Assessment of sensor driven automatic smart soil and paddy seed metering mechanisms using artificial intelligence for paddy nurseries

A sensor-based automatic soil and paddy seed metering mechanism is essential to ensure uniform distribution of soil and seeds in trays with precise quantities, optimizing seedling growth and efficiency. This is important for fostering optimal biometric seedling growth and ensuring sustainable paddy production. Thus, in present study, a sensor based metering mechanism for soil filling and paddy seeding was developed and evaluated. The operating and metering parameters included the speed of the chain conveyor (0.123 to 0.27 m/s), the speed of the base soil feed unit (539 to 718 rpm), the speed of the paddy seed feed unit (43 to 53 rpm), and the speed of the topsoil feed unit (108 to 180 rpm). Optimizing these parameters in tray nurseries to achieve a base soil depth of 17.5 mm, a paddy seed quantity of 150 g per tray, and a topsoil depth of 4 mm is necessary to ensure uniform distribution. The optimized values of the speed of the chain conveyor, speed of the base soil feed unit, speed of the paddy seed feed unit, and speed of the topsoil feed unit were obtained to be 0.21 m/s, 651.45 rpm, 52.99 rpm, and 150.40 rpm, respectively. At the optimized condition, the values of depth of base soil, the quantity of paddy seed, depth of topsoil, coefficient of base soil distribution uniformity, coefficient of paddy seed distribution uniformity, and coefficient of topsoil distribution uniformity were predicted as 15.97 mm, 143.73 g/tray, 3.41 mm, 97.53 %, 96.19 %, and 95.26 %, respectively by artificial neural network (ANN). The ANN-MOGA (multi-objective genetic algorithms) predicted higher experimental response values compared to the response surface methodology. A paddy seed germination rate of 94.0 % was achieved using the optimized condition predicted by ANN. Hence, the optimized conditions improved soil and seed distribution precision, ensuring uniform seedling emergence and sustainable paddy production.

Authors

Institutions

Publication Details

Journal
Smart Agricultural Technology
Published
2025-02-07
DOI
https://doi.org/10.1016/j.atech.2025.100831
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Assessment of sensor driven automatic smart soil and paddy seed metering mechanisms using artificial intelligence for paddy nurseries

Prakhar Patidar, Lokesh Kumawat, Naseeb Singh, Rajendra Machavaram et al.
Smart Agricultural Technology
Smart Agriculture and AI
article

Assessment of sensor driven automatic smart soil and paddy seed metering mechanisms using artificial intelligence for paddy nurseries

Prakhar Patidar, Lokesh Kumawat, Naseeb Singh, Rajendra Machavaram, Vinod Choudhary, Gajendra Singh
article en

Abstract

A sensor-based automatic soil and paddy seed metering mechanism is essential to ensure uniform distribution of soil and seeds in trays with precise quantities, optimizing seedling growth and efficiency. This is important for fostering optimal biometric seedling growth and ensuring sustainable paddy production. Thus, in present study, a sensor based metering mechanism for soil filling and paddy seeding was developed and evaluated. The operating and metering parameters included the speed of the chain conveyor (0.123 to 0.27 m/s), the speed of the base soil feed unit (539 to 718 rpm), the speed of the paddy seed feed unit (43 to 53 rpm), and the speed of the topsoil feed unit (108 to 180 rpm). Optimizing these parameters in tray nurseries to achieve a base soil depth of 17.5 mm, a paddy seed quantity of 150 g per tray, and a topsoil depth of 4 mm is necessary to ensure uniform distribution. The optimized values of the speed of the chain conveyor, speed of the base soil feed unit, speed of the paddy seed feed unit, and speed of the topsoil feed unit were obtained to be 0.21 m/s, 651.45 rpm, 52.99 rpm, and 150.40 rpm, respectively. At the optimized condition, the values of depth of base soil, the quantity of paddy seed, depth of topsoil, coefficient of base soil distribution uniformity, coefficient of paddy seed distribution uniformity, and coefficient of topsoil distribution uniformity were predicted as 15.97 mm, 143.73 g/tray, 3.41 mm, 97.53 %, 96.19 %, and 95.26 %, respectively by artificial neural network (ANN). The ANN-MOGA (multi-objective genetic algorithms) predicted higher experimental response values compared to the response surface methodology. A paddy seed germination rate of 94.0 % was achieved using the optimized condition predicted by ANN. Hence, the optimized conditions improved soil and seed distribution precision, ensuring uniform seedling emergence and sustainable paddy production.

Smart Agricultural TechnologyVol. 10
Indian Institute of Technology Kharagpur (IN), University of South Australia (AU), NIMS University (IN), ICAR Research Complex for NEH Region (IN), University of Science and Technology Bannu (PK), The University of Adelaide (AU)
Zero hunger
Openalex Percentile: Top 98%
Smart Agriculture and AI
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.