From Lab to Field: An Empirical Study on the Generalization of Convolutional Neural Networks towards Crop Disease Detection

Abstract

Due to complex feature abstraction and learning power, CNNs have been the most successful machine learning algorithms for image classification tasks. The objective of this work was to evaluate the potential of convolutional neural networks (CNNs) for extracting underlying complex features and recognize these patterns towards the task of detecting healthy and diseased crop plants. The generalization of these algorithms was assessed on different situations of training and testing scenarios using images from controlled lab conditions and real field environments. Results have shown that when presented with sufficient data variability in training, englobing images with similar conditions faced in testing, the deep learning architectures delivered accurate results of over 90%. In contrast, the same architectures were not able to generalize the accuracy of training towards the detection of new unseen images that were not extracted in the same settings as the ones from the training set, delivering, in this case, a general accuracy of around 50%. The deployment of practical automated support systems for disease detection depends on the provision of robust datasets for training CNNs which contemplate the spectral variability conditions found in numerous crop cultivation environments encountered in diverse field sites across the globe.