Design and Implementation of a Fruit Image Recognition System Based on CNN
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
Fruit classification and recognition have significant application value in fields such as agricultural sorting and fresh food retail. To address the poor robustness of traditional manual feature extraction methods, this paper designs and implements a fruit image recognition system based on lightweight convolutional neural networks (CNNs). The system adopts 20 common types of fruit images from the Fruit-360 dataset. After preprocessing, an improved LeNet-5 model and a simplified AlexNet model are constructed, with a comparative analysis of the performance differences between ReLU and Sigmoid activation functions. Experimental results show that the simplified AlexNet combined with the ReLU activation function achieves the optimal performance, with an average test set accuracy of 96.87%. It also features fast training convergence, making it suitable for real-time recognition requirements in low-computing-power scenarios.
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 | 0.001 | 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 | 0.001 | 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 it