Artificial Intelligence in Agriculture最新文献_第10页

Species Recognition in Flowers 花卉中的物种识别

Artificial Intelligence in Agriculture Pub Date : 2021-09-28 DOI: 10.1201/9781003245759-13

Rajesh Singh, A. Gehlot, M. Prajapat, Bhupendra Singh

引用次数: 0

Tools for Artificial Intelligence 人工智能工具

Artificial Intelligence in Agriculture Pub Date : 2021-09-28 DOI: 10.1201/9781003245759-9

Rajesh Singh, A. Gehlot, M. Prajapat, Bhupendra Singh

引用次数: 0

Retrieval of Flower Videos Based on a Query With Multiple Species of Flowers 基于多种花查询的花卉视频检索

Artificial Intelligence in Agriculture Pub Date : 2021-01-18 DOI: 10.20944/PREPRINTS202101.0318.V1

Manjunath Aradhya, Jyothi Vk, Sharath Kumar, Guru Ds

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引用次数: 1

Worldwide trends in the scientific production of literature on traceability in food safety: A bibliometric analysis 食品安全可追溯性文献科学生产的全球趋势:文献计量学分析

Artificial Intelligence in Agriculture Pub Date : 2021-01-01 DOI: 10.1016/j.aiia.2021.11.002

Aditya Sinha , Prashant Priyadarshi , Mani Bhushan , Dharmendra Debbarma

{"title":"Worldwide trends in the scientific production of literature on traceability in food safety: A bibliometric analysis","authors":"Aditya Sinha , Prashant Priyadarshi , Mani Bhushan , Dharmendra Debbarma","doi":"10.1016/j.aiia.2021.11.002","DOIUrl":"10.1016/j.aiia.2021.11.002","url":null,"abstract":"<div><p>Food traceability is an important aspect of the food safety supply chain to ensure efficient tracking of produce to check contamination and other foodborne diseases. The health and nutrition response after the Covid-19 pandemic requires a robust and diverse food supply chain in which traceability could play a major role. Since it is an emerging field of study with growing interest in the technological front, it is important to study the scientific trend and research activities. This study provides an important insight into the food safety value chain response towards modern food safety management systems through scientometric analysis. Scopus database was used to retrieve the documents from the year 1992–2021. The research papers and conference papers were only chosen. Vosviewer software was used to carry out the scientometric analysis. The distribution and growth trend of documents, country-level distribution of publications, the relationship between authors and co-authors, etc., were analyzed. The intensity of publications from different countries and the collaborations was analyzed using bibliometrix R-package. The year-wise research publication showed a rapid increase in the researchers conducted on traceability systems to enhance food safety from 2014 onwards, mainly from the USA and China. However, the research appeared to be in the developing phase compared to other technology implementation and automation advancements.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"5 ","pages":"Pages 252-261"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589721721000337/pdfft?md5=4720e9eda367c90e7f698575224424e9&pid=1-s2.0-S2589721721000337-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48840384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Retrieval of flower videos based on a query with multiple species of flowers 基于多种花卉查询的花卉视频检索

Artificial Intelligence in Agriculture Pub Date : 2021-01-01 DOI: 10.1016/j.aiia.2021.11.001

V.K. Jyothi , V.N. Manjunath Aradhya , Y.H. Sharath Kumar , D.S. Guru

{"title":"Retrieval of flower videos based on a query with multiple species of flowers","authors":"V.K. Jyothi , V.N. Manjunath Aradhya , Y.H. Sharath Kumar , D.S. Guru","doi":"10.1016/j.aiia.2021.11.001","DOIUrl":"https://doi.org/10.1016/j.aiia.2021.11.001","url":null,"abstract":"<div><p>Searching, recognizing and retrieving a video of interest from a large collection of a video data is an instantaneous requirement. This requirement has been recognized as an active area of research in computer vision, machine learning and pattern recognition. Flower video recognition and retrieval is vital in the field of floriculture and horticulture. In this paper we propose a model for the retrieval of videos of flowers. Initially, videos are represented with keyframes and flowers in keyframes are segmented from their background. Then, the model is analysed by features extracted from flower regions of the keyframe. A Linear Discriminant Analysis (LDA) is adapted for the extraction of discriminating features. Multiclass Support Vector Machine (MSVM) classifier is applied to identify the class of the query video. Experiments have been conducted on relatively large dataset of our own, consisting of 7788 videos of 30 different species of flowers captured from three different devices. Generally, retrieval of flower videos is addressed by the use of a query video consisting of a flower of a single species. In this work we made an attempt to develop a system consisting of retrieval of similar videos for a query video consisting of flowers of different species.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"5 ","pages":"Pages 262-277"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589721721000325/pdfft?md5=fd578c5df6736d41131cea584293ba6c&pid=1-s2.0-S2589721721000325-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72242071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling the energy gain reduction due to shadow in flat-plate solar collectors; Application of artificial intelligence 平板太阳能集热器中阴影对能量增益降低的影响人工智能的应用

Artificial Intelligence in Agriculture Pub Date : 2021-01-01 DOI: 10.1016/j.aiia.2021.08.002

Morteza Taki, Rouhollah Farhadi

{"title":"Modeling the energy gain reduction due to shadow in flat-plate solar collectors; Application of artificial intelligence","authors":"Morteza Taki, Rouhollah Farhadi","doi":"10.1016/j.aiia.2021.08.002","DOIUrl":"10.1016/j.aiia.2021.08.002","url":null,"abstract":"<div><p>Energy lost due to shadow in the absorber plate of solar collectors can decrease the solar energy gain. In some studies, mathematical modeling was applied for calculating the energy gain reduction due to shadow in flat-plate solar collectors. In this study, ANN method was developed for modeling the energy gain reduction. Multilayer Perceptron (MLP) with one hidden layer and a range of neurons (5–30) by two training algorithms (LM and BR) and tangent sigmoid activation function (TanSig) were used by help of K-fold cross validation method. In the first section, six set of solar collector dimensions were used (1×1; 1×1.5; 1×2; 1.5×1.5; 1.5×2 and 2×2). In the second section all the range of dimensions were used as the inputs. The results of the first section showed that MLP with BR training algorithm can predict the energy gain reduction with minimum MAPE and RMSE in all the categories. The best results related to (1.5×1.5) dimension that achieved a MAPE of 0.15 ± 0.09% and RMASE of 4.42 ± 2.43 KJm<sup>−2</sup> year<sup>−1</sup>, respectively. The results of the second section indicated that BR is a better training algorithm than LM. The MAPE and R<sup>2</sup> factors for the best topology (5-27-1) were 0.0610 ± 0.0051% and 0.9999 ± 0.0001, respectively. The results of the sensitivity analysis showed that height has the biggest impact on total energy gain reduction due to shadow in flat-plate solar collectors. Finally, the results of this study indicated that by using ANN and decrease the energy lost, the efficiency of solar collectors can be increased in all applications such as industry and agriculture.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"5 ","pages":"Pages 185-195"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S258972172100026X/pdfft?md5=b4e3ffedcede3aad300356cfa67e1d28&pid=1-s2.0-S258972172100026X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54191247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A novel elemental composition based prediction model for biochar aromaticity derived from machine learning 基于机器学习的生物炭芳香性元素组成预测模型

Artificial Intelligence in Agriculture Pub Date : 2021-01-01 DOI: 10.1016/j.aiia.2021.06.002

Hongliang Cao , Yaime Jefferson Milan , Sohrab Haghighi Mood , Michael Ayiania , Shu Zhang , Xuzhong Gong , Electo Eduardo Silva Lora , Qiaoxia Yuan , Manuel Garcia-Perez

{"title":"A novel elemental composition based prediction model for biochar aromaticity derived from machine learning","authors":"Hongliang Cao , Yaime Jefferson Milan , Sohrab Haghighi Mood , Michael Ayiania , Shu Zhang , Xuzhong Gong , Electo Eduardo Silva Lora , Qiaoxia Yuan , Manuel Garcia-Perez","doi":"10.1016/j.aiia.2021.06.002","DOIUrl":"10.1016/j.aiia.2021.06.002","url":null,"abstract":"<div><p>The measurement of aromaticity in biochars is generally conducted using solid state <sup>13</sup>C nuclear magnetic resonance spectroscopy, which is expensive, time-consuming, and only accessible in a small number of research-intensive universities. Mathematical modelling could be a viable alternative to predict biochar aromaticity from other much easier accessible parameters (e.g. elemental composition). In this research, Genetic Programming (GP), an advanced machine learning method, is used to develop new prediction models. In order to identify and evaluate the performance of prediction models, an experimental data set with 98 biochar samples collected from the literature was utilized. Due to the benefits of the intelligence iteration and learning of GP algorithm, a kind of underlying exponential relationship between the elemental compositions and the aromaticity of biochars is disclosed clearly. The exponential relationship is clearer and simpler than the polynomial mapping relationships implicated by Maroto-Valer, Mazumdar, and Mazumdar-Wang models. In this case, a novel exponential model is proposed for the prediction of biochar aromaticity. The proposed exponential model appears better prediction accuracy and generalization ability than existing polynomial models during the statistical parameter evaluation.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"5 ","pages":"Pages 133-141"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.aiia.2021.06.002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54191073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Reducing deep learning network structure through variable reduction methods in crop modeling 作物建模中通过变量约简方法简化深度学习网络结构

Artificial Intelligence in Agriculture Pub Date : 2021-01-01 DOI: 10.1016/j.aiia.2021.09.001

Babak Saravi , A. Pouyan Nejadhashemi , Prakash Jha , Bo Tang

{"title":"Reducing deep learning network structure through variable reduction methods in crop modeling","authors":"Babak Saravi , A. Pouyan Nejadhashemi , Prakash Jha , Bo Tang","doi":"10.1016/j.aiia.2021.09.001","DOIUrl":"10.1016/j.aiia.2021.09.001","url":null,"abstract":"<div><p>Crop models are widely used to predict plant growth, water input requirements, and yield. However, existing models are very complex and require hundreds of variables to perform accurately. Due to these shortcomings, large-scale applications of crop models are limited. In order to address these limitations, reliable crop models were developed using a deep neural network (DNN) – a new approach for predicting crop yields. In addition, the number of required input variables was reduced using three common variable selection techniques: namely Bayesian variable selection, Spearman's rank correlation, and Principal Component Analysis Feature Extraction. The reduced-variable DNN models were capable of estimating future crop yields for 10,000,000 different weather and irrigation scenarios while maintaining comparable accuracy levels to the original model that used all input variables. To establish clear superiority of the methodology, the results were also compared with a very recent feature selection algorithm called min-redundancy max-relevance (mRMR). The results of this study showed that the Bayesian variable selection was the best method for achieving the aforementioned goals. Specifically, the final Bayesian-based DNN model with a structure of 10 neurons in 5 layers performed very similarly (78.6% accuracy) to the original DNN crop model with 400 neurons in 10 layers, even though the size of the neural network was reduced by 80-fold. This effort can help promote sustainable agricultural intensifications through the large-scale application of crop models.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"5 ","pages":"Pages 196-207"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589721721000271/pdfft?md5=33b6742cab8fc2c67973faaac5cf0f20&pid=1-s2.0-S2589721721000271-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54191298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Erratum regarding missing Declaration of Competing Interest statements in previously published articles 关于先前发表的文章中遗漏竞争利益声明的勘误表

Artificial Intelligence in Agriculture Pub Date : 2021-01-01 DOI: 10.1016/j.aiia.2021.01.001

引用次数: 0

An investigation into the potential of Gabor wavelet features for scene classification in wild blueberry fields Gabor小波特征在野生蓝莓田场景分类中的潜力研究

Artificial Intelligence in Agriculture Pub Date : 2021-01-01 DOI: 10.1016/j.aiia.2021.03.001

Gashaw Ayalew , Qamar Uz Zaman , Arnold W. Schumann , David C. Percival , Young Ki Chang

{"title":"An investigation into the potential of Gabor wavelet features for scene classification in wild blueberry fields","authors":"Gashaw Ayalew , Qamar Uz Zaman , Arnold W. Schumann , David C. Percival , Young Ki Chang","doi":"10.1016/j.aiia.2021.03.001","DOIUrl":"10.1016/j.aiia.2021.03.001","url":null,"abstract":"<div><p>A Gabor wavelets based technique was investigated as a potential tool for scene classification (into one of bare patch, plant, or weed) for its ultimate utility in site-specific application of agrochemicals in wild blueberry fields.</p><p>Images were gathered from five sites located in central Nova Scotia, Canada. Gabor wavelet features extracted from these images were used to classify scenes according to visually determined classes using step-wise linear discriminant analysis.</p><p>For individual fields, classification accuracy attained ranged between 87.9% and 98.3%; selected Gabor features ranged between 27 and 72; contextual accuracy for herbicide ranged between 67.5% and 96.7%, and contextual accuracy for fertilizer ranged between 63.6% and 97.1%. The pooled scenes yielded a classification accuracy of 81.4%, and contextual accuracy figures of 61.1% and 73.1% for herbicide and fertilizer, respectively, with selected Gabor features of 36.</p><p>Calibrations based on LDA coefficients from the pooled scenes could help avoid the need to re-calibrate for each field, whereas those based on individual field LDA coefficients could improve accuracy, hence enable saving on expensive agrochemicals.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"5 ","pages":"Pages 72-81"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.aiia.2021.03.001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"94014456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2