UAV Remote Sensing: An Innovative Tool for Detection and Management of Rice Diseases

Unmanned aerial vehicle (UAV) remote sensing is a new alternative to traditional diagnosis and detection of rice diseases by visual symptoms, providing quick, accurate and large coverage disease detection. UAV remote sensing offers an unprecedented spectral, spatial, and temporal resolution that can distinguish diseased plant tissue from healthy tissue based on the characteristics of disease symptoms. Research has been conducted on using RGB sensor, multispectral sensor, and hyperspectral sensor for successful detection and quantification of sheath blight (Rhizoctonia solani), using multispectral sensor to accurately detect narrow brown leaf spot (Cercospora janseana), and using infrared thermal sensor for detecting the occurrence of rice blast (Magnaporthe oryzae). UAV can also be used for aerial application, and UAV spraying has become a new means for control of rice sheath blight and other crop diseases in many countries, especially China and Japan. UAV spraying can operate at low altitudes and various speeds, making it suitable for situations where arial and ground applications are unavailable or infeasible and where precision applications are needed. Along with advances in digitalization and artificial intelligence for precision application across fertilizer, pest and crop management needs, this UAV technology will become a core tool in a farmer’s precision equipment mix in the future

High-Throughput Phenotyping of Fire Blight Disease Symptoms Using Sensing Techniques in Apple

Washington State produces about 70% of total fresh market apples in the United States. One of the primary goals of apple breeding programs is the development of new cultivars resistant to devastating diseases such as fire blight. The overall objective of this study was to investigate high-throughput phenotyping techniques to evaluate fire blight disease symptoms in apple trees. In this regard, normalized stomatal conductance data acquired using a portable photosynthetic system, image data collected using RGB and multispectral cameras, and visible-near infrared spectral reflectance acquired using a hyperspectral sensing system, were independently evaluated to estimate the progression of fire blight infection in young apple trees. Sensors with ranging complexity – from simple RGB to multispectral imaging to hyperspectral system – were evaluated to select the most accurate technique for the assessment of fire blight disease symptoms. The proximal multispectral images and visible-near infrared spectral reflectance data were collected in two field seasons (2016, 2017); while, proximal side-view RGB images and multispectral images using unmanned aerial systems were collected in 2017. The normalized stomatal conductance data was correlated with disease severity rating (r = 0.51, P < 0.05). The features extracted from RGB images (e.g., maximum length of senesced leaves, area of senesced leaves, ratio between senesced and healthy leaf area) and multispectral images (e.g., vegetation indices) also demonstrated potential in evaluation of disease rating (|r| > 0.35, P < 0.05). The average classification accuracy achieved using visible-near infrared spectral reflectance data during the classification of susceptible from symptomless groups ranged between 71 and 93% using partial least square regression and quadratic support vector machine. In addition, fire blight disease ratings were compared with normalized difference spectral indices (NDSIs) that were generated from visible-near infrared reflectance spectra. The selected spectral bands in the range 710–2,340 nm used for computing NDSIs showed consistently higher correlation with disease severity rating than data acquired from RGB and multispectral imaging sensors across multiple seasons. In summary, these specific spectral bands can be used for evaluating fire blight disease severity in apple breeding programs and potentially as early fire blight disease detection tool to assist in production systems

Science Takes Flight: Detection of Black Leg on Turnip Gray Mold on Hemp

Disease detection through traditional techniques such as scouting fields on foot, molecular assays, or morphological identification of plant pathogens is time consuming and costly. Disease diagnosis in the field can be extremely subjective, and largely depends on the experience and knowledge of pathogen identification and disease quantification. This thesis provides an evaluation of remote sensing for assessing disease in agricultural field settings via an unmanned aerial vehicle and machine learning. Case studies are presented on two different fungal diseases important in western Oregon crop production: black leg on turnip (incited by Leptosphaeria spp.) and gray mold on hemp (caused by Botrytis spp.). Both case studies utilized a support vector machine model to classify pixels of digital images collected with a multispectral Micasense RedEdge-M optical sensor. Turnip leaves were imaged at 1.5 m in situ while hemp plant images were collected by an unmanned aerial vehicle with flights at 10 m ex situ. Detection of pixels exemplifying black leg leaf spot symptoms on turnip leaves had an overall accuracy of 97.0% with a model sensitivity of 0.48. The support vector machine model utilized in gray mold detection on hemp incorporated a novel vegetation index, a modified green-red vegetation index along with the triangular greenness index, to identify pixels of diseased hemp inflorescences extracted from background soil and vegetation. The model had an overall accuracy of 95.8% when identifying a hemp inflorescence as diseased or non-diseased. False negatives were found to be high with a sensitivity of 0.70 in the hemp model. Additionally, gray mold disease incidence determined using the support vector machine model was compared with disease assessments collected by scouting on foot and was found to have similar treatment rankings, although the differences in the relative percentages between the two methods were found to be large. The findings of this study provide the foundation for further development of remote sensing techniques for black leg disease assessments in Brassica crops and potential deployment of remote sensing strategies for measuring gray mold in hemp fields