Application of hyperspectral remote sensing in agriculture

In addition to preventing individual producer losses, early detection will allow for the prevention of spread to neighboring fields or crops. Using diagnostic symptoms of pathogens such as changes in leaf pigments, leaf structure and moisture content, hyperspectral and multispectral imaging can aid in mapping fields for plant disease management.

For example, the fungal pathogen, Puccinia recondite, causes wheat rust characterized by small brown pustules on the leaf surface. In a greenhouse study, wheat plants were inoculated and then hyperspectral and multispectral imaging technologies were compared for their accuracy in distinguishing treated plants from non-treated plants. Franke et al. Compared to multispectral imaging, the higher spectral sensitivity of hyperspectral imaging produced superior detection at an earlier stage of development of the pathogen, when only slight visual symptoms were apparent.

In addition, hyperspectral imaging was much less sensitive to external factors such as illumination conditions than multispectral imaging was. This prevented the poor classification accuracy found in the multispectral imaging data sets. Venturia inaequalis is the pathogen responsible for apple scab in apple trees. Infection first appears as yellow or chlorotic spots on leaves progressing to darker spots and yellowing of the leaves.

Economic losses are caused primarily by damage to the fruit surfaces. Using hyperspectral imaging and statistical procedures for classification, Delalieux et al concluded that stress from apple scab was able to be detected before symptoms were visible to the human eye.

Application of remote sensing in agriculture | Gamaya

Under controlled conditions, Mahlein et al reported that hyperspectral imaging was suitable for, not only the detection, but also the identification and quantification of fungal diseases of sugar beets at the leaf level. This study examined three pathogens of sugar beets, Cercospora leaf spot Cercospora beticola Sacc. The use of hyperspectral imaging can be applied successfully on a larger scale.

Orange rust of sugarcane caused by Puccinia kuehnii is a fungal disease that produces lesions which rupture allowing water to escape from the plant. From images taken at the field level, Apan et al. Late blight of tomato, caused by Phytophthora infestans, is a major threat to tomato production in California.

Using bands in the range of 0.

Following on from the verification of the Photochemical Reflectance Index PRI for whole canopies discussed above, studies have addressed the deficiencies of current ecological monitoring systems to measure global carbon utilization. Without this third data-set, models of global carbon contain substantial uncertainties which restrict their usefulness.

The development of PRI, in combination with hyperspectral imaging, provides a new methodology to obtain more accurate data on light-use efficiency of whole-canopies on a global scale. This methodology offers promise for monitoring large areas of agricultural land for photosynthetic efficiency, which translates into land output and yield data of great value for food production estimations. Nutrient stress in plants causes various symptoms that may be measured by the use of hyperspectral or multispectral imaging.

Both deficiencies in nutrients and heavy metal contamination of soils can be assessed with this technology.

Precision farming services. Remote sensing & hyperspectral imaging

Schuerger et al measured zinc deficiency and toxicity in Bahia grass by using a hyperspectral imager to determine plant chlorophyll levels correlated with stress symptoms. Similarly, mercury levels in mustard plants was assessed by Dunagan et al and spectral reflectance values were significantly correlated with levels of the contaminant. In addition to investigating contamination, another application of hyperspectral imaging is to determine areas in a crop field that are nutrient poor so that fertilizer inputs could be minimized and directly targeted to nutrient poor areas. Nitrogen and phosphorus are the major yield limiting nutrients in midwestern U.

Osborne et al. One important finding of this study was that timing of the images was critical to making accurate estimations of yield. The analysis and mapping of soil characteristics is also possible with hyperspectral and multispectral imaging. Maps of soil properties can improve precision agriculture technologies and enhance capabilities. Researchers in Israel were able to determine soil properties, even for soils under vegetation, with the use of hyperspectral sensors.

Ben-Dor et al mapped soil organic matter, moisture, and soil salinity in a field scale experiment. The possibilities for these types of studies related to precision agriculture are virtually endless as indexes for each species, nutrient or soil property continue to be developed and improved. Studies have been conducted to estimate yield in corn by taking images during the midgrain filling stage and developing yield maps. Currently, many other applications of hyperspectral and multispectral imaging are being tested in: Hyperspectral imaging delivered by lower-cost, portable devices that still deliver high-quality accurate data has become a vital tool for researchers and farmers.

The ability of these devices to enhance and enable day-to-day monitoring promises to create a new paradigm of agricultural efficiency. Precision Agriculture and Hyperspectral Sensors: See More. Detection of Stress-related Spectral Variations The ability of hyperspectral imaging to provide valuable data on the condition and health of crops is predicated on the interaction and relationship between electromagnetic radiation EMR and foliage.

Drought Stress Drought is a significant factor in predicting crop yields and the final success of a crop. Plant Pathogens Fungal pathogens cause serious losses to yields and quality of agricultural crops globally. Ecological Monitoring Following on from the verification of the Photochemical Reflectance Index PRI for whole canopies discussed above, studies have addressed the deficiencies of current ecological monitoring systems to measure global carbon utilization.

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Machine vision technology for agricultural applications. Computers and electronics in Agriculture, 36 2 , Little, Christopher and Summy, Kenneth. Dimitiri Ventzas Ed. InTech, 7. Lillesand, T. Remote sensing and image interpretation. New York: John Wiley and Sons, Inc. Workshop on Imaging Spectroscopy. Rascher, Ewe, et al. Wiegand, C. B, and Harlan, J.

Physiological factors and optical parameters as bases of vegetation discrimination and stress analysis. Houston, Texas: Proceedings of the seminar on: Operational Remote Sensing. Colombo, R, et al. Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling. Remote sensing of environment, Vol. Early drought stress detection in cereals: Functional Plant Biology, Vol. Rossini, M. Assessing canopy PRI from airborne imagery to map water stress in maize.

ISPRS journal of photogrammetry and remote sensing, 86, Jiang, Y. Assessment of narrow-band canopy spectral reflectance and turfgrass performance under drought stress.

Detection of Stress-related Spectral Variations

HortScience, 40 1 , Sankaran, S. A review of advanced techniques for detecting plant diseases. Computers and Electronics in Agriculture, 72 1 , Franke, Jonas, et al. Comparison of multi- and hyperspectral imaging data of leaf rust infected wheat plants. Delalieux, S. Non-parametric statistical approaches and physiological implications.

European Journal of Agronomy, 27 1 , Mahlein, A. Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases. Plant methods, 8 1 , 3. Reduced environmental impact 4.

Higher quality and healthier produce 5. Better prediction and management of risks. When remote sensing produces precise prescriptions for precision agriculture, yields increase and expenses for chemicals, fertilizers, and water decrease. This combination results in higher profit margins for farmers and agricultural producers. Gamaya provides an integrated framework for large-scale airborne hyperspectral remote sensing for precision farming that consists of: At Gamaya we turn high-resolution, high precision aerial imagery data into maps of different problems across farmland parcels.

We process information via our cloud-based system using machine learning and big data analysis techniques. Our analytical framework provides practical information about areas of your farmland experiencing abiotic and biotic stresses, which you can use to create prescriptions for agrochemical and fertilizer treatments. Our forecasting services predict yield and biomass and monitor the development and growth cycles.

The output is delivered in maps for visual assessment, in tables for advanced statistical analysis, and in formats readable by precision agriculture machinery. These different formats are available via an online platform and a mobile application, allowing you to integrate with your farm management software or manage data directly from the field.

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