Precision farming practices with data-driven analysis and machine learning-based crop and fertiliser recommendation system
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
Agriculture forms a major occupation in countries like India. More than 75% people rely on farming for their daily wages. Food security on a global scale is mostly dependent on agriculture. Hence, achieving good yield in the crops grown by farmers is the major concern. Various environmental factors have a significant impact on the crop yield. One such component that contributes majorly to the crop yield is soil. Due to urbanization and enhanced industrialization, the agricultural soil is getting contaminated, losing fertility, and hindering the crop yield. One exciting new way to maximise crop yields while decreasing input costs is precision farming, which makes use of machine learning (ML) and the IoT. Machine Learning (ML) is employed for agricultural data analysis. The goal of this research is to optimise agricultural practices by presenting an integrated crop and fertiliser recommendation system. The proposed ML based model “Precision Agriculture” aims at predicting the suitable crops that can be grown based on the class which the soil sample belongs to and suggests the fertilizers that can be used to further enhance the fertility of soil. Using proposed model, farmers can make decisions on which crop to grow based on the soil classification and decide upon the nitrogen–phosphorous– potassium (NPK) fertilizers ratio that can be used. Comparison of the SVM algorithm with Naive Bayes, and LSTM has shown that SVM performed with a higher accuracy. Decision support tools that integrate AI and domain knowledge are provided by the study, which is a substantial contribution to precision agriculture.
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) | 0.000 | 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