In the rapidly evolving field of agricultural technology, the need for
advanced, reliable methods for assessing dry fruit quality has become paramount. This
paper presents an empirical review of the rapidly increasing application of Machine
Learning (ML) and Deep Learning (DL) techniques in hyperspectral imaging for the
qualitative analysis of dry fruits and their subtypes. Traditional methodologies, while
having laid a robust foundation, often grapple with constraints such as limited
precision, suboptimal accuracy, and scalability challenges. Furthermore, these
conventional approaches typically exhibit significant delays in processing and struggle
with complexity in handling diverse dry fruit categories. Addressing these limitations,
this review comprehensively evaluates ML and DL methodologies tailored explicitly
for hyperspectral imaging applications in dry fruit quality analysis. The proposed
review process stands out by precisely comparing these methods across a spectrum of
critical evaluation metrics, such as precision, accuracy, recall, processing delay,
complexity, and scalability. This approach not only bridges the gaps identified in
existing literature but also lays the groundwork for a more nuanced understanding of
the capabilities and limits of these advanced technologies in practical scenarios. This,
in turn, has improved quality control measures and enhanced overall productivity
levels. In conclusion, this work extends the existing body of knowledge. It sets a new
benchmark for applying deep learning techniques in agricultural hyperspectral imaging,
thus marking a significant stride forward in pursuing agricultural innovation and
excellence.
Keywords: Deep learning, Dry fruit quality, Hyperspectral imaging, Machine learning, Scenarios.
