Survey on IoT based Farm Freshness Mobile Application
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
Food safety and hygiene are major concerns when it comes to preventing food waste. In India's major markets the fruits and vegetables are getting wasted due to temperature and humidity fluctuations. The quality of the food (fruits and vegetables) should be examined, and they should be guarded from rotting and decaying due to atmospheric aspects like temperatures, moisture, and shade, which help farmers conserve the food (fruits and vegetables) while in transit. An Android application based on the Internet of Things will be built in this article to monitor environmental elements such as heat, moisture, alcohol percentage, and light exposed. The Arduino UNO, a well-known popular tooling board, is at the heart of this device. Different sensors are interfaced to the Microcontroller board such as the DHT11, MQ3, LDR, <tex>$16\times 2$</tex> LCD, ESP8266 Wi-Fi, and Image sensor, which are all connected to an Android app where the user is notified with real-time data that defines the food quality. ML will be used to analyze the image captured by the app and predict the condition of the food. The app includes a chatbot that provides information on food quality. The app will suggest nearby organic stores based on the user's location. The app will also support Multilanguage's like English, Telugu, Tamil, Kannada and Hindi which will help the farmers, stakeholders to understand in their local language.
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 | 0.000 | 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) | 0.001 | 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