AI Roles in 4R Crop Pest Management—A Review
Bibliographic record
Abstract
Insect pests are a major threat to agricultural production, causing significant crop yield reductions annually. Integrated pest management (IPM) is well-studied, but its precise application in farmlands is still challenging due to variable weather, diverse insect behaviors, crop variability, and soil heterogeneity. Recent advancements in Artificial Intelligence (AI) have shown the potential to revolutionize pest management by implementing 4R pest stewardship: right pest identification, right method selection, right control timing, and right action taken. This review explores the roles of AI technologies within the 4R framework, highlighting AI models for accurate pest identification, computer vision systems for real-time monitoring, predictive analytics for optimizing control timing, and tools for selecting and applying pest control measures. Innovations in remote sensing, UAV surveillance, and IoT-enabled smart traps further strengthen pest monitoring and intervention strategies. By integrating AI into 4R pest management, this study underscores the potential of precision agriculture to develop sustainable, adaptive, and highly efficient pest control systems. Despite these advancements, challenges persist in data availability, model generalization, and economic feasibility for widespread adoption. The lack of interpretability in AI models also makes some agronomists hesitant to adopt these technologies. Future research should focus on scalable AI solutions, interdisciplinary collaborations, and real-world validation to enhance AI-driven pest management in field crops.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".