Detecting litchis in a complex natural environment is important for yield estimation and provides reliable support to litchi-picking robots. This paper proposes an improved litchi detection model named YOLOv5-litchi environments. First, we add convolutional block attention module each C3 the backbone of network enhance ability extract feature information. Second, small-object layer enable locate smaller targets performance small targets. Third, Mosaic-9 data augmentation increases diversity...

Crop classification of large-scale agricultural land is crucial for crop monitoring and yield estimation. Hyperspectral image has proven to be an effective method this task. Most current popular hyperspectral methods are based on classification, specifically convolutional neural networks (CNNs) recurrent (RNNs). In contrast, paper focuses semantic segmentation proposes a new transformer-based approach called HyperSFormer classification. The key enhancement the proposed replacement encoder in...

Abstract As one of the world's most popular beverages, tea plays a significant role in improving production efficiency and quality through identification shoots during manufacturing process. However, due to complex morphology, small size, susceptibility factors like lighting obstruction, traditional methods suffer from low accuracy efficiency. In this study, image enhancement techniques such as HSV transformation, horizontal flipping, vertical flipping were applied training dataset improve...

Soil moisture is an important factor determining yield. With the increasing demand for agricultural irrigation water resources, evaluating soil in advance to create a reasonable schedule would help improve resource utilization. This paper established continuous system collecting meteorological information and data from litchi orchard. acquired data, time series model called Deep Long Short-Term Memory (Deep-LSTM) proposed this paper. The Deep-LSTM has five layers with fused predict of...

During the growth season, jujube trees are susceptible to infestation by leaf mite, which reduces fruit quality and productivity. Traditional monitoring techniques for mites time-consuming, difficult, subjective, result in a time lag. In this study, method based on particle swarm optimization (PSO) algorithm extreme learning machine estimation of chlorophyll content (SPAD) under mite was proposed. Initially, image data SPAD values orchards four severities were collected analysis. Six...

Introduction It is crucial to accurately determine the green fruit stage of citrus and formulate detailed conservation flower thinning plans increase yield citrus. However, color fruits similar background, which results in poor segmentation accuracy. At present, when deep learning other technologies are applied agriculture for crop estimation picking tasks, accuracy recognition reaches 88%, area enclosed by PR curve coordinate axis 0.95, basically meets application requirements.To solve...

The development of precision agriculture requires unmanned aerial vehicles (UAVs) to collect diverse data, such as RGB images, 3D point clouds, and hyperspectral images. Recently, convolutional networks have made remarkable progress in downstream visual tasks, while often disregarding the trade-off between accuracy speed UAV-based segmentation tasks. study aims provide further valuable insights using an efficient model named Taoism-Net. findings include following: (1) Prescription maps...

This paper proposes a single image dehazing based on deep neural network that is to deal with haze image. In this paper, we build up restore the hazy We test our method both objective and subjective compare classical for dehazing. Our shows works better than others in reducing Halo effect also does well colorful of input Finally, process faster.

In this paper, we propose an effective and efficient method to remove haze from a single input image. We first fine details the minimum channel of RGB channels by low-pass filter then use it as rough estimation transmission map. Then, refine using saturation HSI color space local contrast. Based on atmospheric scattering model, obtain high-quality haze-removed Results variety hazy images demonstrate effectiveness efficiency our method.

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Abstract:Identifying defects in citrus peels and analyzing fruit morphology are two core challenges quality inspection. In order to more accurately identify minor on peels, we propose a detection model Yolo-FD (Yolo for defects). The is based the Yolov5 network framework, backbone embeds Three-dimensional Coordinate Attention (TDCA) mechanism innovatively designed this study, which significantly enhances model's ability perceive process position information target space. Moreover, employe...

At present, learning-based citrus blossom recognition models based on deep learning are highly complicated and have a large number of parameters. In order to estimate flower quantities in natural orchards, this study proposes lightweight model improved YOLOv4. compress the backbone network, we utilize MobileNetv3 as feature extractor, combined with separable convolution for further acceleration. The Cutout data enhancement method is also introduced simulate nature enhancement. test results...

Abstract:With the continuous development of artificial intelligence and Internet Things, these technologies provide significant technical support for comprehensive evaluation litchi trees.At present, most studies are focused on precise identification mature fruits, lacking a scientific based entire maturation period trees. This study enhances fruits by improving network model algorithms, combines it with analysis process meteorological data during trees, using to assess quality trees.Central...

The Internet of Things(IoT) and hyperspectral technology have been widely applied in the field crop disease monitoring. However, effective integration these two data modalities remains an exigent challenge. This study concentrates on litchi downy blight proposes a model that combines IoT for precocious prediction utilizing artificial intelligence algorithms.In this model, is collected by sensor devices. We proposed 15 sensitive feature factors closely related to utilized Long Short-Term...

The quantitative inversion of the leaf area index (LAI) green plum trees is crucial for orchard field management and yield prediction. data on relative content chlorophyll (SPAD) in leaves environmental from orchards show a significant correlation with LAI. Effectively integrating these two types LAI important to explore. This study proposes multi−source decision fusion model plums based their adjusted determination coefficient (MDF−ADRS). First, three statistical methods—Pearson, Spearman...

The paper firstly explains the meaning of reform in curriculum design about SCM (single-chip microcomputer), and introduces teaching status quo design. Then, scheme its implementation is explicated, which emphasize cultivation project ability creative students through providing several development boards self-designed by our team also offering applicable projects with different difficulties. At last, evaluation summed up.

Signal and system is one of the most important courses in electrical electronics engineering curricula. However, performance traditional teaching approach not satisfied practice. This paper firstly introduces how to integrate MATLAB simulation application into class, then discusses implement lab with online. Some examples are given detail. Finally, effect System course summed up.

We propose a jointly designing transceiver for time-varying multiple-input multiple-output (MIMO) systems. Based on geometric mean decomposition (GMD), the scheme decomposes MIMO channel into parallel subchannels with identical capacities and each subchannel meets certain BER constraints. Thus power loading modulation/demodulation process are particularly simplified because of signal-to-interference-and-noise ratios (SINR) across subchannels. compare average spectral efficiency (ASE) our...