The mechanisation of sesame (Sesamum indicum L.) planting remains a significant challenge due to the crop’s small, fragile seeds and non-uniform shape, which hinder the effectiveness of standard seeding systems. Crop emergence and production are adversely affected by poor singulation and uneven seed distribution, which are frequently caused by conventional and general-purpose planting equipment. For sesame, consistency in seed distribution and emergence is very important, necessitating careful consideration of agronomic conditions as well as seed properties. This study was conducted as a systematic review following the PRISMA 2020 guidelines to critically evaluate the existing literature on advanced planting methods that prioritise precision, efficiency, and seed protection. A comprehensive search was conducted across Scopus, Web of Science, and Google Scholar for peer-reviewed studies published from 2000 to 2025. Studies focused on the agronomic parameters of sesame, planting technologies, and/or simulation integration, such as Discrete Element Modelling (DEM), were included in this review, and studies unrelated to sesame planting or not available in full text were excluded. The findings from these studies were analysed to examine the interaction between seed metering mechanisms and seed morphology, specifically seed thickness and shape variability. Agronomic parameters such as optimal seed spacing, sowing depth, and population density are analysed to guide the development of effective planting systems. The review also evaluates limitations in existing mechanised approaches while highlighting innovations in precision planting technology. These include optimised seed plate designs, vacuum-assisted metering systems, and simulation tools such as DEM for performance prediction and system refinement. A total of 22 studies were included and analysed using systematic narrative synthesis, grouped into agronomical, technological, and simulation-based themes. The studies were screened for methodological clarity, and reference list screening was performed to reduce reporting bias. In conclusion, the findings of this research support the development of crop-specific planting strategies tailored to meet the unique requirements of sesame production.

Accurate representation of particle shape is essential for reliable discrete element method (DEM) simulations of small, irregularly shaped seeds such as sesame, yet the effect of multi-sphere resolution on predictive accuracy remains poorly quantified. This study characterised the physical properties of sesame seeds and evaluated five multi-sphere particle models (9, 11, 15, 19 and 24 spheres) constructed in Ansys Rocky using the filling-sphere method. The contact parameters were transferred unchanged from a previous sesame study, and the measured seed–seed and seed–glass static friction and restitution coefficients agreed with the adopted values to within 4%. Each model was validated against experimental piling tests (angle of repose and bulk density; five repetitions), and the 19- and 24-sphere models were further validated by a hopper discharge test with 150 g of seed (three repetitions). The seeds had a mean length, width, and thickness of 3.12, 1.88, and 0.90 mm, respectively, and an experimental angle of repose of 20.74°. As the number of spheres increased from 9 to 24, the error in the simulated angle of repose decreased from 24.06% to 1.16%, and the error in bulk density from 5.67% to 0.12%. The 24-sphere model was the only model whose angle of repose did not differ significantly from the experimental value (p = 0.057), and it predicted the discharge time to within 1.70%, compared with 5.66% for the 19-sphere model, while requiring 2.9 times the run time of the 9-sphere model. Because its clump volume was within 0.8% of the measured seed volume, it achieved this agreement with four fewer spheres per particle than the 28-sphere model previously reported for sesame. The 24-sphere model is recommended for this seed lot under the tested conditions and provides a validated particle model for DEM simulation of sesame seed handling and planting equipment.