Abstract
Accurate and early detection of nutrient deficiencies in crops is paramount for optimizing agricultural productivity and sustainability. Traditional methods often rely on visual inspection or laboratory analysis, which can be time-consuming, labor-intensive, and may only identify stress after significant damage has occurred. This research investigates the synergistic integration of hyperspectral imaging (HSI) and Long Short-Term Memory (LSTM) neural networks for the non-invasive, sub-visual detection of nutrient stress in agricultural settings. Hyperspectral data, capturing spectral information across numerous narrow bands, offers rich spectral signatures indicative of plant physiological status. LSTM networks, a type of recurrent neural network, are adept at learning temporal dependencies and patterns in sequential data, making them suitable for analyzing spectral data where subtle spectral shifts can signify early stress. We developed a novel framework that processes hyperspectral data cubes, extracting relevant spectral features and feeding them into an LSTM model trained to identify subtle spectral anomalies associated with specific nutrient deficiencies (e.g., nitrogen, phosphorus, potassium) prior to the manifestation of visible symptoms. The methodology involved acquiring hyperspectral images of hydroponically grown lettuce under controlled nutrient deprivation conditions. Spectral data was preprocessed to reduce noise and enhance relevant features. The LSTM model was trained on these spectral sequences to classify plants into 'stressed' and 'non-stressed' categories, focusing on identifying stress at its earliest detectable stages. Preliminary results demonstrate that the proposed HSI-LSTM approach achieves high accuracy in detecting sub-visual nutrient stress, outperforming traditional spectral analysis techniques and conventional machine learning models. This work highlights the potential of combining advanced spectral sensing with sophisticated deep learning architectures for proactive crop management, paving the way for precision agriculture systems that minimize resource waste and maximize yield.