Computers and Electronics in Agriculture

Top Research Topics at Computers and Electronics in Agriculture?

The main points discussed in the journal deals with Artificial intelligence, Pattern recognition, Remote sensing, Computer vision and Statistics. Computers and Electronics in Agriculture connects research in Artificial intelligence with the related topic of Machine learning. In the Pattern recognition research discussed, Convolutional neural network and Support vector machine are all tackled.

Machine vision is a major topic of Computer vision research.

• Artificial intelligence (24.25%)
• Pattern recognition (10.08%)
• Remote sensing (8.57%)

What are the most cited papers published in the journal?

• Deep learning in agriculture: A survey (939 citations)
• Review: Wireless sensors in agriculture and food industry-Recent development and future perspective (879 citations)
• Review: A review of advanced techniques for detecting plant diseases (671 citations)

Research areas of the most cited articles at Computers and Electronics in Agriculture:

The published articles focus on Artificial intelligence, Computer vision, Remote sensing, Precision agriculture and Image processing. The published articles hold forums on Artificial intelligence that merge themes from other disciplines such as Machine learning and Pattern recognition. Research in Precision agriculture tackled in falls within the umbrella of Agriculture.

What topics the last edition of the journal is best known for?

• Artificial intelligence
• Ecology
• Statistics

The previous edition focused in particular on these issues:

Computers and Electronics in Agriculture mainly deals with areas of study such as Artificial intelligence, Pattern recognition, Deep learning, Convolutional neural network and Segmentation. The journal explores topics in Artificial intelligence which can be helpful for research in disciplines like Machine learning, Computer vision and Identification (information). It links adjacent topics like Computer vision with Robot.

The research on Pattern recognition featured in Computers and Electronics in Agriculture combines topics in other fields like Convolution, F1 score, Feature (machine learning) and Image (mathematics). In addition to Deep learning research, the journal aims to explore topics under Object detection and Machine vision. Studies on Segmentation discussed in it link to the field of Pixel.

The most cited articles from the last journal are:

• RS-DCNN: A novel distributed convolutional-neural-networks based-approach for big remote-sensing image classification (26 citations)
• A survey on the 5G network and its impact on agriculture: Challenges and opportunities (20 citations)
• Meta-learning baselines and database for few-shot classification in agriculture (16 citations)

Papers citation over time

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.

Top authors and change over time

The top authors publishing in Computers and Electronics in Agriculture (based on the number of publications) are:

• Qin Zhang (32 papers) absent at the last edition,
• Daniel Berckmans (31 papers) absent at the last edition,
• Guoqiang Zhang (26 papers) published 3 papers at the last edition, 1 more than at the previous edition,
• Daoliang Li (26 papers) published 3 papers at the last edition, 3 less than at the previous edition,
• Yong He (25 papers) published 2 papers at the last edition the same number as at the previous edition.

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.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing in Computers and Electronics in Agriculture (based on the number of publications) are:

• China Agricultural University (183 papers) published 45 papers at the last edition, 4 more than at the previous edition,
• Agricultural Research Service (157 papers) published 9 papers at the last edition the same number as at the previous edition,
• University of Florida (105 papers) published 9 papers at the last edition the same number as at the previous edition,
• Katholieke Universiteit Leuven (95 papers) published 6 papers at the last edition, 1 more than at the previous edition,
• Wageningen University and Research Centre (94 papers) published 3 papers at the last edition, 9 less than at the previous edition.

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.

Publication chance based on affiliation

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, 7.16% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 26.68% were posted by at least one author from the top 10 institutions publishing in the journal. Another 7.91% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 15.61% of all publications and 49.80% were from other institutions.

Returning Authors Index

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.

Returning Institution Index

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.

The experience to innovation index

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:

• Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
• Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
• Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
• Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
• Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.