Forty percent wheat yield reduction is reported globally due to crown rot (Fusarium pseudograminearum). An emerging approach for sensor-based disease discrimination is the use of spectral reflectance with combinations of wavebands and varying bandwidths, which has potential to reduce the impact of environmental factors on spectral sensitivity detection accuracy. Transferring such technology from a laboratory to field environment presents challenges, particularly in regard to producing adequately robust models.An experiment was conducted in which near-infrared spectral reflectance data was captured in a glasshouse environment, for cultivars of winter wheat with varying resistances to F. pseudograminearum. A contact sensor sensitive to shortwave infrared (900-1700 nm) wavebands was used. Raw sensor data was calibrated and transformed using a discrete wavelet approach, allowing for variable waveband size. Optimised machine learning disease identification models were compared across the nine weeks following inoculation with F. pseudograminearum. Models were compared for the ability to accurately detect crown rot across weeks.The results show crown rot detection ability with accuracies ranging from 49-74%, as well as a temporal patterning effect as the season progresses. An artificial neural network classifier (ANN) performed best with a top accuracy of 74.14%, of the six machine learning algorithms trialed. Waveform differences between plus and minus treatments indicate that the sensing approach has potential to be scaled to a camera-based system for use on remote sensing platforms. Further work is being conducted to understand the viability of such an approach, which is an important step towards both robotic and RPA-based disease discrimination.

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.