An intelligent monitoring system of diseases and pests on rice canopy.

Accurate and timely surveys of rice diseases and pests are important to control them and prevent the reduction of rice yields. The current manual survey method of rice diseases and pests is time-consuming, laborious, highly subjective and difficult to trace historical data. To address these issues, we developed an intelligent monitoring system for detecting and identifying the disease and pest lesions on the rice canopy. The system mainly includes a network camera, an intelligent detection model of diseases and pests on rice canopy, a web client and a server. Each camera of the system can collect rice images in about 310 m2 of paddy fields. An improved model YOLO-Diseases and Pests Detection (YOLO-DPD) was proposed to detect three lesions of Cnaphalocrocis medinalis, Chilo suppressalis, and Ustilaginoidea virens on rice canopy. The residual feature augmentation method was used to narrow the semantic gap between different scale features of rice disease and pest images. The convolution block attention module was added into the backbone network to enhance the regional disease and pest features for suppressing the background noises. Our experiments demonstrated that the improved model YOLO-DPD could detect three species of disease and pest lesions on rice canopy at different image scales with an average precision of 92.24, 87.35 and 90.74%, respectively, and a mean average precision of 90.11%. Compared to RetinaNet, Faster R-CNN and Yolov4 models, the mean average precision of YOLO-DPD increased by 18.20, 6.98, 6.10%, respectively. The average detection time of each image is 47 ms. Our system has the advantages of unattended operation, high detection precision, objective results, and data traceability.

Measurement of acoustic properties for passive-material samples using multichannel inverse filter.

The measurement of acoustic properties for passive-material samples is strongly interfered by reverberations in a limited space. A multichannel inverse filter (MIF) can achieve spatio-temporal focusing by retransmitting the optimal signal estimated from multichannel impulse responses of the transmitter channel and the underwater acoustic channel. To decrease the influence of reverberations on measurements and improve the measurement precision, a MIF is employed to measure the echo reduction and insertion loss, respectively. The method is demonstrated by simulations and experiments, which were performed in a cylindrical tank to measure the two acoustic parameters for a steel plate sample.