Design, Development and Application of a Drone-Integrated Cutting Mechanism for Agriculture
Bibliographic record
Abstract
In Malaysia, one of the industrial crop resources is coconut, where the coconut-based products have varied usage and are being exported to other countries. Hence, it contributes to increasing Malaysia's profit, but the industrial crops face challenges in providing a large scale of coconuts because of high demands from manufacturers. Besides, the manual coconut plucking process is a labour-intensive and time-consuming task, demands skilled climbers, and is exposed to safety risks. Thus, this paper presents the design a cutting mechanism and drone for coconut harvesting and to test the performance of real-time camera feed visualization and the cutting process for the drone. The chosen type of drone for this project is a quadcopter drone, and the drone's body is designed on Autodesk Fusion 360 and printed using a 3D printer with PLA filament. The Arduino UNO board acts as a central controller that connects other components such as the MPU6050, receiver, and ESCs. The ESP32 CAM acts as an eye to display the location of coconuts on the tree. The DC motor and steel saw blade is used as cutting mechanism. The results showed that the ESP32 CAM successfully visualized real-time video streaming with minimal lag, while the cutting mechanism able to cut through the rolled paper and branch within a practical timeframe.
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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 | 0.001 |
| 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".