Applying advanced technologies such as computer vision is highly desirable in seed testing. Among testing needs, computer vision is a feasible technology for conducting seed and seedling classification used in purity analysis and in germination tests. This review focuses on seed identification that currently encounters extreme challenges due to a shortage of expertise, time-consuming training and operation, and the need for large numbers of reference specimens. The reviewed computer vision techniques and application strategies also apply to other methods in seed testing. The review describes the development of machine learning-based computer vision in automating seed identification and their limitations in feature extraction and accuracy. As a subset of machine learning techniques, deep learning has been applied successfully in many agricultural domains, which presents potential opportunities for its application in seed identification and seed testing. To facilitate application in seed testing, the challenges of deep learning-based computer vision systems are summarised through analysing their application in other agricultural domains. It is recommended to accelerate the application in seed testing by optimising procedures or approaches in image acquisition technologies, dataset construction and model development. A concept flow chart for using computer vision systems is proposed to advance computer-assisted seed identification.

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Rapidly progressive glomerulonephritis is one of the most dramatic and tragic presentations of lupus nephritis (LN) or renal manifestation of systemic lupus erythematosus (SLE). A 35-year-old Bangladeshi gentleman presented with worsening oedema, scanty, high colored, frothy urine and deteriorating renal function. He had puffy face, anaemia, oedema, normal jugular venous pressure (JVP), high blood pressure (150/90 mm Hg), ascites and bilateral pleural effusions. Diagnostic work-up confirmed SLE with class IV LN. His initial response to specific therapy showed improvement Birdem Med J 2020; 10(2): 137-138