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You searched for: Journal name Computers and electronics in agriculture Remove constraint Journal name: Computers and electronics in agriculture Publication year rev 7978-2022 Remove constraint Publication year rev: 7978-2022 Source 2022 v.194 Remove constraint Source: 2022 v.194
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1. Assessing machine leaning algorithms on crop yield forecasts using functional covariates derived from remotely sensed data

Author:
Luca Sartore; Arthur N. Rosales; David M. Johnson; Clifford H. Spiegelman
Source:
Computers and electronics in agriculture 2022 v.194 pp. 106704
ISSN:
0168-1699
Subject:
agriculture; crop yield; electronics; remote sensing
Abstract:
... Machine learning methods are increasingly used in analyzing remotely sensed data and studying different aspects of agricultural production. In particular, several of these flexible models are widely adopted to predict regional crop yield during or after the growing season. However, most existing models cannot be applied when dealing with functional covariates. In this paper, an approach based on m ...
DOI:
10.1016/j.compag.2022.106704

2. Assessment of ammonia sensors and photoacoustic measurement systems using a gas calibration unit

Author:
Naemi von Jasmund; Alexander J. Schmithausen; Manuel S. Krommweh; Manfred Trimborn; Peter Boeker; Wolfgang Büscher
Source:
Computers and electronics in agriculture 2022 v.194 pp. 106744
ISSN:
0168-1699
Subject:
agricultural sciences; agriculture; air quality; ammonia; barns; calibration; electrochemistry; electronics; relative humidity
Abstract:
... The accurate measurement of environmentally-relevant gases such as ammonia (NH₃) is increasingly important, especially in agricultural science. Accurate and continuous measurement of gases is essential for evaluating NH₃ and other gases as resource-based indicators of air quality in forced ventilation barns and for determining emission rates. To better assess measuring devices under different barn ...
DOI:
10.1016/j.compag.2022.106744

3. Automated detection of boundary line in paddy field using MobileV2-UNet and RANSAC

Author:
Yong He; Xiya Zhang; Zeqing Zhang; Hui Fang
Source:
Computers and electronics in agriculture 2022 v.194 pp. 106697
ISSN:
0168-1699
Subject:
agricultural machinery and equipment; agriculture; algorithms; automatic detection; electronics; lighting; models; paddies; precision agriculture
Abstract:
... The detection of boundary lines in farmlands is critical for precision agriculture and automatic navigation. This study proposed a novel method for automatic detection of boundary lines in paddy fields based on images acquired from a vision system. These images were collected from different environmental conditions with wheel marks, shadows, weeds, and uneven illumination in paddy fields. To allev ...
DOI:
10.1016/j.compag.2022.106697

4. Automated workflow analysis in vegetable grafting using an Ultra-Wide Band based real-time indoor location tracking system

Author:
Bijoy Dripta Barua Chowdhury; Young-Jun Son; Chieri Kubota; Russell Tronstad
Source:
Computers and electronics in agriculture 2022 v.194 pp. 106773
ISSN:
0168-1699
Subject:
agriculture; algorithms; automation; electronics; horticultural crops; vegetables
Abstract:
... Traditional workflow analysis requires intensive manual processes to extract meaningful information from raw sensory data. A novel framework for workflow analysis is developed based on a real-time indoor location tracking system utilizing Ultra-Wide Band technology. The proposed framework is applied to identify worker and task associations in a labor-intensive horticultural plant production proces ...
DOI:
10.1016/j.compag.2022.106773
CHORUS:
10.1016/j.compag.2022.106773

5. Biophysical parameter estimation of crops from polarimetric synthetic aperture radar imagery with data-driven polynomial chaos expansion and global sensitivity analysis

Author:
Mehmet Furkan Çelik; Esra Erten
Source:
Computers and electronics in agriculture 2022 v.194 pp. 106781
ISSN:
0168-1699
Subject:
agriculture; barley; canola; electronics; leaf area index; normalized difference vegetation index; oats; peas; polarimetry; regression analysis; synthetic aperture radar; uncertainty
Abstract:
... Data-driven machine learning regression methods are easy to implement and applicable to a wide-range of data in biophysical parameter estimation and have become a common approach in the remote sensing field. Among the regression methods, polynomial chaos expansion (PCE) is one of the reliable and interesting ones due to its tight relationship with uncertainty quantification. One of the advantages ...
DOI:
10.1016/j.compag.2022.106781

6. CANBUS-enabled activity-based costing for leveraging farm management

Author:
Michele Mattetti; Marco Medici; Maurizio Canavari; Massimiliano Varani
Source:
Computers and electronics in agriculture 2022 v.194 pp. 106792
ISSN:
0168-1699
Subject:
agricultural machinery and equipment; agriculture; capital; economic impact; electronics; farm management; farms; labor; statistics; tractors; wireless technology
Abstract:
... The improvement of economic management in farms has become an important research topic in recent decades as the most dominant feature of current farm management information systems (FMIS). Production cost statistics allow farmers to assess the economic impact of farm activities and compare historical data against previous farm practices or competitors’ activities. Therefore, the availability of re ...
DOI:
10.1016/j.compag.2022.106792

7. CG-ANER: Enhanced contextual embeddings and glyph features-based agricultural named entity recognition

Author:
Xuchao Guo; Shuhan Lu; Zhan Tang; Zhao Bai; Lei Diao; Han Zhou; Lin Li
Source:
Computers and electronics in agriculture 2022 v.194 pp. 106776
ISSN:
0168-1699
Subject:
agriculture; data collection; electronics; neural networks
Abstract:
... In recent years, deep learning has greatly improved the performance of named entity recognition models in various fields, especially in the agricultural domain. However, most existing works only utilize word embedding models to generate the context-independent embeddings, which is limited in modeling polysemous words. Moreover, the abundant morphological information in agricultural texts has not b ...
DOI:
10.1016/j.compag.2022.106776

8. ChickenNet - an end-to-end approach for plumage condition assessment of laying hens in commercial farms using computer vision

Author:
Christian Lamping; Marjolein Derks; Peter Groot Koerkamp; Gert Kootstra
Source:
Computers and electronics in agriculture 2022 v.194 pp. 106695
ISSN:
0168-1699
Subject:
agriculture; automation; computer vision; electronics; humans; neural networks; plumage
Abstract:
... Regular plumage condition assessment in laying hens is essential to monitor the hens’ welfare status and to detect the occurrence of feather pecking activities. However, in commercial farms this is a labor-intensive, manual task. This study proposes a novel approach for automated plumage condition assessment using computer vision and deep learning. It presents ChickenNet, an end-to-end convolution ...
DOI:
10.1016/j.compag.2022.106695

9. Classification of crop pests based on multi-scale feature fusion

Author:
Depeng Wei; Jiqing Chen; Tian Luo; Teng Long; Huabin Wang
Source:
Computers and electronics in agriculture 2022 v.194 pp. 106736
ISSN:
0168-1699
Subject:
agriculture; data collection; electronics
Abstract:
... Crop diseases and insect pests are a serious natural disaster, which needs to be predicted and monitored in time to ensure the output of crops. Due to the wide variety of pests and the similar morphology of crops in the early stages of growth, it is difficult for agricultural workers to accurately identify various types of pests. Crop insects have brought huge challenges to the prevention and cont ...
DOI:
10.1016/j.compag.2022.106736

10. Classification of urban tree species using multi-features derived from four-season RedEdge-MX data

Author:
Huaipeng Liu
Source:
Computers and electronics in agriculture 2022 v.194 pp. 106794
ISSN:
0168-1699
Subject:
agriculture; electronics; models; statistical analysis; texture; tree growth; trees
Abstract:
... In the remote sensing discrimination of tree species, the use of multi-temporal images may considerably improve the accuracy of recognising tree species. This study aimed to explore the effectiveness of the RedEdge-MX sensor, which was used to capture images during the key node period of tree growth, for driving the identification of tree species. The spectral band, texture and digital surface mod ...
DOI:
10.1016/j.compag.2022.106794

11. Combine harvester remote monitoring system based on multi-source information fusion

Author:
Zhaomei Qiu; Gaoxiang Shi; Bo Zhao; Xin Jin; Liming Zhou
Source:
Computers and electronics in agriculture 2022 v.194 pp. 106771
ISSN:
0168-1699
Subject:
agriculture; algorithms; combine harvesters; electronics
Abstract:
... Combine harvesters are prone to blockage, belt burnout and maintenance problems due to their complex transmission structure and variable operating environment. Therefore, a remote monitoring system of combine harvesters based on multi-source information fusion was designed, which could not only realize effective monitoring of combine harvesters, but also realize the functions of fault diagnosis an ...
DOI:
10.1016/j.compag.2022.106771

12. Combining multicolor fluorescence imaging with multispectral reflectance imaging for rapid citrus Huanglongbing detection based on lightweight convolutional neural network using a handheld device

Author:
Chengcheng He; Xiaobin Li; Yunshi Liu; Biyun Yang; Zhiwei Wu; Shipei Tan; Dapeng Ye; Haiyong Weng
Source:
Computers and electronics in agriculture 2022 v.194 pp. 106808
ISSN:
0168-1699
Subject:
Citrus sinensis; agriculture; cultivars; data collection; disease detection; electronics; fluorescence; greening disease; neural networks; orchards; photosynthesis; rapid methods; reflectance; secondary metabolites
Abstract:
... Citrus Huanglongbing (HLB) has posed a great challenge to the citrus production. Timely removal of HLB infected trees was considered as one of the most effective strategies for citrus orchard management. Therefore, rapid detection of HLB disease is urgently needed. The study was aimed to propose an effective method for HLB disease detection by developing a handheld device to capture multicolor flu ...
DOI:
10.1016/j.compag.2022.106808

13. Dairy cow lameness detection using a back curvature feature

Author:
Bo Jiang; Huaibo Song; Han Wang; Changying Li
Source:
Computers and electronics in agriculture 2022 v.194 pp. 106729
ISSN:
0168-1699
Subject:
agriculture; algorithms; computer vision; dairy cows; dairy industry; economic impact; electronics; lameness; neural networks
Abstract:
... Manual detection of lameness poses several problems, such as difficulty in finding sudden, severe or early lameness behavior. A dairy cow’s lameness is closely related to curvature of the cow’s back. Focusing on the curvature features of dairy cows’ backs, this study proposes a lameness detection method that combines machine vision technology with a deep learning algorithm. Firstly, the FLYOLOv3 a ...
DOI:
10.1016/j.compag.2022.106729

14. Data-centric analysis of on-tree fruit detection: Experiments with deep learning

Author:
Xu (Annie) Wang; Julie Tang; Mark Whitty
Source:
Computers and electronics in agriculture 2022 v.194 pp. 106748
ISSN:
0168-1699
Subject:
agriculture; data collection; electronics; fruits; grapes; models; precision agriculture
Abstract:
... Due to the rapid development of deep learning, object detection models have become the current tool of choice for on-tree fruit detection in precision agriculture. The pipeline of fruit detection based on deep learning generally starts from custom dataset collection, then image annotation, then training the object detection model, and finally determination of its accuracy and running the trained m ...
DOI:
10.1016/j.compag.2022.106748

15. Deep learning-based segmentation of multiple species of weeds and corn crop using synthetic and real image datasets

Author:
Artzai Picon; Miguel G. San-Emeterio; Arantza Bereciartua-Perez; Christian Klukas; Till Eggers; Ramon Navarra-Mestre
Source:
Computers and electronics in agriculture 2022 v.194 pp. 106719
ISSN:
0168-1699
Subject:
Abutilon theophrasti; Amaranthus retroflexus; Chenopodium; Digitaria sanguinalis; Echinochloa crus-galli; Setaria verticillata; Zea mays; agriculture; algorithms; corn; crop yield; crops; data collection; electronics; grasses; herbicides; image analysis; models; soil; solar radiation
Abstract:
... Weeds compete with productive crops for soil, nutrients and sunlight and are therefore a major contributor to crop yield loss, which is why safer and more effective herbicide products are continually being developed. Digital evaluation tools to automate and homogenize field measurements are of vital importance to accelerate their development. However, the development of these tools requires the ge ...
DOI:
10.1016/j.compag.2022.106719

16. Deep multi-scale dual-channel convolutional neural network for Internet of Things apple disease detection

Author:
Wenzhuo Zhang; Guoxiong Zhou; Aibin Chen; Yahui Hu
Source:
Computers and electronics in agriculture 2022 v.194 pp. 106749
ISSN:
0168-1699
Subject:
Internet; agriculture; apples; cameras; color; databases; disease detection; electronics; neural networks; solar radiation; texture
Abstract:
... It is difficult to identify similar apples diseases due to the complicated changes in color and texture of diseased parts. In order to solve this problem, an Internet of Things (IoT) system for apple disease detection based on deep multi-scale dual-channel convolutional neural network (DMCNN) was proposed in this paper. Firstly, the image was transformed into HSV and RGB color subspaces through co ...
DOI:
10.1016/j.compag.2022.106749

17. Design and testing of a crop growth sensor aboard a fixed-wing unmanned aerial vehicle

Author:
Huali Yuan; Jiayu Yang; Xiaoping Jiang; Yan Zhu; Weixing Cao; Jun Ni
Source:
Computers and electronics in agriculture 2022 v.194 pp. 106762
ISSN:
0168-1699
Subject:
agriculture; altitude; computer software; electronics; flight; leaf area index; leaf dry mass; leaf nitrogen content; rice; simulation models; spectroradiometers; unmanned aerial vehicles
Abstract:
... The aim of this study is to overcome disturbance of downwash flow field caused by the low-altitude operation of a multirotor unmanned aerial vehicle (UAV) on crop canopies and interference in spectral reflection information of canopies. For this purpose, a crop growth sensor aboard a fixed-wing UAV was developed through flight dynamics simulation analysis of a fixed-wing UAV. This sensor can colle ...
DOI:
10.1016/j.compag.2022.106762

18. Detecting occluded Y-shaped fruit tree segments using automated iterative training with minimal labeling effort

Author:
Keenan Granland; Rhys Newbury; Zijue Chen; David Ting; Chao Chen
Source:
Computers and electronics in agriculture 2022 v.194 pp. 106747
ISSN:
0168-1699
Subject:
agriculture; apples; automation; electronics; fruit trees; humans; neural networks
Abstract:
... Training of convolutional neural networks for semantic segmentation of fruit tree branches requires accurate pixel-wise labeling which requires large amounts of human effort. The human-in-the-loop method, where a human annotator corrects the outputs of a neural network, reduces labeling effort; however, it requires human intervention for each image. This paper describes an iterative training metho ...
DOI:
10.1016/j.compag.2022.106747

19. Detection of early stage bruise in apples using optical property mapping

Author:
Zhizhong Sun; Dong Hu; Lijuan Xie; Yibin Ying
Source:
Computers and electronics in agriculture 2022 v.194 pp. 106725
ISSN:
0168-1699
Subject:
agriculture; apple peels; apples; electronics; industry; postharvest storage; transportation
Abstract:
... Apple bruises, which are mainly caused by external forces during picking, transportation and marketing, could cause significant economic losses in postharvest storage. Considering the short time window (1–2 h) between bruises occurring and apples are stored, there needs a technique that can identify the invisible early stage bruises (defined as within 1–2 h after occurring), which cannot be comple ...
DOI:
10.1016/j.compag.2022.106725

20. Developing an automatic conjunctive surface-groundwater operating system for sustainable agricultural water distribution

Author:
Ardalan Askari Fard; S. Mehdy Hashemy Shahdany; Saman Javadi; J.M. Maestre
Source:
Computers and electronics in agriculture 2022 v.194 pp. 106774
ISSN:
0168-1699
Subject:
agriculture; aquifers; automation; electronics; energy; groundwater; groundwater extraction; irrigation; surface water; water distribution; water shortages; Iran
Abstract:
... Groundwater extraction from aquifers is the primary approach for supplying agricultural water demand due to the inadequate, unfair, and unreliable distribution of surface water (SW) systems. In this regard, the present study developed an automated operating system for conjunctive surface and groundwater (GW) resources. By centralized model predictive control (CMPC), an automated SW distribution sy ...
DOI:
10.1016/j.compag.2022.106774