Agricultural Robot for Intelligent Detection of Pyralidae Insects

The Pyralidae insects are one of the main pests in economic crops. However, the manual detection and identification of Pyralidae insects are labor intensive and inefficient, and subjective factors can influence recognition accuracy. To address these shortcomings, an insect monitoring robot and a new method to recognize the Pyralidae insects are presented in this chapter. Firstly, the robot gets images by performing a fixed action and detects whether there are Pyralidae insects in the images. The recognition method obtains the total probability image by using reverse mapping of histogram and multi-template images, and then image contour can be extracted quickly and accurately by using constraint Otsu. Finally, according to the Hu moment characters, perimeter, and area characters, the contours can be filtrated, and recognition results with triangle mark can be obtained. According to the recognition results, the speed of the robot car and mechanical arm can be adjusted adaptively. The theoretical analysis and experimental results show that the proposed scheme has high timeliness and high recognition accuracy in the natural planting scene

A YOLOv7 incorporating the Adan optimizer based corn pests identification method

Major pests of corn insects include corn borer, armyworm, bollworm, aphid, and corn leaf mites. Timely and accurate detection of these pests is crucial for effective pests control and scientific decision making. However, existing methods for identification based on traditional machine learning and neural networks are limited by high model training costs and low recognition accuracy. To address these problems, we proposed a YOLOv7 maize pests identification method incorporating the Adan optimizer. First, we selected three major corn pests, corn borer, armyworm and bollworm as research objects. Then, we collected and constructed a corn pests dataset by using data augmentation to address the problem of scarce corn pests data. Second, we chose the YOLOv7 network as the detection model, and we proposed to replace the original optimizer of YOLOv7 with the Adan optimizer for its high computational cost. The Adan optimizer can efficiently sense the surrounding gradient information in advance, allowing the model to escape sharp local minima. Thus, the robustness and accuracy of the model can be improved while significantly reducing the computing power. Finally, we did ablation experiments and compared the experiments with traditional methods and other common object detection networks. Theoretical analysis and experimental result show that the model incorporating with Adan optimizer only requires 1/2-2/3 of the computing power of the original network to obtain performance beyond that of the original network. The mAP@[.5:.95] (mean Average Precision) of the improved network reaches 96.69% and the precision reaches 99.95%. Meanwhile, the mAP@[.5:.95] was improved by 2.79%-11.83% compared to the original YOLOv7 and 41.98%-60.61% compared to other common object detection models. In complex natural scenes, our proposed method is not only time-efficient and has higher recognition accuracy, reaching the level of SOTA

Multi-Risk Source Oil Spill Risk Assessment Based on a Fuzzy Inference System

Oil is one of the most important sources of energy, about 25 percent of which comes from offshore sources. As a result, the transportation of oil tankers, and the construction of offshore oil platforms and subsea pipelines have increased, to facilitate offshore oil exploitation. However, most oil spill risk assessments analyze the impact of one risk source, and rarely consider multiple risk sources in the study area. This paper focuses on three risk sources that may cause oil spills in a certain area, and establishes an oil spill risk assessment model through a fuzzy inference system. Oil spill probabilities for different risk sources are calculated through the model. According to the definition of oil spill risk, the risk probability of multiple risk sources in the study area is obtained, which can provide technical support for regional oil spill emergency capacity and emergency resource allocation

A Self-Adaptive Progressive Support Selection Scheme for Collaborative Wideband Spectrum Sensing

The sampling rate of wideband spectrum sensing for sparse signals can be reduced by sub-Nyquist sampling with a Modulated Wideband Converter (MWC). In collaborative spectrum sensing, the fusion center recovers the spectral support from observation and measurement matrices reported by a network of CRs, to improve the precision of spectrum sensing. However, the MWC has a very high hardware complexity due to its parallel structure; it sets a fixed threshold for a decision without considering the impact of noise intensity, and needs a priori information of signal sparsity order for signal support recovery. To address these shortcomings, we propose a progressive support selection based self-adaptive distributed MWC sensing scheme (PSS-SaDMWC). In the proposed scheme, the parallel hardware sensing channels are scattered on secondary users (SUs), and the PSS-SaDMWC scheme takes sparsity order estimation, noise intensity, and transmission loss into account in the fusion center. More importantly, the proposed scheme uses a support selection strategy based on a progressive operation to reduce missed detection probability under low SNR levels. Numerical simulations demonstrate that, compared with the traditional support selection schemes, our proposed scheme can achieve a higher support recovery success rate, lower sampling rate, and stronger time-varying support recovery ability without increasing hardware complexity

Adaptive and Blind Wideband Spectrum Sensing Scheme Using Singular Value Decomposition

The Modulated Wideband Converter (MWC) can provide a sub-Nyquist sampling for continuous analog signal and reconstruct the spectral support. However, the existing reconstruction algorithms need a priori information of sparsity order, are not self-adaptive for SNR, and are not fault tolerant enough. These problems affect the reconstruction performance in practical sensing scenarios. In this paper, an Adaptive and Blind Reduced MMV (Multiple Measurement Vectors) Boost (ABRMB) scheme based on singular value decomposition (SVD) for wideband spectrum sensing is proposed. Firstly, the characteristics of singular values of signals are used to estimate the noise intensity and sparsity order, and an adaptive decision threshold can be determined. Secondly, optimal neighborhood selection strategy is employed to improve the fault tolerance in the solver of ABRMB. The experimental results demonstrate that, compared with ReMBo (Reduce MMV and Boost) and RPMB (Randomly Projecting MMV and Boost), ABRMB can significantly improve the success rate of reconstruction without the need to know noise intensity and sparsity order and can achieve high probability of reconstruction with fewer sampling channels, lower minimum sampling rate, and lower approximation error of the potential of spectral support

Intelligent Measurement of Morphological Characteristics of Fish Using Improved U-Net

In the smart mariculture, batch testing of breeding traits is a key issue in the breeding of improved fish varieties. The body length (BL), body width (BW) and body area (BA) features of fish are important indicators. They are of great significance in breeding, feeding and classification. To accurately and intelligently obtain the morphological characteristic sizes of fish in actual scenes, data augmentation is first used to greatly expand the published fish dataset, thereby ensuring the robustness of the training model. Then, an improved U-net segmentation and measurement algorithm is proposed, which uses a dilated convolution with a dilation rate 2 and a convolution to partially replace the convolution in the original U-net. This operation can enlarge the partial convolution receptive field and achieve more accurate segmentation for large targets in the scene. Finally, a line fitting method based on the least squares method is proposed, which is combined with the body shape features of fish and can accurately measure the BL and BW of inclined fish. Experimental results show that the Mean Intersection over Union (mIoU) is 97.6% and the average relative error of the area is 0.69%. Compared with the unimproved U-net, the average relative error of the area is reduced to about half. Moreover, with the improved U-net and the line fitting method, the average relative error of BL and the average relative error of BW of inclined fish decrease to 0.37% and 0.61%, respectively

A Water Quality Prediction Method Based on the Deep LSTM Network Considering Correlation in Smart Mariculture

An accurate prediction of cage-cultured water quality is a hot topic in smart mariculture. Since the mariculturing environment is always open to its surroundings, the changes in water quality parameters are normally nonlinear, dynamic, changeable, and complex. However, traditional forecasting methods have lots of problems, such as low accuracy, poor generalization, and high time complexity. In order to solve these shortcomings, a novel water quality prediction method based on the deep LSTM (long short-term memory) learning network is proposed to predict pH and water temperature. Firstly, linear interpolation, smoothing, and moving average filtering techniques are used to repair, correct, and de-noise water quality data, respectively. Secondly, Pearson’s correlation coefficient is used to obtain the correlation priors between pH, water temperature, and other water quality parameters. Finally, a water quality prediction model based on LSTM is constructed using the preprocessed data and its correlation information. Experimental results show that, in the short-term prediction, the prediction accuracy of pH and water temperature can reach 98.56% and 98.97%, and the time cost of the predictions is 0.273 s and 0.257 s, respectively. In the long-term prediction, the prediction accuracy of pH and water temperature can reach 95.76% and 96.88%, respectively