Enhancing Precision Agriculture Based on Explainable AI for Automated Nutrient Deficiency Diagnosis in Rice Using Attention SqueezeNet
Bibliographic record
Abstract
Rice (Oryza sativa) is a linchpin of global food security, feeding most of the world's population, especially in Asia and Africa.Nitrogen (N), Phosphorus (P), and Potassium (K) must be held in precise equilibrium for rice to grow right and produce well.A lack of these nutrients can put plant health at risk, causing poor yields that result in huge economic losses to farmers.This means it is important to have an accurate diagnosis done on time so that corrective measures can be taken to ensure the sustainability of rice cultivation.Conventional techniques used to detect nutrient deficiencies in rice plants like manual inspection and biochemical testing are frequently inadequate.Manual inspection may be effective, but it is laborious, subjective, and unworkable when working on a large-scale farm.On the other hand, the accuracy of biochemical tests does not compensate for their time-consuming nature, their cost as well as the need for specialized equipment and expertise; this makes them inaccessible to many smallholder farmers.This research proposes Attention SqueezeNet-a streamlined deep learning model aimed at overcoming such limitations.Attention SqueezeNet leverages the Kaggle "Nutrient Deficiency Symptoms in Rice" dataset by including an attention mechanism that focuses on significant visual features associated with N P K deficiency on rice leaves.Compared with traditional methods, therefore, these focused areas enable more accurate diagnostic results from the model as opposed to conventional methods which diagnose diseases broadly rather than focusing on specific symptoms.In this context, we propose: i) developing a robust Attention SqueezeNet model specifically aimed at categorizing nutrient deficiencies in rice plants; ii) making the model more robust against unseen data variations through pre-processing and augmenting existing dataset; iii) comparing the performance of Attention SqueezeNet with existing deep learning models in terms of accuracy and efficiency per computational unit utilized (CU).The results show that Attention SqueezeNet has better classification accuracy compared to state-of-the-art models and is more computationally efficient than them.This has resulted in the transformation of agriculture through automation and objectivity in diagnosis of nutrient deficiency which ensures sustainable crop management practices towards global food security.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".