Deep Residual CNN with Contrast Limited Adaptive Histogram Equalization for Weed Detection in Soybean Crops
Bibliographic record
Abstract
Weeding is the fundamental task in agriculture to increase yields crop. Accurate weed recognition is major prerequisite in precision agriculture. Precision weeding significant reduces the usage of herbicides in farming. Deep learning has been a major endeavor for enhancing the learning performance, particularly for classification. This paper proposes a Deep Residual Convolutional neural network (DRCNN) with Contrast Limited Adaptive Histogram Equalization (CLAHE) for weed and crop classification helpful for accurate individual targeting of weeds. In this method, initially data augmentation is performed to avoid overfitting on training data, A deeper residual network architecture is defined through residual connections in CNN architecture this architecture improves gradient flow through the network and for training the deeper network. The experiments are carried out on the publicly available dataset with four groups of images viz., soil, grass, soybean and broadleaf. Different state-of-the-art pretrained networks like AlexNet, and VGG-16 Net are also investigated and the results are compared. The proposed method yielded an accuracy of 97.3% which is superior to other methods.
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".