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Derek Zhou
Computer Science, University of California, Berkeley, Berkeley, CA, USA
Computer Science & IT

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Domain-Shift-Aware Climate-Smart Crop Portfolio Advisory for Nepal with Self-Supervised Tabular Learning and Conformal Shortlists Lumeng Han; Derek Zhou
Journal of Technology Informatics and Engineering Vol. 4 No. 2 (2025): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i2.592

Abstract

Crop advisory models can perform well on balanced benchmarks while failing under unfamiliar environmental or household conditions. This study evaluates two separate research tracks: crop-label classification using 2,200 agronomic benchmark records and observed crop-portfolio prediction using 43,753 village-census households and 981 TAFSSA households in Nepal. Eight crop classifiers were compared within a training pool, with independent temperature-calibration, conformal-calibration, and test subsets. Household models were evaluated under random and geographic holdouts, including 30 paired community-level ablations. An external explanation-consistency experiment used 900 held-out, human-annotated RAGTruth responses. Random forest attained the highest cross-validated macro-F1 (0.992), while LightGBM had the lowest conventional-model log loss (0.059). Temperature scaling produced negligible change in held-out log loss. At 90% nominal coverage, adaptive prediction sets included the benchmark label in 438 of 440 cases, with a mean of 1.09 crops. Environmental-cluster holdouts reduced macro-F1 to 0.000–0.656, partly because some test crops were absent from training. Village-census logistic-regression macro average precision declined from 0.680 under random partitioning to 0.578 under district holdout for the same test households. TAFSSA cross-district values were 0.332 and 0.445. A fixed-split contextual gain did not persist across community holdouts: full context reduced macro average precision by 0.037 relative to farm-only predictors. On RAGTruth, numeric checks detected 132 of 579 erroneous responses; a source-linked lexical screen detected 432 but also rejected 102 of 321 valid responses. The findings support cautious interpretation of benchmark shortlists and household associations. The components remain a research prototype without end-to-end or field validation of agricultural decisions in Nepal.
Lightweight Image–Text Triage for Rice Leaf Diseases with Clarify-or-Abstain Decisions and Nepal-Context Advisory Support Lumeng Han; Derek Zhou
Journal of Technology Informatics and Engineering Vol. 4 No. 1 (2025): APRIL | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i1.593

Abstract

Image-based rice disease screening requires assessment of unfamiliar conditions, degraded photographs, and the evidence used for advice. This study evaluates a modular image–text workflow with separately trained visual and retrieval components. A rotation- and reflection-aware audit of 5,932 rice leaf images identified 343 similarity groups for leakage-reduced training, validation, and testing. Seven lightweight classifiers and a 24,724-parameter CNN were evaluated alongside open-set rejection, image-quality triage, simulated recapture, and external testing on 10,333 exact-unique Paddy Doctor images. Color-texture logistic regression achieved 78.41% grouped-test accuracy and the CNN achieved 81.03%; their accuracies on 4,232 external images from the four known classes fell to 13.33% and 12.55%, respectively. External unknown-detection AUROCs were 0.512 for Mahalanobis scoring and 0.486 for CNN nonconfidence. Across 7,745 scoped CROP records, reranked retrieval recovered the paired answer within five candidates for 934/1,168 test queries (79.97%). Evidence-assisted benchmark accuracy was 65.21% on 365 questions and 41.34% on 179 without an exact question match in the retrieval collection. Four advisory gates enforced disease, location, treatment, and numeric-rate rules, with a trade-off between answer delivery and reference-answer retention. Nepal-context retrieval remained preliminary: the reranker succeeded on 7/8 queries (87.50%; Wilson 95% CI, 52.91–97.76%). Classification, triage, and advisory retrieval provide separate levels of evidence. The findings support further investigation of the workflow, while the external failures preclude claims of reliable field deployment.
Co-Authors Lumeng Han