Application of Artificial Intelligence in the Identification of Banana Bunch Top Virus (BBTV) in Mozambique
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Agricultural production faces many challenges, such as disease and pest infestation, which can lead to severe crop loss and environmental impacts due to the excessive use of chemicals. Artificial intelligence has become a key technique to solve different agricultural-related challenges. The main objective of this study was to train and validate artificial intelligence algorithms for the detection of Banana Bunchy Top Virus (BBTV) in banana crops. Approximately 2,500 images of healthy and BBTV-infected leaves were collected, stratified according to the stage of plant development, and used to calibrate and validate an artificial intelligence algorithm for the detection of BBTV. Pre-trained models such as VGG 16, ResNet50, and InceptionV3 were tested. The ResNet50 model achieved a training accuracy of 99.56% and validation precision, recall, and F1 score of 96.53%, 94.94%, and 95.73%, respectively, outperforming the other models in detecting BBTV-infected plants.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it