Toward Sustainability: Trade-Off Between Data Quality and Quantity in Crop Pest Recognition
Open accessAbstract
The crop pest recognition based on the convolutional neural networks is meaningful and important for the development of intelligent plant protection. However, the current main implementation method is deep learning, which relies heavily on large amounts of data. As known, current big data-driven deep learning is a non-sustainable learning mode with the high cost of data collection, high cost of high-end hardware, and high consumption of power resources. Thus, toward sustainability, we should seriously consider the trade-off between data quality and quantity. In this study, we proposed an embedding range judgment (ERJ) method in the feature space and carried out many comparative experiments. The results showed that, in some recognition tasks, the selected good data with less quantity can reach the same performance with all training data. Furthermore, the limited good data can beat a lot of bad data, and their contrasts are remarkable. Overall, this study lays a foundation for data information analysis in smart agriculture, inspires the subsequent works in the related areas of pattern recognition, and calls for the community to pay more attention to the essential issue of data quality and quantity.
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The most prominent data quality dimension in the DL papers is amount of data, which is empirically shown to benefit performance, albeit in a saturating manner.
“The most prominent dimension is amount of data123–126,129,146–159,183–189 which is empirically shown to benefit performance, albeit in a saturating manner.”
From The METRIC-framework for assessing data quality for trustworthy AI in medicine: A systematic review (2024) · cites this paper as [149]
The Results section, “High-Quality Data Selection Under Different Models” (page unavailable), reports that selected subsets of about 75%, 60%, and 40% of the data can match full-data performance under different models, supporting diminishing returns from adding data in this crop-pest task. The paper does not establish that amount of data is the most prominent data-quality dimension across DL papers, and its results emphasize that data quality also affects performance. (LLM, conf=0.82)
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- Authors
- Li, Y., & Chao, X.
- Affiliations
- Shihezi University (CN)
- Corresponding author
- Chao, Xuewei
- ORCID
- Venue
- Frontiers in Plant Science
- Publisher
- Frontiers Media SA
- Published
- 2021-12-24
- Volume
- 12
- Pages
- 811241-811241
- ISSN
- 1664-462X (ISSN-L)
- PMC
- PMC8739801
- OpenAlex
- W4200499937
- Cited by
- 65
- References
- 31
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 18 · 2023: 11 · 2022: 24
- FWCI
- 7.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Plant Science (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (32101612)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- feature space selection, smart agriculture, crop protection, convolutional neural networks, training data selection
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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