (3): 62-74.doi: 10.12133/j.smartag.SA202308010
• Special Issue--Monitoring Technology of Crop Information • Previous Articles Next Articles
LONG Jianing1,2(), ZHANG Zhao1,2(
), LIU Xiaohang1,2, LI Yunxia1,2, RUI Zhaoyu1,2, YU Jiangfan1,2, ZHANG Man1,2, FLORES Paulo3, HAN Zhexiong4,5, HU Can6, WANG Xufeng6
Received:
2023-08-04
Online:
2023-09-30
Foundation items:
About author:
LONG Jianing, E-mail:614020890@qq.com
corresponding author:
ZHANG Zhao, E-mail:zhaozhangcau@cau.edu.cn
LONG Jianing, ZHANG Zhao, LIU Xiaohang, LI Yunxia, RUI Zhaoyu, YU Jiangfan, ZHANG Man, FLORES Paulo, HAN Zhexiong, HU Can, WANG Xufeng. Wheat Lodging Types Detection Based on UAV Image Using Improved EfficientNetV2[J]. Smart Agriculture, 2023, 5(3): 62-74.
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URL: https://www.smartag.net.cn/EN/10.12133/j.smartag.SA202308010
Table 1
Results of using SVM classifier to categorize the types of lodging wheat for each category at three heights
高度/m | 评价指标 | 未倒伏/% | 根部倒伏/% | 茎部倒伏/% |
---|---|---|---|---|
15 | Precision | 82.13 | 81.56 | 79.43 |
Recall | 83.45 | 100.00 | 72.81 | |
F 1-Score | 84.23 | 84.23 | 77.79 | |
Accuracy/% | 81.33 | |||
45 | Precision | 83.56 | 95.13 | 85.50 |
Recall | 85.11 | 100.00 | 79.35 | |
F 1-Score | 84.11 | 98.73 | 82.44 | |
Accuracy/% | 83.51 | |||
91 | Precision | 84.47 | 85.47 | 78.02 |
Recall | 73.97 | 100.00 | 81.28 | |
F 1-Score | 82.30 | 82.45 | 80.60 | |
Accuracy/% | 81.00 |
Table 2
Results of using the three deep learning classification models to categorize the types of wheat lodging for each category at the three heights
ResNet101 | EfficientNetV2 | EfficientNetV2-C | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
高度/m | 倒伏类型 | Precision/% | Recall/% | F 1-Score/% | Precision/% | Recall/% | F 1-Score/% | Precision/% | Recall/% | F 1-Score/% |
15 | 未倒伏 | 77.42 | 90.00 | 83.24 | 80.08 | 92.50 | 88.09 | 97.53 | 98.75 | 98.14 |
根部倒伏 | 84.71 | 84.71 | 84.71 | 88.59 | 85.53 | 87.03 | 96.59 | 100.00 | 98.27 | |
茎部倒伏 | 83.12 | 71.11 | 76.65 | 82.22 | 73.78 | 79.05 | 98.84 | 94.44 | 96.59 | |
Accuracy/% | 81.57 | 84.40 | 97.65 | |||||||
45 | 未倒伏 | 77.08 | 92.50 | 84.09 | 83.72 | 90.00 | 86.75 | 84.62 | 96.25 | 90.06 |
根部倒伏 | 84.21 | 75.29 | 79.50 | 79.55 | 82.35 | 80.92 | 92.13 | 96.47 | 94.25 | |
茎部倒伏 | 77.11 | 71.11 | 73.99 | 76.54 | 68.89 | 72.51 | 93.59 | 81.11 | 86.90 | |
Accuracy/% | 79.22 | 82.00 | 92.5 | |||||||
91 | 未倒伏 | 79.79 | 93.75 | 86.21 | 81.11 | 91.25 | 85.88 | 87.95 | 91.25 | 89.57 |
根部倒伏 | 78.31 | 76.47 | 77.38 | 85.33 | 75.29 | 80.00 | 87.21 | 93.75 | 90.36 | |
茎部倒伏 | 75.64 | 65.56 | 70.24 | 73.33 | 73.33 | 73.33 | 92.41 | 81.11 | 86.39 | |
Accuracy/% | 78.04 | 81.61 | 90.59 |
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