Published by: Elsevier
https://www.sciencedirect.com/journal/information-processing-in-agriculture
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 637 | 12 | 13 | 9 |
| Engineering and Technology | 990 | 8 | 10 | 8 |
The journal focuses on Artificial intelligence, Artificial neural network, Pattern recognition, Horticulture and Mean squared error. It facilitates discussions on Artificial intelligence that incorporate concepts from other fields like Machine learning and Computer vision. Computer vision research is the primary subject tackled in it with a focus on Machine vision.
The work on Artificial neural network addressed in the journal expands to the thematically related Adaptive neuro fuzzy inference system. Segmentation is a key component of Pattern recognition research discussed in the journal. The study on Segmentation presented in it intersects with subjects under the field of Pixel.
Horticulture research discussed connects with the study of Water content. The Mean squared error research discussed is included in the broader subject of Statistics.
The journal articles are organized to address concerns in the fields of Artificial intelligence, Environmental engineering, Agronomy, Pattern recognition and Artificial neural network. The journal articles facilitate discussions on Artificial intelligence that incorporate concepts from other fields like Machine learning and Computer vision. The published articles focus on Artificial neural network but the discussions also offer insight into other areas such as Segmentation, Statistics, Cluster analysis and Crop yield.
The journal aims to foster the development of research in Artificial intelligence, Artificial neural network, Pattern recognition, Machine learning and Convolutional neural network. While work presented in it provided substantial information on Artificial intelligence, it also covered topics in Field (computer science) and Computer vision. It focuses on Artificial neural network but the discussions also offer insight into other areas such as Mean squared error, Biological system and Cross-validation.
The studies on Pattern recognition discussed can also contribute to research in the domains of Image (mathematics) and Random forest. The presented studies in Decision tree and Naive Bayes classifier fall within the purview of Machine learning but it also intertwines with topics in Plant disease. The journal explores research in Pixel and overlapping concepts in Segmentation to expand the discourse in Machine vision.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in Information Processing in Agriculture (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in Information Processing in Agriculture (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 7.61% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 32.94% were posted by at least one author from the top 10 institutions publishing in the journal. Another 7.06% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 22.35% of all publications and 37.65% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
Another rewarding avenue to consider for those interested in research topics specifically covered under 'Information Processing in Agriculture', could be a career in teaching and research within private schools in Kansas. A path towards becoming a **private school teacher** offers the chance to introduce young minds to these crucial topics, and to inspire a future generation of researchers. In Kansas, the requirements to become a private school teacher involves specific academic qualifications and teaching experiences that need to be fulfilled. Particularly in schools focused on STEM education, an academic background or a degree in subjects related to Artificial Intelligence, Pattern Recognition, Horticulture, or Statistics would be a beneficial starting point. Additionally, having hands-on experience with techniques such as Machine Learning and Computer vision, could provide a competitive edge in both private and public academic spaces. If you're curious about these opportunities and want to understand the specific requirements, you can read more about the private school teacher requirements in Kansas. This article will provide you with a comprehensive overview of required qualifications and procedure to take the next step in your career. Engaging in educating young minds can also fuel ideas for ground-breaking research, simultaneously creating a significant impact on society's academic structure and future scientific explorations. Whether you choose to research, teach, or both, your contribution to the field of 'Information Processing in Agriculture' can be substantial.
José G.M. Esgario;Pedro B.C. de Castro;Lucas M. Tassis;Renato A. Krohling
(2021)Mohammad Hosseinpour-Zarnaq;Mahmoud Omid;Ebrahim Biabani-Aghdam
(2021)Ramiro T. Gonzalez del Cerro;M.S.P Subathra;Nallapaneni Manoj Kumar;Sebastian Verrastro
(2021)Sanaz Rasti;Chris J. Bleakley;N.M. Holden;Rebecca Whetton
(2021)Yang Yu;Sergio A. Velastin;Fei Yin
(2020)For those interested in pursuing Computer Science in the USA, online degree programs offer flexible and accessible options. Many students benefit from self paced degree programs, which allow learners to progress at their own speed, balancing studies with personal and professional commitments.
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