Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery

The use of a low-cost five-band multispectral camera (RedEdge, MicaSense, USA) and a low-altitude airborne platform is investigated for the detection of plant stress caused by yellow rust disease in winter wheat for sustainable agriculture. The research is mainly focused on: (i) determining whether or not healthy and yellow rust infected wheat plants can be discriminated; (ii) selecting spectral band and Spectral Vegetation Index (SVI) with a strong discriminating capability; (iii) developing a low-cost yellow rust monitoring system for use at farmland scales. An experiment was carefully designed by infecting winter wheat with different levels of yellow rust inoculum, where aerial multispectral images under different developmental stages of yellow rust were captured by an Unmanned Aerial Vehicle at an altitude of 16–24m with a ground resolution of 1–1.5cm/pixel. An automated yellow rust detection system is developed by learning (via random forest classifier) from labelled UAV aerial multispectral imagery. Experimental results indicate that: (i) good classification performance (with an average Precision, Recall and Accuracy of 89.2%, 89.4% and 89.3%) was achieved by the developed yellow rust monitoring at a diseased stage (45 days after inoculation); (ii) the top three SVIs for separating healthy and yellow rust infected wheat plants are RVI, NDVI and OSAVI; while the top two spectral bands are NIR and Red. The learnt system was also applied to the whole farmland of interest with a promising monitoring result. It is anticipated that this study by seamlessly integrating low-cost multispectral camera, low-altitude UAV platform and machine learning techniques paves the way for yellow rust monitoring at farmland scales

Spatial autocorrelation of inoculum density of <i>Verticillium dahliae</i> from point sampling.

<p>(A) individual fields, and (B) pooled over three fields. Within each field, 70 samples were collected by point sampling (at depth 0–20 cm).</p

Optically levitated gyroscopes with a MHz rotating micro-rotor

The optically levitated particles have been driven to rotate at an ultra-high speed of GHz, and the gyroscopic application of these levitated particles to measure angular motion have long been explored. However, this gyroscope has not been proven either theoretically or experimentally. Here, a rotor gyroscope based on optically levitated high-speed rotating particles is proposed. In vacuum, an ellipsoidal vaterite particle with 3.58 $\mu$m average diameter is driven to rotate at MHz, and the optical axis orientation of the particle is measured by the particle rotational signal. The external inputted angular velocity makes the optical axis deviate from the initial position, which changes the frequency and amplitude of the rotational signal. The inputted angular velocity is hence detected by the rotational signal, and the angular rate bias instability of the prototype is measured to be $0.08^o/s$. It is the smallest rotor gyroscope in the world, and the bias instability can be further improved up to $10^{-9o}/h$ theoretically by cooling the motion and increasing the angular moment of the levitated particle. Our work opens a new application paradigm of the levitated optomechanical systems and possibly bring the rotor gyroscope to the quantum realm

Number of cotton plants in each wilt category, assessed in six commercial plantations in 2013.

<p>Number of cotton plants in each wilt category, assessed in six commercial plantations in 2013.</p

Inoculum density of <i>Verticillium dahliae</i> from point sampling.

<p>Histogram (A) and spatial pattern (B) of estimated inoculum density (CFU g^-1 of soil) of <i>Verticillium dahliae</i> in three commercial cotton fields (W_A, W_B, and W_C) using a real-time quantitative PCR method. Within each row, 35 soil samples (at depth 0–20cm) were taken; the circle size is proportional to the CFU estimate at the point (the largest circle corresponds to 300 CFU g^-1 of soil in W_C).</p

Estimated <i>Verticillium dahliae</i> inoculum densities in soil plotted against wilt scores of individual plants.

<p>These plantations (W_A, W_B, W_C) were located in Weinan, Shaanxi. The distance between soil sampling points and the plant stem was ca. 15 cm. Wilt on each plant in each plant was scored as: 0—no symptoms; 1—leaves with wilt symptoms ≤ 25%; 2—leaves with wilt symptoms > 25% and ≤ 50%; 3—leaves with wilt symptoms > 50% and ≤ 75%; and 4—>75% leaves with wilt symptoms.</p

A diagram illustrating the point sample positions.

<p>Soil was sampled in three commercial cotton fields to study spatial relationship in the density of <i>Verticillium dahliae</i> inoculum. Numbers indicate the distances (m) from sampling points to first sampling point in the same row. Sampling was conducted in three commercial fields in late August 2013, Weinan, Shaanxi Province, China.</p

Quantified <i>Puccinia striiformis</i> f. sp. <i>tritici</i> in inoculated wheat leaves (cv. Mingxian 169) from day 0 to day 30 after inoculation using qPCR of DNA and RNA (cDNA).

<p>Urediniospores on the leaf surface were removed through washing leaves with distilled water. On day 20, more than 50% leaf area senesced, and on day 30, the leaves were completely senesced. The experiment was done three times. The vertical bar of a mean value represents the standard deviation among the three repeats of the experiment; significant treatment differences were based on the pooled residual error in the repeated measurement ANOVA. The level of quantified <i>Pst</i> DNA with “**” on 8, 9, 10, 20 and 30 days was significantly different from that based on the RNA quantification at <i>P</i> = 0.01. There were no significant (<i>P</i> > 0.05) differences between quantified levels of <i>Pst</i> DNA and RNA on other days.</p

Number of sequenced expression tags during the germination of <i>Verticillium dahliae</i> microsclerotia in the germinated (VDMG-b) and non-germinated (VDM) microsclerotia.

<p>Clean tags are tags after dirty tags (low quality tags) were excluded from the raw data. Unambiguous tags are the remaining clean tags after those tags that mapped to reference sequences from multiple genes were removed from the data.</p

Pedigrees, levels of winter-hardness, and breeding institute of wheat cultivars used to study the survival of wheat stripe rust under constant temperatures.

<p>Pedigrees, levels of winter-hardness, and breeding institute of wheat cultivars used to study the survival of wheat stripe rust under constant temperatures.</p