RIGGS: Journal of Artificial Intelligence and Digital Business
metadata only
Classification Of Tomato Maturity Levels Based on RGB And HSV Colors Using KNN Algorithm
Tomatoes (Lycopersiconeculentum Mill) are vegetables that are widely produced in tropical and subtropic areas. According to (Harllee) tomatoes are grouped into 6 levels of maturity, namely green, breakers, turning, pink, light red, and red. One way that can be used to classify the level of maturity
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Genetic transformation of rice using rhizobium and agrobacterium and functional analysis of OsHox6 Gene#257843
- Genomic variation in tomato, from wild ancestors to contemporary breeding accessions#1146262
- Genome-Wide Association in Tomato Reveals 44 Candidate Loci for Fruit Metabolic Traits#1146250
- PIGMENTnet: Chlorophyll-b prediction of Lactuca sativa leaf under hybrid genetic algorithm and recurrent neural network#471134
- Multi-variant GWAS and genomic prediction dissect the genetic architecture underlying tomato fruit weight.#666116
Record · ID 1017722
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive