A Comprehensive Review on Image-Driven Seed Germination Prediction Using Machine Learning Techniques

¹ Research Scholar, Department of Electronics and Telecommunication, Sipna College of Engineering & Technology, Amravati
² Professor, Department of Electronics & Telecommunication , Sipna College of Engineering & Technology, Amravati.

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Published online: 26 June 2026

Abstract

Predicting seed germination can be beneficial for precision agriculture, leading to increased yield, better seed quality assessment, and saving resources. Imaging-based instrumental techniques are becoming more popular as conventional germination tests are manual, destructive, and time-consuming. In this paper, we offer a critical review on machine learning (ML) and deep learning (DL) for the prediction of seed germination from RGB, hyperspectral (HS), thermal, and fluorescence images. Although CNNs and ResNet-based models have demonstrated very high accuracy in laboratory settings, the performance is highly dependent on dataset size, imaging conditions, and validation strategies. The choice of the system must consider the trade-off between the cost of hardware and accuracy. RGB systems are low-cost and scalable, but of moderate accuracy, whereas hyperspectral imaging offers better biochemical sensitivity at a higher cost in hardware and computation. However, despite promising results from field applications, large-scale access is restricted by the non-standardisation of datasets, the lack of cross-crop and real-field assessments, and practical constraints on energy efficiency, maintenance costs, and environmental variability. These gaps also motivate the creation of standard benchmarks, field (multi-crop) validation support, and energy-efficient edge AI through co-design between algorithms and hardware.