In-field hyperspectral imaging: An overview on the ground-based applications in agriculture

Abstract

The measurement of vegetation indexes that characterise the plants growth, assessing the fruit ripeness or detecting the presence of defects and diseases, is a key factor to gain high quality of fruit or vegetables. Such evaluation can be carried out using both destructive and non destructive techniques. Among non-destructive techniques, hyperspectral imaging (HSI), combining image analysis and visible/near-infrared spectroscopy, looks particularly useful. Many studies have been published concerning the use of hyperspectral cameras in the agronomic and food field, especially in controlled laboratory conditions. Conversely, few studies described the application of HSI technology directly in field, especially involving ground-based systems. Results suggest that this technique could be particularly useful, even if the role of environmental variables has to be considered (e.g., intensity and incidence of solar radiation, wind or the soil in the background). In this paper, recent in-field HSI applications based on ground systems are reviewed.

Hyperspectral imaging for small-scale analysis of Hordeum vulgare L. leaves under the benzo[a]pyrene effect

“…The spectral parameters (red edge slope and area) responded to the phenanthrene, consistently with the plant parameters (Zhu et al 2014;Lassalle et al 2019), absorption of chlorophyll and carotenoids (Emengini et al 2013a, b;Zhang et al 2017;Lassalle et al 2019). At the present stage, hyperspectral methods make it possible to assess the degree of plant disease and crop loss if it is used for an agricultural area (Bock et al 2010;Khan et al 2018;Benelli et al 2020). The identification of changes in the spectral characteristics of plants in response to stress during soil pollution occurs before the moment of visible disturbances in plant life, and allows early detection of environmental pollution (Das et al 2021;Emengini et al 2013a, b), which is important in the conditions of agricultural crops production.…”

An Effective Image-Based Tomato Leaf Disease Segmentation Method Using MC-UNet

“…Tomato, like other crops, is susceptible to a range of pests and diseases as they grow. Similar to diseases that affect tomato fruits, tomato leaves are also susceptible to disease [ 2 ]. If not handled promptly, it will decrease production and even crop failure [ 3 ].…”

Towards Real-Time Hyperspectral Multi-Image Super-Resolution Reconstruction Applied to Histological Samples

“…In a certain sense, this is by itself an act of gathering information about an object or scene without coming into physical contact. There are many specific cases for which HSI presents an interesting approach, such as satellite remote sensing [ 13 ], food quality assessment [ 14 ], and agriculture [ 15 ], dermatology [ 16 ]. In this publication we focus on medical applications, in particular on computational histology [ 17 ].…”