REVIEW
Selective protection of cereals using artificial neural networks
1
Actuaro Ltd, Warsaw, Poland
2
Plant Biology, W. Szafer Institute of Botany Polish Academy of Sciences, Krakow, Poland
3
Department of Organic Agriculture and Environmental Protection, Institute of Plant Protection – NIR, Poznań, Poland
4
4Robot Ltd., Kęty, Poland
5
Method of Pests Forecasting, Institute of Plant Protection – NIR, Poznań, Poland
6
Agricultural Advisory Centre in Brwinów, WSG University Bydgoszcz, Warsaw, Poland
These authors had equal contribution to this work
A - Research concept and design; B - Collection and/or assembly of data; C - Data analysis and interpretation; D - Writing the article; E - Critical revision of the article; F - Final approval of article
Submission date: 2024-09-17
Acceptance date: 2024-11-14
Online publication date: 2025-06-24
Corresponding author
Magdalena Szechyńska-Hebda
Plant Biology, W. Szafer Institute of Botany Polish Academy of Sciences, Krakow, Poland
Plant Biology, W. Szafer Institute of Botany Polish Academy of Sciences, Krakow, Poland
Journal of Plant Protection Research 2025;65(2):164-173
HIGHLIGHTS
- Spectral imaging technologies are crucial for early detection of plant diseases
- Hyperspectral imaging reveals changes in plants before visible symptoms appear
- Integration of hyperspectral imaging with AI enhances precision in plant protection
- UAVs offer complementary advantages for field-based selective plant protection
- Future research should address UAV-based precision spraying and monitoring
KEYWORDS
artificial intelligence (AI)early disease detectionprecision agricultureremote sensingspectral imagingunmanned aerial vehicles (UAVs)
TOPICS
ABSTRACT
Global agricultural losses due to pests and pathogens are substantial, particularly for wheat,
maize, and potatoes. Addressing these challenges necessitates innovative approaches in
plant protection, particularly through early detection methods. This article outlines research
areas concerning the application of spectral imaging technologies in selective crop
protection processes. Recent technological advancements, driven by the development of
high-resolution optical sensors and data analysis methods (Pena et al. 2013), have enabled
early detection of weeds, plant diseases, and pests in the field. Spectral imaging technologies,
particularly hyperspectral imaging, play a pivotal role in early disease detection by capturing
detailed spectral data across a wide range of wavelengths. This technology enables the detection
of subtle physiological changes in plants long before visible symptoms occur. Hyperspectral
imaging has proven effective in identifying diseases such as Fusarium head blight
in wheat, allowing for timely interventions and potentially reducing yield losses. The integration
of hyperspectral imaging with remote sensing technologies, including unmanned
aerial vehicles and ground-based sensors, as well as artificial intelligence represents a significant
advancement in precision agriculture. This multidisciplinary approach aims to enhance
crop protection while minimizing environmental impacts. The article also explores
the advantages and limitations of these technologies and strategies for reducing the reliance
on chemical plant protection methods in agricultural production. It is underlined, that future
research should focus on optimizing these technologies, addressing cost barriers, and
exploring UAV-based applications for precision spraying and monitoring.
FUNDING
This work was supported by the project “Artificial
Intelligence for the identification of undesirable phenomena
and selective crop protection” (Akronim: AI-
4Crop) funded under the “Infostrateg VI”, agreement
no. INFOSTRTEG 6/0014/2023/A, financed by the National
Centre for Research and Development (NCBR).
RESPONSIBLE EDITOR
Przemysław Kardasz
CONFLICT OF INTEREST
The authors have declared that no conflict of interests exist.
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| eISSN: | 1899-007X |
| ISSN: | 1427-4345 |
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