Yield Estimation using Deep Learning for Precision Agriculture
Bibliographic record
Abstract
We perform fruit counting on video footage by following a two-stage pipeline that consists of detecting the fruits, then tracking them frame-by-frame. Detection is done through the use of You Only Look Once model (YOLO). Bounding boxes are extracted from detection and Non Max Suppression (NMS) is performed to get final detections. The boxes are then input into the tracking pipeline. For tracking, we apply a custom-developed DeepSORT algorithm to work with fruits. Using the box coordinates, every detected object is cropped out of the original image, and a separate feature extraction using a convolutional neural network (CNN) called ResNet is performed on that image crop to get the feature map. New detections are associated with old detections by comparing their features as a distance metric, where two objects with minimal distance are associated together. Input objects with no association are treated as new objects to be tracked. By keeping track of the fruits throughout the video frames, we ensure that we’re counting them appropriately when they are first detected. We demonstrate the approach on videos from an apple orchard to test the performance of the proposed pipeline in natural light. Experimental results show high accuracy of fruit counting on real-time video feeds. The new approach can be efficiently applied on any type of fruit and vegetables with no changes in the algorithms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".