Showing papers in "Computers and Electronics in Agriculture in 2022"

TL;DR: Wang et al. as discussed by the authors proposed an improved SE-YOLOv5 network model for the recognition of tomato virus diseases, which used a squeeze-and-excitation module to realize the extraction of key features, using a human visual attention mechanism for reference.

TL;DR: In this article , the authors proposed a real-time object detection framework Dense-YOLOv4 based on an improved version of the YOLO-v4 algorithm by including DenseNet in the backbone to optimize feature transfer and reuse.

TL;DR: In this article , the authors conduct a comprehensive review based on bibliometrics to summarize and structure existing academic literature and reveal current research trends and hotspots, which indicates that remote sensing, precision agriculture, deep learning, machine learning, and the Internet of Things are critical topics related to agricultural drones.

TL;DR: In this article , a review of the literature on the prediction of livestock behaviour from raw accelerometer data, with a specific focus on livestock ruminants, is presented, based on 66 surveyed articles, providing reliable evidence of a 3-step methodology common to all studies.

TL;DR: In this article , a new YOLOv5-B model was constructed using the InvolutionBottleneck module in the network structures of CNN and improving the loss function to improve the accuracy and speed of small target detection.

TL;DR: In this paper , the potential of ICT technologies in traditional agriculture, as well as the challenges that may arise when they are used in farming techniques are discussed, and a thorough review of the most recent literature in each area of expertise is presented.

TL;DR: In this paper , a novel underwater image enhancement method based on Retinex-inspired color correction and detail preserved fusion technology is proposed to cope with color cast, blurring, and low contrast of underwater images for display and further analysis.

TL;DR: Wang et al. as mentioned in this paper proposed a combined end-to-end neural network to detect and track the abnormal behavior of porphyry seabream, which passes the initial value of the target into tracking algorithm, and the tracking algorithm tracks subsequent frames to achieve end to end abnormal fish behavior detection and achieve high speed and accurate tracking of abnormal behavior individuals.

TL;DR: The role of IoT in precision agriculture and smart greenhouses has been reinforced by recent R&D projects, growing commercialization of IoT infrastructure, and related technologies such as satellites, artificial intellige nce, sensors, actuators, uncrewed aerial vehicles, big data analytics, intelligent machines, and radio-frequency identification devices as discussed by the authors .

TL;DR: Wang et al. as mentioned in this paper proposed a convolutional neural network based on Inception and residual structure with an embedded modified Convolutional Block Attention module (CBAM), aiming to improve the classification of plant leaf diseases.

TL;DR: Wang et al. as discussed by the authors proposed a deep feature descriptor based on transfer learning to obtain a high-level latent feature representation, and integrated the deep features with traditional handcrafted features by feature fusion to capture the local texture information in plant leaf images.

TL;DR: In this article , a new deep neural network called RTSD-Net is proposed based on stat-of-art light-weighted YOLOv4-tiny with reduced layers and modified structure for real-time strawberry detection under infield condition.

TL;DR: In this paper , a real-time apple defects inspection method was proposed based on YOLO V4 deep learning algorithm, where the input images were generated by combining NIR images in three consecutive rubber roller stations, and a non-maximum suppression (NMS) method based on L1 norm was proposed to remove redundant prediction box after fine-tuning the pruned network.

TL;DR: In this article , the authors presented the state-of-the-art CNN detectors for citrus leaf disease detection, evaluated based on their precision, recall, and other valuable parameters such as training parameters, inference time, memory usage, speed and accuracy trade-off for each model.

TL;DR: Wang et al. as discussed by the authors proposed a two-level fusion network with a variable universe for detecting and classifying the tea buds and established an image dataset of tea buds in the natural growth state from different angles.

TL;DR: Wang et al. as mentioned in this paper proposed a two-level fusion network with a variable universe to solve the problem of detection and classification of different grades of tea in mechanical picking for famous and high-quality tea.

TL;DR: In this article , a UAV carrying multispectral sensor was used to collect visible and near-infrared images of the canopy at the jointing stage of maize under six fertilization levels to obtain VIs.

TL;DR: In this article, a blockchain enabled optimization approach for greenhouse system is proposed to provide an optimal greenhouse environment that are; prediction, optimization, and finally controlling, where the Kalman filter algorithm is employed for predicting the greenhouse sensor data.

TL;DR: In this paper , a blockchain enabled optimization approach for greenhouse system is proposed to provide an optimal greenhouse environment that are; prediction, optimization, and finally controlling, where the Kalman filter algorithm is employed for predicting the greenhouse sensor data and the optimal parameters are computed for the indoor greenhouse environment.

TL;DR: In this paper , the authors reviewed applications of machine vision in agricultural robot navigation from 2017 to 2021 and summarized the challenges that machine vision needs to overcome in agriculture robot navigation, and discussed the challenges of using machine vision for autonomous navigation of agricultural robots.

TL;DR: An overview of the evolution of GAN architectures followed by a systematic review of their application to agriculture can be found in this article , involving various vision tasks for plant health, weeds, fruits, aquaculture, animal farming, plant phenotyping as well as postharvest detection of fruit defects.

TL;DR: In this article , an effective automatic detection and severity analysis method is proposed for grape black measles disease based on deep learning and fuzzy logic, which can be used to classify grape leaves with different disease risks, based on combination of image analysis and statistical calculation.

TL;DR: Zhang et al. as discussed by the authors proposed a near real-time method for detecting green (early-stage), green-red-mixed (mid-stage; red varieties), or red apples (harvest-stage, red varieties).

TL;DR: In this article , a geometry-aware network, A3N, is proposed to perform end-to-end instance segmentation and grasping estimation using both color and geometry sensory data from a RGB-D camera.

TL;DR: Wang et al. as discussed by the authors proposed a diameter correction method to eliminate the effect of fruit size on transmission spectra more effectively than multiplicative scattering correction (MSC) and standard normal variable (SNV) models based on DCM pretreated spectra.

TL;DR: In this article , a machine vision system is developed to first construct a dataset of 8048 high-magnification (4.5 x) images of damaged rice refractions, that are obtained through the on-field collection.

TL;DR: In this article , an improved Mask Region-based Convolutional Neural Network (Mask RCNN) was proposed to segment apples in an orchard during the growth period to obtain accurate growth information.

TL;DR: An overview of GANs in agriculture can be found in this paper , where the authors present an overview of the evolution of generative adversarial network (GAN) architectures followed by a first systematic review of various applications in agriculture and food systems.

TL;DR: Zhang et al. as mentioned in this paper proposed a lightweight dense-scale network (LDSNet) for real-world corn leaf disease image identification, which improves the adaptability to the scale change of corn leaf diseases through the dense connection of different dilation rate convolutions.

TL;DR: Zhang et al. as discussed by the authors developed a vegetable disease identification model, DTL-SE-ResNet50, optimized by SENet and pre-trained by ImageNet to form a new model, which was trained with the AI Challenger 2018 public database to obtain a new weight.