Wang Jianliang, Chen Chen, Huang Senpeng, Wang Hui, Zhao Yuanyuan, Wang Jiacheng, Yao Zhaosheng, Sun Chengming, Liu Tao
Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou, China.
Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China.
Front Plant Sci. 2025 Jan 9;15:1502863. doi: 10.3389/fpls.2024.1502863. eCollection 2024.
Real-time monitoring of rice-wheat rotation areas is crucial for improving agricultural productivity and ensuring the overall yield of rice and wheat. However, the current monitoring methods mainly rely on manual recording and observation, leading to low monitoring efficiency. This study addresses the challenges of monitoring agricultural progress and the time-consuming and labor-intensive nature of the monitoring process. By integrating Unmanned aerial vehicle (UAV) image analysis technology and deep learning techniques, we proposed a method for precise monitoring of agricultural progress in rice-wheat rotation areas. The proposed method was initially used to extract color, texture, and convolutional features from RGB images for model construction. Then, redundant features were removed through feature correlation analysis. Additionally, activation layer features suitable for agricultural progress classification were proposed using the deep learning framework, enhancing classification accuracy. The results showed that the classification accuracies obtained by combining Color+Texture, Color+L08CON, Color+ResNet50, and Color+Texture+L08CON with the random forest model were 0.91, 0.99, 0.98, and 0.99, respectively. In contrast, the model using only color features had an accuracy of 85.3%, which is significantly lower than that of the multi-feature combination models. Color feature extraction took the shortest processing time (0.19 s) for a single image. The proposed Color+L08CON method achieved high accuracy with a processing time of 1.25 s, much faster than directly using deep learning models. This method effectively meets the need for real-time monitoring of agricultural progress.
实时监测稻麦轮作区对于提高农业生产力以及确保水稻和小麦的总产量至关重要。然而，目前的监测方法主要依赖人工记录和观察，导致监测效率低下。本研究应对了监测农业进展的挑战以及监测过程耗时费力的问题。通过整合无人机（UAV）图像分析技术和深度学习技术，我们提出了一种精确监测稻麦轮作区农业进展的方法。所提出的方法最初用于从RGB图像中提取颜色、纹理和卷积特征以构建模型。然后，通过特征相关性分析去除冗余特征。此外，利用深度学习框架提出了适用于农业进展分类的激活层特征，提高了分类准确率。结果表明，将颜色+纹理、颜色+L08CON、颜色+ResNet50以及颜色+纹理+L08CON与随机森林模型相结合所获得的分类准确率分别为0.91、0.99、0.98和0.99。相比之下，仅使用颜色特征的模型准确率为85.3%，明显低于多特征组合模型。对于单幅图像，颜色特征提取的处理时间最短（0.19秒）。所提出的颜色+L08CON方法以1.25秒的处理时间实现了高精度，比直接使用深度学习模型快得多。该方法有效满足了农业进展实时监测的需求。
