Crops ›› 2025, Vol. 41 ›› Issue (1): 250-259.doi: 10.16035/j.issn.1001-7283.2025.01.032

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Identification Method of Plant Growth Stages Based on Improved ResNet18 and Realization of Intelligent Plant Lighting Supplement

Wang An(), Chen Zhanxu(), Kong Jingxu, Wu Siyuan, He Shaowei, Zhang Jialing, Wan Wei   

  1. College of Optoelectronic Engineering, Guangdong Polytechnic Normal University, Guangzhou 510450, Guangdong, China
  • Received:2023-06-07 Revised:2024-02-24 Online:2025-02-15 Published:2025-02-12

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Abstract:

In order to accurately identify the growth stage of plants and achieve intelligent plant light supplement, a set of intelligent plant supplementary light system that can identify plant species and growth stages was designed. The ResNet18 model was used to improve the identification of plant growth stage, and the deep separable convolution was used to replace traditional convolution. The SE module (squeeze and excitation module) was introduced to improve the efficiency and accuracy of model task processing, and early stop method and learning rate attenuation mechanism were combined for training to avoid overfitting. Tomato was used as the research object to verify and identify its seedling, flowering and fruiting, fruit ripening stages. The results showed that the recognition accuracy of the improved ResNet18 model reaches 96.57%, which was 4.93 percentage points higher than the original model. The single recognition time was 0.27 seconds, which was 0.30 seconds faster than the original model. The model volume was 14% of the original model. At the same time, the improved model was superior to the four convolutional neural networks of ResNet18, ResNet34, AlexNet and VGG16 in terms of test set accuracy, parameter quantity and Macro F1 score. Finally, the improved ResNet18 model was applied to the plant light supplement system, and the actual recognition accuracy of tomato growth stage reached 96.49%, and the expected spectrum was output. The system can accurately identify plant species and their growth stage, so as to invoke the light formula matching the plant and its growth stage, to achieve the purpose of intelligent plant light supplement.

Key words: Intelligent light supplement, Convolutional neural network, Improved ResNet18 model, Identification of plant growth stage

Fig.1

Plant supplementary light intelligent system"

Fig.2

Tomato image collection"

Fig.3

Visualization of pretreatment during seedling stage"

Table 1

structure of ResNet18"

图层名称
Layer name		 输出尺寸
Output size		 ResNet18
conv1 60×60 7×7, 64, stride=2
conv2

 30×30

 3×3 maxpool, stride=2
3 × 3 , ? 64 3 × 3 , ? 64 ×2
conv3
 15×15
 3 × 3 , ? 128 3 × 3 , ? 128 ×2
conv4
 8×8
 3 × 3 , ? 256 3 × 3 , ? 256 ×2
conv5
 4×4
 3 × 3 , ? 512 3 × 3 , ? 512 ×2
1×1 Average pool, 1000-d fc, softmax
FLOPs 1.8×109

Fig.4

ResNet18 residual structure"

Fig.5

Depthwise separable convolution process"

Fig.6

Channel feature extraction"

Fig.7

Improved residual module"

Fig.8

Learning rate attenuation chart"

Fig.9

Effects of learning rate adjustment on test set loss value and accuracy rate (a) Influence of rough tuning learning rate on loss value, (b) Influence of rough tuning learning rate on accuracy rate, (c) Influence of fine tuning learning rate on loss value, (d) Influence of fine tuning learning rate on accuracy rate."

Fig.10

Effects of batch size on test set loss value and accuracy rate"

Fig.11

Improved ResNet18 test set identification confusion matrix"

Table 2

Comparison of network models"

模型
Model		 测试准确率
Test-accuracy
(%)		 参数量
Parameter
quantity		 Macro
F1		 识别时间
Identification
time (s)
AlexNet 93.56 4.09×106 0.9210 0.13
VGG16 94.56 5.03×107 0.9247 1.57
ResNet18 91.64 1.12×107 0.8990 0.57
ResNet34 95.32 2.13×107 0.9417 0.93
改进ResNet18
Improved ResNet18		 96.57
 1.53×106
 0.9630
 0.27

Fig.12

Improved ResNet18 recognition feature thermal diagram"

Table 3

System test results"

生长阶段
Growth stage		 正确次数
Number
of correct		 错误次数
Number
of errors		 成功率
Success
rate (%)
幼苗期Seedling stage 50 0 100.00
开花坐果期
Flowering and fruit-setting stage		 67
 3
 95.71
果实成熟期Fruit ripening stage 75 5 93.75

Fig.13

Output spectrum measurement (a) red and blue spectrum ratio is 3:1, (b) red and blue spectrum ratio is 7:1, (c) red and blue spectrum ratio is 5:1."

Table 4

Spectral parameters"

生长阶段Growth stage 红光辐照度Er (W/m2) 蓝光辐照度Eb (W/m2) 红蓝比Er-Eb ratio 光通量密度PPFD [μmol/(m2·s)]
幼苗期Seedling stage 10.00 3.31 3.02 68.44
开花坐果期Flowering and fruit-setting stage 8.84 1.24 7.13 54.00
果实成熟期Fruit ripening stage 8.91 1.76 5.07 56.43
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