Abstract
Pathogens and animal pests (P&A) are a major threat to global food security as they directly affect the quantity and quality of food. The Southern Amazon, Brazil’s largest domestic region for soybean, maize and cotton production, is particularly vulnerable to the outbreak of P&A due to its (sub)tropical climate and intensive farming systems. However, little is known about the spatial distribution of P&A and the related yield losses. Machine learning approaches for the automated recognition of plant diseases can help to overcome this research gap. The main objectives of this study are to (1) evaluate the performance of Convolutional Neural Networks (ConvNets) in classifying P&A, (2) map the spatial distribution of P&A in the Southern Amazon, and (3) quantify perceived yield and economic losses for the main soybean and maize P&A. The objectives were addressed by making use of data collected with the smartphone application Plantix. The core of the app’s functioning is the automated recognition of plant diseases via ConvNets. Data on expected yield losses were gathered through a short survey included in an “expert” version of the application, which was distributed among agronomists. Between 2016 and 2020, Plantix users collected approximately 78,000 georeferenced P&A images in the Southern Amazon. The study results indicate a high performance of the trained ConvNets in classifying 420 different crop-disease combinations. Spatial distribution maps and expert-based yield loss estimates indicate that maize rust, bacterial stalk rot and the fall armyworm are among the most severe maize P&A, whereas soybean is mainly affected by P&A like anthracnose, downy mildew, frogeye leaf spot, stink bugs and brown spot. Perceived soybean and maize yield losses amount to 12 and 16%, respectively, resulting in annual yield losses of approximately 3.75 million tonnes for each crop and economic losses of US$2 billion for both crops together. The high level of accuracy of the trained ConvNets, when paired with widespread use from following a citizen-science approach, results in a data source that will shed new light on yield loss estimates, e.g., for the analysis of yield gaps and the development of measures to minimise them.
Highlights
IntroductionPathogens and animal pests (P&A) are major challenges to global food security, directly affecting the quantity (reduced productivity) and quality (e.g., reduced content of valuable nutrients, poorer market quality, and inferior storage characteristics) of food (Oerke, 2006)
Pathogens and animal pests (P&A) are major challenges to global food security, directly affecting the quantity and quality of food (Oerke, 2006
The use of pesticides in Brazil increased from approximately50 million tonnes in 1990 to 378 million tonnes in 2017 (FAOSTAT, 2019), with Mato Grosso (MT) reporting the largest amount of pesticide use (Pignati et al, 2017)
Summary
Pathogens and animal pests (P&A) are major challenges to global food security, directly affecting the quantity (reduced productivity) and quality (e.g., reduced content of valuable nutrients, poorer market quality, and inferior storage characteristics) of food (Oerke, 2006). Pathogens and animal pests (P&A) are major challenges to global food security, directly affecting the quantity (reduced productivity) and quality (e.g., reduced content of valuable nutrients, poorer market quality, and inferior storage characteristics) of food (Oerke, 2006)1 They can cause devastating yield losses, leading to malnutrition and starvation, as several examples in history have shown (e.g., the Irish Potato Famine (1845–49), caused by potato leaf blight; and witches’ broom disease, Moniliophthora perniciosa, which destroyed Brazil’s leading position in world cocoa production). Since its first occurrence in Brazil in the early 2000s, the fungus has caused annual yield losses in the range of 360,000–4.6 million tonnes, and economic losses (grain loss +pest control costs) of approximately US$0.18–2.38 billion per year (Godoy et al, 2016)
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