A high-precision detection method of hydroponic lettuce seedlings status based on improved Faster RCNN
In order to improve the efficiency and reduce high cost for seedlings sorting in the raising process of hydroponic lettuce seedlings, we propose an automatic detection method for hydroponic lettuce seedlings based on improved Faster RCNN framework, taking the dead and double-planting status of seedlings growing in a single hole as our research objects. Since the characteristics of hydroponic lettuce seedlings are dense and small in the images, our model uses High Resolution Network (HRNet) as the backbone network for image feature extraction so as to obtain reliable and high- resolution feature expressions. Besides, we adopt focal loss as the classification loss in the Region Proposal Network (RPN) stage to address the imbalance between difficult and easy samples in seedlings classification. We also employ the Region of Interest (RoI) Align instead of the RoI Pooling layer to improve the detection accuracy of seedlings in the different status. The results show that the mean average precision of our method for the hydroponic lettuce seedlings is 86.2%, which is higher than RetinaNet, SSD, Cascade RCNN, FCOS and other detectors. Compared with different feature extraction networks, the detection accuracy of adopting HRNet performs nicely. Therefore, our method presented for the detection of hydroponic lettuce seedlings status can achieve high accuracy and identify seedlings in a problematic status well, which will provide technical support for automatic seedlings detection of hydroponic lettuce.
期刊:
Computers and Electronics in Agriculture
2021
作者:
Yizhe Wang,Jun Yue,Jinqi Yang,Ruohao Guo,Yongbo Yang,Ye Li,Zhenbo Li
DOI:10.1016/j.compag.2021.106054
A solanaceae disease recognition model based on SE-Inception
Aiming at the diseases of tomato and eggplant, we present a solanaceae disease recognition model based on SE-Inception. Our model uses batch normalization layer (BN) to accelerate network convergence. Besides, SE-Inception structure and multi-scale feature extraction module is adopted to improve accuracy of this model. Our sample data set consists of 4 disease categories including whitefly, powdery mildew, yellow smut, cotton blight. We also add healthy leaves into it. In order to reduce overfitting, the data set is expanded by the data enhancement method of translation, rotation and flip. Experiments show that the average recognition accuracy of this model is 98.29% and the model size is 14.68 MB on our constructed dataset. In addition, in order to verify the robustness of this model, it was also verified on the public data set of PlantVillage, and the top-1, top-5 accuracy and the size of our proposed model is 99.27%, 99.99% and 14.8 MB respectively. Moreover, we implemented a solanaceae disease image recognition system using this model based on the Android. The accuracy of average recognition and the recognition time of a single photo are 95.09% and 227 ms, respectively. Our constructed model has a small number of parameters with maintaining high accuracy, which can meet the needs of automatic recognition of disease images on mobile devices. Data and code are available at https://github.com/Jujube-sun/diseaseRecognition.
期刊:
Computers and Electronics in Agriculture
2020
作者:
Jun Yue,Jinqi Yang,Ruohao Guo,Ye Li,Yongbo Yang,Zhenbo Li
DOI:10.1016/j.compag.2020.105792
基于协同回归模型的矩阵分解推荐
推荐系统是解决信息过载的有效途径。传统的推荐系统难以从海量数据中推选出符合用户个性化偏好的项目,推荐质量不高。为此,通过优化传统的协同过滤推荐算法,针对数据稀疏性等问题,提出协同回归模型的矩阵分解算法（CLMF）。通过机器学习算法发掘内容信息的深层次特征,提升了原始数据的信息量;并构建辅助特征矩阵,通过融合特征矩阵,CLMF最大化了特征标签的作用,并结合数据标签,语义信息和评分矩阵得到推荐算法框架。在真实数据集上实验结果显示,新型推荐算法可有效解决特征值缺失问题,改善了数据稀疏性,提升了算法扩展性,并显著增强覆盖性。
期刊:
图学学报
2019
作者:
岳峻,杨晋琪,李振波
设施园艺物联网技术与应用进展
期刊:
农业工程技术
2018
作者:
盖国卫,杨晋琪,李振波
