Hydroponic systems offer controlled environments for crop growth, demanding precise and continuous monitoring of nutrient levels to optimize plant health and yield. In-situ monitoring of crops and nutrient supply plays a pivotal role in comprehensively understanding their mineral constituents, facilitating early correction of nutritional deficiencies. In addition to detecting nutritional deficiencies, this process offers farmers guidance regarding precise crop cultivation practices. However, current monitoring techniques are often labor-intensive, laboratory-based, and require tedious sample preparation. In this context, we propose an optically modified direct spectrograph-coupled laser induced breakdown spectroscopy (LIBS) system capable of detecting and quantifying key nutrients including potassium, calcium, and magnesium. In this work, in-situ nutrient monitoring of the nutrient supply and lettuce crop was performed using singleshot LIBS. Analysis of the results elucidates that highly sensitive and reproducible LIBS spectra can be directly obtained without any sample preparation. Additionally, we assess the feasibility and accuracy of LIBS in capturing dynamic changes in nutrient concentrations, providing valuable insights into nutrient uptake kinetics and potential imbalances. This research showcases LIBS as a valuable tool for real-time monitoring in hydroponic systems, highlighting its potential for remote analysis in agriculture. It facilitates informed decision-making and enhances the overall efficiency of nutrient management practices in agricultural fields.
Hydroponic farming is considered as a more sustainable solution in comparison to conventional farming. Most of the hydroponic farms rely on manual visual inspection for crop monitoring, which can be subjective, time-consuming, and tedious, especially in the case of large area farms. Hyperspectral Imaging (HSI) is a promising technique for automated sensing and monitoring. Though several automated systems based on HSI have been developed recently for crop monitoring, these tend to be computationally complex and demand significant processing power and time, especially when handling extensive data from large farms. In this context, we explore an approach using spectral ratios for crop growth monitoring and the detection of early-stage nutrient stress. The early detection of the nutrient stress can enable effective crop, resource, and time management in large hydroponic farms. A sensitive nutrient deficiency index, named Normalized Nutrient Deficiency Index (NNDI), has been formulated for the early-stage detection of nutrient deficiencies. Evaluating these indices is computationally simple and quick. A methodology for crop growth monitoring and nutrient deficiency stress using these indices is demonstrated on Lactuca sativa L. crops. It is envisaged that the proposed quick, non-destructive imaging technique can enable future automation possibilities and serve as an invaluable tool in indoor hydroponic farms.
Automated crop monitoring techniques help in better management of growing conditions in order to improve quality and yield, and to reduce the impact on the environment. Various stages of a crop in its life cycle is noticeable with a change in its leaf color. The yellowing of leaf is considered as an important quality defect in green leafy vegetables. Yellowing is initiated at the end of maturity stage and continues in the senescence stage. Most of the methods used for monitoring leaf quality requires the detachment of the leaf from the plant, which is destructive in nature. Among the several nondestructive techniques available, hyperspectral imaging (HSI) modality offers the possibility to address this problem by monitoring the reflection spectrum of the leaf in situ. When the freshness of the leaf reduces, the chlorophyll content in the leaf decreases. This results in an increase in its reflectance in the visible region as the absorption of light by chlorophyll reduces. Hence, the reflection spectrum can be used as a measure for the freshness of the leaf. However, monitoring large areas usually require translation of the whole imaging system or removal of the leaf from the plant. In this context, we propose to use a flexible probe-based HSI system to mitigate these issues. We demonstrate the adoption of a probe based HSI modality to enable in situ live plant monitoring. The classification of the leaves from the HSI data is performed using principal component analysis (PCA) technique.
Corrosion in metal structures is one of the prevailing problems impacting automobile, cargo, and construction industries. The detection of corrosion at the right time and determination of the root cause are crucial in its prevention and control. In this context, we propose hyperspectral imaging as a potential imaging modality for monitoring corrosion. This technique is very relevant for high-speed, non-destructive inspection. The proposed hyperspectral imager can efficiently monitor corrosion with high sensitivity and it enables corrosion detection even at human inaccessible areas with the aid of a custom fabricated fiber optic probe. In contrast to traditional methods, the hyperspectral imaging technique can capture reflectance at several wavelengths from several spatial points of the sample and hence provides a means of rigorous analysis of the sample reflectance. Using a two dimensional to one dimensional fiber bundle reformatter, hyperspectral images of metal samples were recorded. Induced corrosion in the sample was monitored by the hyperspectral imager and the data recorded were processed to form the three-dimensional spectral datacube. Obtained results show that hyperspectral reflectance imaging is a powerful tool for corrosion monitoring, non-destructively.
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