Identification of purple spot disease on asparagus crops across spatial and spectral scales

Navrozidis, Ioannis; Alexandridis, Thomas; Dimitrakos, Agathoklis; Lagopodi, Anastasia L.; Moshou, Dimitrios; Zalidis, George C.

doi:10.1016/j.compag.2018.03.035

Cited by 39 publications

(12 citation statements)

References 25 publications

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“…Remote sensing medium resolution imagery has shown a great capacity to quantify damages in crop lands. The satellites MODIS and Landsat have been widely used [12][13][14], but Sentinel-2, especially for its finer resolution, constitutes a major asset for this kind of application [15,16]. However, due to the types of damages that affect the majority of the crops, satellite resolutions are still too coarse.…”

Section: Introductionmentioning

confidence: 99%

“…However, due to the types of damages that affect the majority of the crops, satellite resolutions are still too coarse. In this sense, imagery provided by the high spatial resolution commercial satellites as Deimos-2, GeoEye-2, QuickBird, or WorldView-2 could be an appropriate option [14,17,18]. Nevertheless, in some specific decision making contexts the cost-benefit ratio is too high, especially if multiple images of different dates are required or large surfaces are needed.…”

Section: Introductionmentioning

confidence: 99%

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Calibrating Sentinel-2 Imagery with Multispectral UAV Derived Information to Quantify Damages in Mediterranean Rice Crops Caused by Western Swamphen (Porphyrio porphyrio)

Pla

Bota

Duane

et al. 2019

Drones

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Making agricultural production compatible with the conservation of biological diversity is a priority in areas in which human–wildlife conflicts arise. The threatened Western Swamphen (Porphyrio porphyrio) feeds on rice, inducing crop damage and leading to decreases in rice production. Due to the Swamphen protection status, economic compensation policies have been put in place to compensate farmers for these damages, thus requiring an accurate, quantitative, and cost-effective evaluation of rice crop losses over large territories. We used information captured from a UAV (Unmanned Aerial Vehicle) equipped with a multispectral Parrot SEQUOIA camera as ground-truth information to calibrate Sentinel-2 imagery to quantify damages in the region of Ebro Delta, western Mediterranean. UAV vegetation index NDVI (Normalized Difference Vegetation Index) allowed estimation of damages in rice crops at 10 cm pixel resolution by discriminating no-green vegetation pixels. Once co-registered with Sentinel grid, we predicted the UAV damage proportion at a 10 m resolution as a function of Sentinel-2 NDVI, and then we extrapolated the fitted model to the whole Sentinel-2 Ebro Delta image. Finally, the damage predicted with Sentinel-2 data was quantified at the agricultural plot level and validated with field information compiled on the ground by Rangers Service. We found that Sentinel2-NDVI data explained up to 57% of damage reported with UAV. The final validation with Rangers Service data pointed out some limitations in our procedure that leads the way to improving future development. Sentinel2 imagery calibrated with UAV information proved to be a viable and cost-efficient alternative to quantify damages in rice crops at large scales.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Calibrating Sentinel-2 Imagery with Multispectral UAV Derived Information to Quantify Damages in Mediterranean Rice Crops Caused by Western Swamphen (Porphyrio porphyrio)

Pla

Bota

Duane

et al. 2019

Drones

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“…Spatial resolution has a known influence on pathogen mapping. In general, improved spatial resolution is expected to improve detection [61]. Leaf disease mapping in other crops, such as the powdery wilt of wheat, showed that the detection by multispectral satellite imagery is not trivial [62].…”

Section: Discussionmentioning

confidence: 99%

Identification of Ramularia Leaf Blight Cotton Disease Infection Levels by Multispectral, Multiscale UAV Imagery

Xavier

Souto

Statella

et al. 2019

Drones

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The reduction of the production cost and negative environmental impacts by pesticide application to control cotton diseases depends on the infection patterns spatialized in the farm scale. Here, we evaluate the potential of three-band multispectral imagery from a multi-rotor unmanned airborne vehicle (UAV) platform for the detection of ramularia leaf blight from different flight heights in an experimental field. Increasing infection levels indicate the progressive degradation of the spectral vegetation signal, however, they were not sufficient to differentiate disease severity levels. At resolutions of ~5 cm (100 m) and ~15 cm (300 m) up to a ground spatial resolution of ~25 cm (500 m flight height), two-scaled infection levels can be detected for the best performing algorithm of four classifiers tested, with an overall accuracy of ~79% and a kappa index of ~0.51. Despite limited classification performance, the results show the potential interest of low-cost multispectral systems to monitor ramularia blight in cotton.

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“…Hyperspectral imaging, which combines spectroscopy with imaging methods, allows collection of canopy images and delivers representative re ectance data that are useful for the determination of plant phosphorus status in the eld. In recent years, this technique has been effectively employed for various crops to estimate biophysical parameters, such as leaf area index [13,14], leaf and fruit pigment content [15][16][17][18], biomass [19] as well as detection of diseases and fungal infections [20][21][22][23]. Several studies have been reported on the spectral changes related to leaf water content [24,25], chlorophyll content [26] and macronutrient content, e.g., nitrogen [27,28] and potassium [29].…”

Section: Introductionmentioning

confidence: 99%

Identification of Plant Leaf Phosphorus Content at Different Growth Stages Based on Hyperspectral Reflectance

Siedliska¹,

Baranowski²,

Pastuszka-Woźniak³

et al. 2020

Preprint

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Background: Modern agriculture strives to sustainably manage fertilizer for both economic and environmental reasons. The monitoring of any nutritional (phosphorus, nitrogen, potassium) deficiency in growing plants is a challenge for precision farming technology. A study was carried out on three species of popular crops, celery (Apium graveolens L., cv. Neon), sugar beet (Beta vulgaris L., cv. Tapir) and strawberry (Fragaria × ananassa Duchesne, cv. Honeoye), fertilized with four different doses of phosphorus (P) to deliver data for non-invasive detection of P content. Results: Data obtained via biochemical analysis of the chlorophyll and carotenoid contents in plant material showed that the strongest effect of P availability for plants was in the diverse total chlorophyll content in sugar beet and celery compared to that in strawberry, in which P affects a variety of carotenoid contents in leaves. The measurements performed using hyperspectral imaging, obtained in several different stages of plant development, were applied in a supervised classification experiment. A machine learning algorithm (Backpropagation Neural Network, Random Forest, Naive Bayes and Support Vector Machine) was developed to classify plants from four variants of P fertilization. The lowest prediction accuracy was obtained for the earliest measured stage of plant development. Statistical analyses showed correlations between leaf biochemical constituents, phosphorus fertilization and the mass of the leaf/roots of the plants. Conclusions: Obtained results demonstrate that hyperspectral imaging combined with artificial intelligence methods has potential for non-invasive detection of non-homogenous phosphorus fertilization on crop levels.

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Identification of purple spot disease on asparagus crops across spatial and spectral scales

Cited by 39 publications

References 25 publications

Calibrating Sentinel-2 Imagery with Multispectral UAV Derived Information to Quantify Damages in Mediterranean Rice Crops Caused by Western Swamphen (Porphyrio porphyrio)

Calibrating Sentinel-2 Imagery with Multispectral UAV Derived Information to Quantify Damages in Mediterranean Rice Crops Caused by Western Swamphen (Porphyrio porphyrio)

Identification of Ramularia Leaf Blight Cotton Disease Infection Levels by Multispectral, Multiscale UAV Imagery

Identification of Plant Leaf Phosphorus Content at Different Growth Stages Based on Hyperspectral Reflectance

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