Hyperspectral imaging in crop fields: precision agriculture

Caballero, Daniel; Calvini, Rosalba; Amigo, José Manuel

doi:10.1016/b978-0-444-63977-6.00018-3

Cited by 81 publications

(39 citation statements)

References 108 publications

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“…Though not always consistent across growth stage and fertilizer rate, chlorophyll or protein indicators can be used as proxies for N status due to the strong relationship between Ncontaining compounds and N content (Fu et al, 2021). Many different vegetation indices are widely used to estimate crop N content or accumulation, alleviating confounding factors from soils or water, which are generally calculated from the leaf or canopy reflectance values of wavebands in the visible and nearinfrared regions (Zhang et al, 2018;Caballero et al, 2020;Fu et al, 2021). Rapid developments in sensing technologies coupled with machine learning (and other techniques) have increased our abilities to accurately predict yield and non-destructively estimate plant N status (Yao et al, 2015;Chlingaryan et al, 2018).…”

Section: Precision Agriculture and Nuementioning

confidence: 99%

A Research Road Map for Responsible Use of Agricultural Nitrogen

Udvardi

Below

Castellano

et al. 2021

Front. Sustain. Food Syst.

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Nitrogen (N) is an essential but generally limiting nutrient for biological systems. Development of the Haber-Bosch industrial process for ammonia synthesis helped to relieve N limitation of agricultural production, fueling the Green Revolution and reducing hunger. However, the massive use of industrial N fertilizer has doubled the N moving through the global N cycle with dramatic environmental consequences that threaten planetary health. Thus, there is an urgent need to reduce losses of reactive N from agriculture, while ensuring sufficient N inputs for food security. Here we review current knowledge related to N use efficiency (NUE) in agriculture and identify research opportunities in the areas of agronomy, plant breeding, biological N fixation (BNF), soil N cycling, and modeling to achieve responsible, sustainable use of N in agriculture. Amongst these opportunities, improved agricultural practices that synchronize crop N demand with soil N availability are low-hanging fruit. Crop breeding that targets root and shoot physiological processes will likely increase N uptake and utilization of soil N, while breeding for BNF effectiveness in legumes will enhance overall system NUE. Likewise, engineering of novel N-fixing symbioses in non-legumes could reduce the need for chemical fertilizers in agroecosystems but is a much longer-term goal. The use of simulation modeling to conceptualize the complex, interwoven processes that affect agroecosystem NUE, along with multi-objective optimization, will also accelerate NUE gains.

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Section: Precision Agriculture and Nuementioning

confidence: 99%

A Research Road Map for Responsible Use of Agricultural Nitrogen

Udvardi

Below

Castellano

et al. 2021

Front. Sustain. Food Syst.

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“…With the image-based VIS/NIR approach, thanks to the combination of spectral information with the spatial and temporal dimensions, it is possible to estimate the occurrence of stressful conditions even at landscape scale (Zhang et al, 2019a ). Spaceborne, airborne and ground-based, help to monitor in real-time the water status, biomass and yield, nutrient status, disease, and pests (Xue and Su, 2017 ; Maes and Steppe, 2019 ; Zhang et al, 2019a ; Caballero et al, 2020 ), thanks also to combined elaboration of ground-based hyperspectral collected data with hand-carried radiometers and spectroradiometers and UAVs imaging data (Zheng et al, 2018 ). In Table 2 , an overview of the recent literature about the application of hyperspectral imaging for stress detection has been reported.…”

Section: Remote Sensing Qualitative Methods For Stress Assessmentmentioning

confidence: 99%

Past and Future of Plant Stress Detection: An Overview From Remote Sensing to Positron Emission Tomography

Galieni¹,

et al. 2021

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Plant stress detection is considered one of the most critical areas for the improvement of crop yield in the compelling worldwide scenario, dictated by both the climate change and the geopolitical consequences of the Covid-19 epidemics. A complicated interconnection of biotic and abiotic stressors affect plant growth, including water, salt, temperature, light exposure, nutrients availability, agrochemicals, air and soil pollutants, pests and diseases. In facing this extended panorama, the technology choice is manifold. On the one hand, quantitative methods, such as metabolomics, provide very sensitive indicators of most of the stressors, with the drawback of a disruptive approach, which prevents follow up and dynamical studies. On the other hand qualitative methods, such as fluorescence, thermography and VIS/NIR reflectance, provide a non-disruptive view of the action of the stressors in plants, even across large fields, with the drawback of a poor accuracy. When looking at the spatial scale, the effect of stress may imply modifications from DNA level (nanometers) up to cell (micrometers), full plant (millimeters to meters), and entire field (kilometers). While quantitative techniques are sensitive to the smallest scales, only qualitative approaches can be used for the larger ones. Emerging technologies from nuclear and medical physics, such as computed tomography, magnetic resonance imaging and positron emission tomography, are expected to bridge the gap of quantitative non-disruptive morphologic and functional measurements at larger scale. In this review we analyze the landscape of the different technologies nowadays available, showing the benefits of each approach in plant stress detection, with a particular focus on the gaps, which will be filled in the nearby future by the emerging nuclear physics approaches to agriculture.

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“…These conditions are also far more detrimental than a pathogen-induced stress: lack of nutrients, water or hyper salinity can cause up to a 70% reduction in the crop yield (Mantri et al, 2012;Waqas et al, 2019). There are several imaging techniques, such as hyperspectral imaging and thermography, that potentially can be used for an indirect detection of abiotic stresses in plants (Bauer et al, 2019;Caballero et al, 2020). These techniques allow for fast imaging of broad field areas and identification of "problematic areas" (Baena et al, 2017;Lu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Raman-Based Diagnostics of Biotic and Abiotic Stresses in Plants. A Review

Payne

Kurouski

2021

Front. Plant Sci.

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Digital farming is a novel agricultural philosophy that aims to maximize a crop yield with the minimal environmental impact. Digital farming requires the development of technologies that can work directly in the field providing information about a plant health. Raman spectroscopy (RS) is an emerging analytical technique that can be used for non-invasive, non-destructive, and confirmatory diagnostics of diseases, as well as the nutrient deficiencies in plants. RS is also capable of probing nutritional content of grains, as well as highly accurate identification plant species and their varieties. This allows for Raman-based phenotyping and digital selection of plants. These pieces of evidence suggest that RS can be used for chemical-free surveillance of plant health directly in the field. High selectivity and specificity of this technique show that RS may transform the agriculture in the US. This review critically discusses the most recent research articles that demonstrate the use of RS in diagnostics of abiotic and abiotic stresses in plants, as well as the identification of plant species and their nutritional analysis.

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Hyperspectral imaging in crop fields: precision agriculture

Cited by 81 publications

References 108 publications

A Research Road Map for Responsible Use of Agricultural Nitrogen

A Research Road Map for Responsible Use of Agricultural Nitrogen

Past and Future of Plant Stress Detection: An Overview From Remote Sensing to Positron Emission Tomography

Raman-Based Diagnostics of Biotic and Abiotic Stresses in Plants. A Review

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