|
|
|
| Crop leaf disease recognition based on feature-level and decision-level fusion |
| WANG Ziheng1, SHEN Jifeng1, ZUO Xin2, WU Xiaohong1, SUN Jun1 |
| 1. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang， Jiangsu 212013, China； 2. School of Computer, Jiangsu University of Science and Technology, Zhenjiang， Jiangsu 212003, China |
|
|
|
|
Abstract To solve the shortcomings of single-network model with learning preference for data， the crop disease identification method was proposed based on multi-model fusion. The single model performance of four mainstream convolutional neural networks of ResNet50, DenseNet121, Xception and MobileNetV2 was evaluated by the proposed method, and the four single models were respectively conducted by multi-model feature-level and decision-level fusion to obtain the identification output. The feature-level fusion method was used to average, maximize and splice the final output feature layer of each sub-network to achieve efficient complementarity of heterogeneous features. The decision-level fusion method was used to maximize and average the output probability of each sub-network to achieve efficient union of probability distribution decisions. The experimental results on the PDR2018 crop disease datasets show that feature-level fusion outperforms decision-level fusion and single-model methods significantly. Among the fusion methods, splicing compression feature fusion achieves the highest recognition accuracy of 98.44%. The cross-database experiments on PlantDoc data subset and the actual images confirm that the feature fusion method exhibits superior accuracy and generalization performance compared to the single-model method.
|
|
Received: 31 August 2022
|
|
|
|
| ［1］ |
朱家辉,苏维均,于重重.基于卷积神经网络的农作物叶片病害诊断 [J].植物检疫,2021,35(2):28-32.
|
|
ZHU J H,SU W J,YU C C.Diagnosis of crop foliar di-seases based on convolutional neural network [J].Plant Quarantine, 2021,35(2):28-32.（in Chinese）
|
| ［2］ |
贾少鹏,高红菊,杭潇. 基于深度学习的农作物病虫害图像别技术研究进展 [J].农业机械学报,2019，50(增刊1)：313-317.
|
|
JIA S P, GAO H J, HANG X. Researchprogress on ima-ge recognition technology of crop pests and diseases based on deep learning [J]. Transactions of the CSAM, 2019,50(Sup1)：313-317.(in Chinese)
|
| ［3］ |
王东方,汪军. 基于迁移学习和残差网络的农作物病害分类 [J].农业工程学报,2021,37(4):199-207.
|
|
WANG D F,WANG J.Crop disease classification with transfer learning and residual networks [J].Transactions of the CSAE, 2021,37(4):199-207.(in Chinese)
|
| ［4］ |
张连宽,肖德琴,岑冠军，等. 基于叶片匹配的作物植株图像分离与计数方法 [J].江苏大学学报(自然科学版), 2023,44(3):293-301.
|
|
ZHANG L K,XIAO D Q,CEN G J,et al.Individual plant extraction and counting from field images based on leaf matching [J].Journal of Jiangsu University(Natural Science Edition),2023,44(3):293-301.(in Chinese)
|
| ［5］ |
张善文,张传雷. 基于局部判别映射算法的玉米病害识别方法 [J].农业工程学报,2014,30(11):167-172.
|
|
ZHANG S W,ZHANG C L. Maize disease recognition based on local discriminant algorithm [J]. Transactions of the CSAE, 2014,30(11):167-172.(in Chinese)
|
| ［6］ |
PADOL P B, YADAV A A. SVM classifier based grape leaf disease detection [C]∥2016 Conference on Advances in Signal Processing, 2016:175-179.
|
| ［7］ |
SURESHA M, SHREEKANTH K N, THIRUMALESH B V. Recognition of diseases in paddy leaves using KNN classifier [C]∥2017 2nd International Conference for Convergence in Technology, 2017:663-666.
|
| ［8］ |
SABROL H, SATISH K. Tomato plant disease classification in digital images using classification tree [C]∥2016 International Conference on Communication and Signal Processing, 2016:1242-1246.
|
| ［9］ |
陈俞松,耿长兴,王永，等. 基于分段垂直投影的水田秧苗列中心线提取 [J].江苏大学学报(自然科学版), 2022,43(3):289-295.
|
|
CHEN Y S，GENG C X，WANG Y，et al. Center line extraction of paddy seedling row based on segmented vertical projection [J].Journal of Jiangsu University(Natural Science Edition),2022,43(3):289-295.(in Chinese)
|
| ［10］ |
苏仕芳,乔焰,饶元. 基于迁移学习的葡萄叶片病害识别及移动端应用 [J].农业工程学报,2021,37(10):127-134.
|
|
SU S F, QIAO Y, RAO Y.Recognition of grape leaf di-seases and mobile application based on transfer learning [J]. Transactions of the CSAE, 201,37(10):127-134.(in Chinese)
|
| ［11］ |
KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Imagenet classification with deep convolutional neural networks [J]. Advances in Neural Information Processing Systems, 2012,60：25-33.
|
| ［12］ |
LYU M, ZHOU G, HE M, et al. Maize leaf disease identification based on feature enhancement and DMS-robust alexnet [J]. IEEE Access, 2020,8:57952-57966.
|
| ［13］ |
孙俊,谭文军,毛罕平,等. 基于改进卷积神经网络的多种植物叶片病害识别 [J].农业工程学报，2017,33(19)：209-215.
|
|
SUN J, TAN W J, MAO H P, et al. Real-time recognition of sugar beet and weeds in complex backgrounds using multi-channel depth-wise separable convolution model [J]. Transactions of the CSAE, 2017,33(19)：209-215.(in Chinese)
|
| ［14］ |
SIMONYAN K, ZISSERMAN A.Very deep convolutional networks for large-scale image recognition [J]. Computer Science,2014，6:113-123.
|
| ［15］ |
张建华,孔繁涛,吴建寨，等. 基于改进VGG卷积神经网络的棉花病害识别模型 [J].中国农业大学学报,2018,23(11):161-171.
|
|
ZHANG J H,KONG F T,WU J Z,et al.Cotton disease identification model based on improved VGG convolution neural network [J]. Journal of China Agricultural University, 2018,23(11):161-171.(in Chinese)
|
| ［16］ |
NAGARAJU Y, SWETHA S, STALIN S. Apple and grape leaf diseases classification using transfer learning via fine-tuned classifier [C]∥2020 IEEE International Conference on Machine Learning and Applied Network Technologies, 2020:1-6.
|
| ［17］ |
HE K, ZHANG X, REN S, et al. Deep residual lear-ning for image recognition [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition， 2016:770-778.
|
| ［18］ |
LI X, RAI L. Apple leaf disease identification and classification using resnet models [C]∥2020 IEEE 3rd International Conference on Electronic Information and Communication Technology, 2020:738-742.
|
| ［19］ |
SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition， 2015:1-9.
|
| ［20］ |
HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely connected convolutional networks [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017:4700-4708.
|
| ［21］ |
SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Rethinking the inception architecture for computer vision [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition， 2016:2818-2826.
|
| ［22］ |
KEH S S. Semi-supervised noisy student pre-training on efficientnet architectures for plant pathology classification [J]. arXiv， DOI：10.48550/arXiv.2012.00332.
|
| ［23］ |
KENDLER S, AHARONI R, YOUNG S, et al. Detection of crop diseases using enhanced variability imagery data and convolutional neural networks [J]. Computers and Electronics in Agriculture, 2022,193:1-8.
|
| ［24］ |
任永梅,杨杰,郭志强,等. 基于多尺度卷积神经网络的自适应熵加权决策融合船舶图像分类方法 [J].电子与信息学报,2021,43(5):1424-1431.
|
|
REN Y M,YANG J,GUO Z Q,et al.Self-adaptive entropy weighted decision fusion method for ship image classification based on multi-scale convolutional neural network [J].Journal of Electronics and Information Techno-logy, 2021,43(5):1424-1431.(in Chinese)
|
| ［25］ |
李昊,王斌. 深度特征融合方法及其在叶片病害识别中的应用 [J].计算机系统应用,2022,31(7):349-355.
|
|
LI H,WANG B.Deep feature fusion method and its application in leaf disease recognition [J].Computer Systems & Applications,2022,31(7):349-355.(in Chinese)
|
| ［26］ |
马玉琨,刘子琼,张文武，等. 多特征融合的农作物病害图像识别 [J].河南科技学院学报(自然科学版), 2021,49(4):45-50，57.
|
|
MA Y K,LIU Z Q,ZHANG W W,et al.Image recognition of crop disease based on multi-feature fusion [J].Journal of Henan Institute of Science and Technology(Natural Science Edition),2021,49(4):45-50，57.(in Chinese)
|
| ［27］ |
杜忠康,房胜,李哲，等. 基于卷积神经网络深度特征融合的番茄叶片病害检测 [J].中国科技论文,2021,16(7):701-707.
|
|
DU Z K，FANG S，LI Z，et al.Tomato leaf disease detection based on deep feature fusion of convolution neural network [J]. China Science Paper,2021,16(7):701-707.(in Chinese)
|
| ［28］ |
CHOLLET F. Xception:deep learning with depthwise separable convolutions [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition， 2017:1251-1258.
|
| ［29］ |
SANDLER M, HOWARD A, ZHU M， et al. MobileNetV2: inverted residuals and linear bottlenecks [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition， 2018:4510-4520.
|
| ［30］ |
ZHOU B, KHOSLA A, LAPEDRIZA A, et al. Learning deep features for discriminative localization [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition， 2016:2921-2929.
|
| ［31］ |
王美华,吴振鑫,周祖光. 基于注意力改进CBAM的农作物病虫害细粒度识别研究 [J].农业机械学报,2021,52(4):239-247.
|
|
WANG M H,WU Z X,ZHOU Z G.Fine-grained identification research of crop pests and diseases based on improved CBAM via attention [J].Transactions of the CSAM,2021,52(4):239-247.(in Chinese)
|
| ［32］ |
孙俊,朱伟栋,罗元秋,等. 基于改进MobileNet-V2的田间农作物叶片病害识别 [J].农业工程学报,2021,37(22):161-169.
|
|
SUN J, ZHU W D, LUO Y Q, et al. Recognizing the diseases of crop leaves in fields using improved MobileNet-V2 [J]. Transactions of the CSAE,2021,37(22):161-169.(in Chinese)
|
| ［33］ |
BEHERA A, WHARTON Z, HEWAGE P R P G, et al. Context-aware attentional pooling (cap) for fine-grained visual classification [C]∥Proceedings of the AAAI Conference on Artificial Intelligence，2021,35(2):929-937.
|
|
|
|
2024, Vol. 45 
