Abstract
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 16–24 m with a ground resolution of 1–1.5 cm/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.
Highlights
Agricultural crops are usually threatened by multiple pests and diseases causing significant economic losses (Yuan et al, 2014)
It is noted that data on 23/March/2018 and 01/April/2018 are not presented, this is because no yellow rust symptom was observed on ground and no spectral difference was found from UAV aerial image, either
This work aims at exploiting the potentials of low-cost five-band multispectral camera (RedEdge), low-altitude airborne platform and state-of-the-art machine learning techniques in the automatic detection of winter wheat stress caused by yellow rust
Summary
Agricultural crops are usually threatened by multiple pests and diseases (e.g. yellow rust, powdery mildew, aphid) causing significant economic losses (Yuan et al, 2014). A common disease control method is calendar-based application of pesticides irrespective of current disease development and risks. Such a control strategy, incurs a high cost (economically) and increases the likelihood of ground water contamination (environmentally) and pesticide residues in agriculture products (socially) (Moshou et al, 2004). There is a trend to adopt a decision-based disease management strategy in which automated non-destructive plant disease detection/quantification plays an important role, enabling site-specific disease control (Sankaran et al, 2010). It is highly desirable that the detection/quantification method is rapid, specific to a particular disease, and sensitive for detection at an early onset of the symptoms (López et al, 2003). Various types of sensors are available to measure the amount of reflected solar radiation: from low-cost multispectral to high-cost imaging spectrometers, from low spatial to high spatial resolution, and from ground-based to aircraft or even satellitebased (Hunt et al, 2013)
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