Abstract
Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a massive global scale.
Highlights
Modern technologies have given human society the ability to produce enough food to meet the demand of more than 7 billion people
Plant diseases are a threat to food security at the global scale, but can have disastrous consequences for smallholder farmers whose livelihoods depend on healthy crops
We evaluate the applicability of deep convolutional neural networks for the classification problem described above
Summary
Modern technologies have given human society the ability to produce enough food to meet the demand of more than 7 billion people. Plant diseases are a threat to food security at the global scale, but can have disastrous consequences for smallholder farmers whose livelihoods depend on healthy crops. More than 80 percent of the agricultural production is generated by smallholder farmers (UNEP, 2013), and reports of yield loss of more than 50% due to pests and diseases are common (Harvey et al, 2014). The largest fraction of hungry people (50%) live in smallholder farming households (Sanchez and Swaminathan, 2005), making smallholder farmers a group that’s vulnerable to pathogen-derived disruptions in food supply. Various efforts have been developed to prevent crop loss due to diseases. Historical approaches of widespread application of pesticides have in the past decade increasingly been supplemented by integrated pest management (IPM) approaches (Ehler, 2006). Independent of the approach, identifying a disease correctly when it first appears is a crucial step for efficient disease management
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