Herbify: an ensemble deep learning framework integrating convolutional neural networks and vision transformers for precise herb identification
Farhan Sheth; Ishika Chatter; Manvendra Jasra; Gireesh Kumar; Richa Sharma · 2025 · Plant Methods
WASTE classifies this as Replication Failure · AI classification, approximate
A previously reported effect did not replicate here — verify it holds before you build on it.
Abstract (excerpt)
Herbs have historically been central to medicinal practices, representing one of the earliest forms of therapeutic intervention. While synthetic drugs are often highly effective in treating acute conditions, their use is frequently…
Excerpt shown for reference under fair use — read the full paper at the publisher.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
Effect of Metal–Organic Framework (MOF) Database Selection on the Assessment of Gas Storage and Separation Potentials of MOFs
Negative / Null Result ReportOlive Leaf Extract as a Hypoglycemic Agent in Both Human Diabetic Subjects and in Rats
Negative / Null Result ReportMoMo: discovery of statistically significant post-translational modification motifs
Negative / Null Result ReportReliable Identification of Significant Differences in Differential Hydrogen Exchange-Mass Spectrometry Measurements Using a Hybrid Significance Testing Approach
Negative / Null Result ReportSignificant Association between Sulfate-Reducing Bacteria and Uranium-Reducing Microbial Communities as Revealed by a Combined Massively Parallel Sequencing-Indicator Species Approach
Negative / Null Result ReportQuantitative, Time-Resolved Proteomic Analysis by Combining Bioorthogonal Noncanonical Amino Acid Tagging and Pulsed Stable Isotope Labeling by Amino Acids in Cell Culture
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.1186/s13007-025-01421-5