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In Australia, the soil-borne disease common root rot (Bipolaris sorokiniana) (CRR) in wheat (Triticum aestivum L.) leads to substantial yield losses, yet has limited visible aboveground symptoms, making detection and identification labor intensive. Near-infrared (NIR) spectroscopy offers an early potential identification solution for CRR in wheat and has previously been reported with success for crop disease detection. This study investigated the ability of nondestructive NIR spectroscopy in combination with deep neural networks (DNN), logistic regression (LR), and principal component analysis combined with support vector machines (PCA-SVM) for early-stage CRR detection in wheat. NIR spectra of five different wheat varieties with varying resistance to CRR were collected in two seasons of glasshouse and three seasons of field trials using a portable spectrometer. Results demonstrated that DNN outperformed LR and PCA-SVM, achieving 66%–91% average classification accuracy in glasshouse trials and an average accuracy of 73% with up to 87% in field trials, effectively distinguishing inoculated and non-inoculated wheat plants from seedling to anthesis stages. Validation with a third season of field data achieved an average of 77% accuracy for the most susceptible variety during the stem elongation stage. NIR reflectance within 1600–1700 nm was identified as most important for estimating CRR presence, with initial detection occurring 35 days after sowing (DAS) in the glasshouse and 46 DAS in the field. In conclusion, a NIR spectrometer with a DNN model successfully performed disease classification, with the potential as a portable early disease detection tool to assist farm management decisions. [ABSTRACT FROM AUTHOR]

In Australia, the soil-borne disease common root rot (Bipolaris sorokiniana) (CRR) in wheat (Triticum aestivum L.) leads to substantial yield losses, yet has limited visible aboveground symptoms, making detection and identification labor intensive. Near-infrared (NIR) spectroscopy offers an early potential identification solution for CRR in wheat and has previously been reported with success for crop disease detection. This study investigated the ability of nondestructive NIR spectroscopy in combination with deep neural networks (DNN), logistic regression (LR), and principal component analysis combined with support vector machines (PCA-SVM) for early-stage CRR detection in wheat. NIR spectra of five different wheat varieties with varying resistance to CRR were collected in two seasons of glasshouse and three seasons of field trials using a portable spectrometer. Results demonstrated that DNN outperformed LR and PCA-SVM, achieving 66%–91% average classification accuracy in glasshouse trials and an average accuracy of 73% with up to 87% in field trials, effectively distinguishing inoculated and non-inoculated wheat plants from seedling to anthesis stages. Validation with a third season of field data achieved an average of 77% accuracy for the most susceptible variety during the stem elongation stage. NIR reflectance within 1600–1700 nm was identified as most important for estimating CRR presence, with initial detection occurring 35 days after sowing (DAS) in the glasshouse and 46 DAS in the field. In conclusion, a NIR spectrometer with a DNN model successfully performed disease classification, with the potential as a portable early disease detection tool to assist farm management decisions. [ABSTRACT FROM AUTHOR]

Crown rot (CR; Fusarium pseudograminearum) is a serious disease in winter cereals. Soil type, temperature, nutrients, water availability and stubble‐borne inoculum levels play major roles in determining disease severity. This paper reports the impact of two different watering regimes on the disease severity and gas exchange of F. pseudograminearum infected bread wheat for the first time. Fusarium pseudograminearum inoculated and noninoculated genotypes with different susceptibility to CR were watered to either field capacity or a reduced watering regime in three controlled environment experiments. Rate of photosynthesis, stomatal conductance, internal CO2 concentration and transpiration rate were measured using a portable photosynthesis system, together with disease severity of leaf sheaths at 28 days after planting. Significant differences in disease severity were reported between watering treatments with reduction in CR symptoms in the partially resistant genotypes in the reduced water treatment. Photosynthesis, stomatal conductance and transpiration rate were significantly decreased across most genotypes when inoculated with F. pseudograminearum. Differences in gas exchange between inoculum treatments were more evident in plants watered to field capacity. Water availability has been reported to be one of the crucial factors for initiating F. pseudograminearum infection and subsequent development of CR disease. This research demonstrates significant variation in genotype‐related responses to the complex interactions of F. pseudograminearum infection and water treatment, with a negative impact of both limited soil water availability and CR disease severity on plant gas exchange in bread wheat. [ABSTRACT FROM AUTHOR]

Common root rot (CRR) caused by the soilborne pathogen Bipolaris sorokiniana (teleomorph Cochliobolus sativus) is becoming increasingly prevalent worldwide. Identification of CRR is difficult and time‐consuming for human assessors due to the non‐distinctive above‐ground symptoms, with browning of subcrown internodes and roots the most distinguishing symptom of infection. CRR disease has been recognized as a significant disease for cereal crops in many countries. In 2009, CRR in Australia was estimated to cause $30 million average annual yield loss for wheat and $13 million for barley. Recent evidence indicates CRR may be more prevalent than expected in Australian wheat cropping areas due to lack of research on this disease. Low levels of B. sorokiniana survive in the soil for up to 10 years and attack plants at early stages of growth. Therefore, mitigating CRR in wheat and barley may not be practical at the late stages of infection due to lack of effective methods; however, early detection might be viable to alleviate the impact of this disease. A comprehensive overview of CRR caused by B. sorokiniana, including disease background, worldwide economic losses, management methods, potential CRR detection using multispectral and hyperspectral sensors and the research focus over the past 50 years is provided in this article. This review paper is expected to provide thorough supplemental information for current studies about CRR and proposes recommendations for whole‐of‐field disease scouting methods to farmers, enabling reduced time and cost for CRR management and increasing wheat and barley production worldwide. [ABSTRACT FROM AUTHOR]