Engineering an Advanced Precision Farming Robot Integrating IoT and Machine Learning for Selective Harvesting Crop Health Monitoring and Enhanced Agricultural Productivity through Smart Automation
Bibliographic record
Abstract
Agriculture is an important sector of Indian economy since many years. Indian agriculture sector accounts for 18% of India’s gross domestic product (GDP). Robotics and internet of things offer better solution in precision agriculture. Harvesting of fruits or vegetables like tomatoes and apple require selective harvesting. Conventional farming involves labors individually handpicking the ripened fruits which requires immense manpower to perform selective farming. We propose a robot that assists farmers in various labour-intensive tasks such as selective crop harvesting, qualitative segregation and also concurrently provide information of crop health, soil nutritional status and crop shelf-life detection. The collected information is analysed, processed and sent to the farmer via android application. Later, quality of the particular harvested crop is inspected by checking the weight, color, health to grade them. The graded fruit is then transferred to the assigned container. Spoiled or over ripened fruits/vegetables would be plucked and dropped so that it would not affect the growth of the plant. Proposed robot would have a harvesting arm which reaches the fruit/vegetable to pluck it from the plant or a tree which would later transfer the fruit to respective container. User would be able to assign a specific task to the device according to his requirement with the help of the app. Selective harvesting, segregation and crop health monitoring requires image processing through camera. It also detects diseases using image processing. The simulation results shows the effectivity of the methodology that is being presented in this research paper.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".