College of Information Engineering, Xijing University, Xi'An, 710123, China.
PLoS One. 2023 Jun 2;18(6):e0276456. doi: 10.1371/journal.pone.0276456. eCollection 2023.
Crop pests seriously affect the yield and quality of crop. To timely and accurately control crop pests is particularly crucial for crop security, quality of life and a stable agricultural economy. Crop pest detection in field is an essential step to control the pests. The existing convolutional neural network (CNN) based pest detection methods are not satisfactory for small pest recognition and detection in field because the pests are various with different colors, shapes and poses. A three-scale CNN with attention (TSCNNA) model is constructed for crop pest detection by adding the channel attention and spatial mechanisms are introduced into CNN. TSCNNA can improve the interest of CNN for pest detection with different sizes under complicated background, and enlarge the receptive field of CNN, so as to improve the accuracy of pest detection. Experiments are carried out on the image set of common crop pests, and the precision is 93.16%, which is 5.1% and 3.7% higher than ICNN and VGG16, respectively. The results show that the proposed method can achieve both high speed and high accuracy of crop pest detection. This proposed method has certain practical significance of real-time crop pest control in the field.
农作物病虫害严重影响作物的产量和品质。及时准确地防治农作物病虫害，对保障粮食安全、提高生活质量、稳定农业经济具有十分重要的意义。田间作物病虫害检测是防治病虫害的重要环节。现有的基于卷积神经网络（CNN）的病虫害检测方法在田间小病虫害识别和检测方面的效果并不理想，因为病虫害种类繁多，颜色、形状和姿态各异。本文构建了一种具有注意力机制的三尺度卷积神经网络（TSCNNA）模型，通过引入通道注意力和空间机制，提高了 CNN 对复杂背景下不同大小病虫害检测的兴趣，扩大了 CNN 的感受野，从而提高了病虫害检测的准确率。在常见作物病虫害图像数据集上进行的实验结果表明，该方法的准确率为 93.16%，分别比 ICNN 和 VGG16 提高了 5.1%和 3.7%。实验结果表明，该方法能够实现田间作物病虫害的高速高精度检测，对田间实时病虫害防治具有一定的实际意义。
