Classification of Millet Cultivars Using Image Processing and Machine Learning Techniques

India is one of the leading producers of various millet cultivars. Indian millets are a class of highly nutritious, drought-resistant crops that are mostly cultivated in the country's semi-arid and drought-prone regions. The most popularly cultivated millet varieties in India are Sorghum (Jowar), Pearl millet (Bajra), Barnyard Millet (Bhagar), Foxtail millet (Kangi), Amaranth (Rajgira), Red sorghum (Chikani), Finger millet (Nachani), and White sorghum (Dadar). In addition to being a wholesome dietary source, millets offer several health advantages. Since they don't contain gluten, they're an excellent alternative for anyone with gluten sensitivity or coeliac disease. Additionally low in glycemic index; millets are a great meal choice for diabetics. Each cultivar has a different nutritional composition and uses. Also, the majority of the time the farmers adapted the millet seeds obtained from their crop hence to preserve the purity of seeds and quality of produce it is very important to classify different millet cultivars. However, the classification of millet cultivars is a complex and time-consuming process. Automation in millet cultivar classification becomes greatly beneficial with improved accuracy and efficiency. This study presents the effective classification of eight different millet cultivars using different machine-learning classifiers along with image processing techniques, based on the derived color features.

Authors

Institutions

Publication Details

Published
2025-02-07
DOI
https://doi.org/10.1109/icccit62592.2025.10927954
Primary Topic
Spectroscopy and Chemometric Analyses
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Classification of Millet Cultivars Using Image Processing and Machine Learning Techniques

Pushpalata Aher, Dewendra Bharambe
Spectroscopy and Chemometric Analyses
article

Classification of Millet Cultivars Using Image Processing and Machine Learning Techniques

Pushpalata Aher, Dewendra Bharambe
article en

Abstract

India is one of the leading producers of various millet cultivars. Indian millets are a class of highly nutritious, drought-resistant crops that are mostly cultivated in the country's semi-arid and drought-prone regions. The most popularly cultivated millet varieties in India are Sorghum (Jowar), Pearl millet (Bajra), Barnyard Millet (Bhagar), Foxtail millet (Kangi), Amaranth (Rajgira), Red sorghum (Chikani), Finger millet (Nachani), and White sorghum (Dadar). In addition to being a wholesome dietary source, millets offer several health advantages. Since they don't contain gluten, they're an excellent alternative for anyone with gluten sensitivity or coeliac disease. Additionally low in glycemic index; millets are a great meal choice for diabetics. Each cultivar has a different nutritional composition and uses. Also, the majority of the time the farmers adapted the millet seeds obtained from their crop hence to preserve the purity of seeds and quality of produce it is very important to classify different millet cultivars. However, the classification of millet cultivars is a complex and time-consuming process. Automation in millet cultivar classification becomes greatly beneficial with improved accuracy and efficiency. This study presents the effective classification of eight different millet cultivars using different machine-learning classifiers along with image processing techniques, based on the derived color features.

Sandip Foundation (IN)
Zero hunger
Openalex Percentile: Top 94%
Spectroscopy and Chemometric Analyses
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Classification of Millet Cultivars Using Image Processing and Machine Learning Techniques — Pushpalata Aher, Dewendra Bharambe · (2025) | TGRS Research Map | TGRS