Near-infrared spectroscopy integrated machine learning techniques for viable seed identification - SINGH, MONIKA; SHARMA, ANU; CHATURVEDI, K K; KUMAR, SANJEEV; MISHRA, DWIJESH CHANDRA; ARORA, ALKA; BHARDWAJ, RAKESH; RAY, MRINMOY; Y S, MAMATHA; GODARA, SAMARTH (The Indian Journal of Agricultural Sciences, 2025)
The evaluation of seed viability is pivotal in agriculture, biodiversity conservation, and ecological research. Traditional methods used for testing the seed viability are often destructive and pose challenges regarding labour intensity and seed wastage. The study was carried out during 2022–23 at ICAR-Indian Agricultural Statistics Research Institute, New Delhi with the aim of collecting the seed genotypes and NIR spectroscopic instrument and computational approaches and appropriate hardware and software resources. A diverse dataset of NIR spectral data from various seed species was used and analysed using three sophisticated ML models, namely Linear Discriminant Analysis (LDA), Random Forest (RF), and Artificial Neural Networks (ANN). The performance of the developed models was evaluated based on accuracy, precision, recall, and F1 score metrics. Furthermore, the experimental results demonstrated that NIR spectroscopy and ML could effectively classify viable seed. The integration of artificial neural networks (ANNs) has demonstrated significant potential in capturing intricate patterns within spectral data, achieving an approximate accuracy of 95%. This highlights their effectiveness in precise classification tasks. Additionally, machine learning (ML)-based approaches have shown promise in conserving valuable seed resources by offering scalable solutions adaptable to large-scale agricultural and conservation applications. To enhance model transparency, Local Interpretable Model-Agnostic Explanations (LIME) has been employed, providing deeper insights into the ANN’s decision-making process by identifying key spectral features that influence classification outcomes. It was observed that ML-based approaches have the potential to enable continuous monitoring, contributing to the conservation of valuable seed resources. Additionally, these methods may offer a scalable solution that can be adapted for large-scale agricultural and conservation applications.