Jan Kreuze of the International Potato Center in Peru will develop a low-cost, mobile phone-based diagnostic test for African farmers that uses artificial intelligence to quickly and accurately detect plant diseases such as cassava brown streak and banana bunchy top, which devastate crops and are threatening to spread. Accurately diagnosing plant diseases is difficult because visual symptoms can be highly variable. Artificial intelligence (AI) has shown promise for analyzing images of plants taken by mobile phone to detect diseases in low-resource settings, but it is not accurate enough. Alternatively, chemical-based diagnostic tests that detect the underlying viruses are far more accurate but difficult to use without training and require costly equipment. They will enhance the accuracy of AI for diagnosing a range of plant diseases by mobile phone by training it with validated diagnostic test results from their microfluidic amplification and detection device used by researchers and inspection agents. Their approach has the potential to recognize hard-to-detect symptoms in plants that may even be missed by crop specialists.
More information about Tools and Technologies for Broad-Scale Disease Surveillance of Crop Plants in Low-Income Countries (Round 21)