Abstract
Maize is the second most plentiful cereal grown for human consumption. It constitutes 36% of total grain production worldwide and it is cultivated in about 160 countries on nearly 150 m ha. Maize faces fungal diseases causing extraordinary reduction in the grain yield. Fungi are responsible for many maize foliar diseases. Fungicides show hazardous effects on human health and also soil and water pollution. Near-infrared (NIR) images can disclose damage patterns not visible to the naked eye or depicted in RGB images. Unmanned aerial vehicles (UAV) are an inexpensive way to collect low altitude images. State-of-the-art Convolutional Neural Networks (CNN) have proven excellent results in image classification in computer vision. This study presents a novel Transfer Learning (TL) based CNN technique and states the hypothesis that NIR images acquired by UAVs contribute to a more precise classification of pathogens in maize. GPS coordinates of the infested areas are also provided for precision spraying with fungicide agents for specific targets, representing an economical mean for yield protection and with the least possible hazard to people and to the ecosystem. The proposed model was evaluated on its performance using different metrics achieving an accuracy of 86.7%, precision 98%, sensitivity 86.9% and F1 Score 92%. According to the state-of-the-art literature consulted, this is the first time that a validated deep learning-based approach has been applied in fungal diseases classification using infrared images.
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Notes
Nanometer [nm]. 1 nm = 0.001 µm (Micrometer) = 1.0E−9 m.
Overfitting occurs when a statistical model fits exactly its training data. When this happens, the model cannot perform its ultimate goal, the capacity of generalization against unseen data.
FLOPs stand for floating points operations.
Predominant class in the dataset.
Labels are represented with a binary value of 1 or 0.
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Antolínez García, A., Cáceres Campana, J.W. Identification of pathogens in corn using near-infrared UAV imagery and deep learning. Precision Agric 24, 783–806 (2023). https://doi.org/10.1007/s11119-022-09951-x
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DOI: https://doi.org/10.1007/s11119-022-09951-x