Crop Lodging Prediction from UAV-Acquired Images of Wheat and Canola\n using a DCNN Augmented with Handcrafted Texture Features
Bibliographic record
Abstract
Lodging, the permanent bending over of food crops, leads to poor plant growth\nand development. Consequently, lodging results in reduced crop quality, lowers\ncrop yield, and makes harvesting difficult. Plant breeders routinely evaluate\nseveral thousand breeding lines, and therefore, automatic lodging detection and\nprediction is of great value aid in selection. In this paper, we propose a deep\nconvolutional neural network (DCNN) architecture for lodging classification\nusing five spectral channel orthomosaic images from canola and wheat breeding\ntrials. Also, using transfer learning, we trained 10 lodging detection models\nusing well-established deep convolutional neural network architectures. Our\nproposed model outperforms the state-of-the-art lodging detection methods in\nthe literature that use only handcrafted features. In comparison to 10 DCNN\nlodging detection models, our proposed model achieves comparable results while\nhaving a substantially lower number of parameters. This makes the proposed\nmodel suitable for applications such as real-time classification using\ninexpensive hardware for high-throughput phenotyping pipelines. The GitHub\nrepository at https://github.com/FarhadMaleki/LodgedNet contains code and\nmodels.\n
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".