Agricultural and Rural Development Institute, Heilongjiang Provincial Academy of Social Sciences, Harbin, China.
Changchun Guanghua University, College of Business, Jilin, Changchun 130033, China.
J Environ Public Health. 2022 Jun 3;2022:1588638. doi: 10.1155/2022/1588638. eCollection 2022.
In the long developmental process, China's agriculture has transformed from organic agriculture to inorganic agriculture. New technologies have made the modernization of agriculture possible. However, most older people who are engaged in agriculture may not completely understand the modernization of agriculture. Based on the limitations of traditional image target detection methods, a deep learning-based pest target detection and recognition method is proposed from a blockchain perspective, to analyze and research agricultural data supervision and governance and explore the effectiveness of deep learning methods in crop pest detection and recognition. The comparative analysis demonstrates that the average precision (AP) of GA-CPN-LAR (global activation-characteristic pyramid network-local activation region) increases by 4.2% compared with other methods. Whether under the Inception or ResNet-50 backbone networks, the AP of GA-CPN-LAR is significantly better than other methods. Compared with the ResNet-50 backbone network, GA-CPN-LAR has higher accuracy and recall rates under Inception. Precision-recall curve measurement shows that the proposed method can significantly reduce the false detection rate and missed detection rate. The GA-CPN-LAR model proposed here has a higher AP value on the MPD dataset than the other target detection methods, which can be increased by 4.2%. Besides, the accuracy and recall of the GA-CPN-LAR method corresponding to two representative pests under the initial feature extractor are higher than the MPD dataset baseline. In addition, the research results of the MPD dataset and AgriPest dataset also show that the pest target detection method based on convolutional neural networks (CNNs) has a good presentation effect and can significantly reduce false detection and missed detection. Moreover, the pest regulation based on blockchain and deep learning comprehensively considers global and local feature extraction and pattern recognition, which positively impacts the conscientization of agricultural data processing and promotes the sustainable development of rural areas.
在漫长的发展过程中，中国农业已经从有机农业转变为无机农业。新技术使农业现代化成为可能。然而，大多数从事农业的老年人可能并不完全理解农业现代化。基于传统图像目标检测方法的局限性，从区块链的角度提出了一种基于深度学习的病虫害目标检测和识别方法，以分析和研究农业数据监管治理，并探索深度学习方法在作物病虫害检测和识别中的有效性。对比分析表明，与其他方法相比，GA-CPN-LAR（全局激活-特征金字塔网络-局部激活区域）的平均精度（AP）提高了 4.2%。无论是在 Inception 还是 ResNet-50 骨干网络下，GA-CPN-LAR 的 AP 都明显优于其他方法。与 ResNet-50 骨干网络相比，GA-CPN-LAR 在 Inception 下具有更高的精度和召回率。精度-召回率曲线测量表明，所提出的方法可以显著降低误检率和漏检率。与其他目标检测方法相比，这里提出的 GA-CPN-LAR 模型在 MPD 数据集上具有更高的 AP 值，可提高 4.2%。此外，GA-CPN-LAR 方法对应于初始特征提取器下两种代表性害虫的准确性和召回率均高于 MPD 数据集基线。此外，MPD 数据集和 AgriPest 数据集的研究结果还表明，基于卷积神经网络（CNNs）的病虫害目标检测方法具有良好的呈现效果，可以显著降低误检和漏检。此外，基于区块链和深度学习的病虫害治理综合考虑了全局和局部特征提取和模式识别，这对农业数据处理的自觉意识产生了积极影响，促进了农村地区的可持续发展。
