Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification
By J. G. Arnal Barbedo. Computers and Electronics in Agriculture, 2018
650 citations recorded by the source corpus.
Abstract The problem of automatic recognition of plant diseases has been historically based on conventional machine learning techniques such as Support Vector Machines, Multilayer Perceptron Neural Networks and Decision Trees. However, the prevailing approach has shifted to the application of deep learning concepts, with focus on Convolutional Neural Networks (CNNs). In general, this kind of technique requires large datasets containing a wide variety of conditions to work properly. This is an important limitation, given the many challenges involved in the construction of a suitable image database. In this context, this study investigates how the size and variety of the datasets impact the effectiveness of deep learning techniques applied to plant pathology. This investigation was based on an image database containing 12 plant species, each presenting very different characteristics in terms of number of samples, number of diseases and variety of conditions. Experimental results indicate that while the technical constraints linked to automatic plant disease classification have been largely overcome, the use of limited image datasets for training brings many undesirable consequences that still prevent the effective dissemination of this type of technology.
Subfields: Analytical Chemistry, Plant Science
View Paper: https://www.semanticscholar.org/paper/c4b9d3d0dcfceccb0e05a0110391cfeb016ef13d
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In Analytical Chemistry
In Plant Science
Citations in the corpus
- An advanced deep learning models-based plant disease detection: A review of recent research (2023, background): “In actual agricultural settings, the presence of background disturbances might make it harder to detect plant pests and diseases (Garcia and Barbedo, 2018).”
- Impact of Dataset Size on Classification Performance: An Empirical Evaluation in the Medical Domain (2021, result): “Similar studies to our work exist in the literature, where the main aim is to investigate the extent to which the size of the dataset can impact the classification performance in different domains such as sentiment classification [2,20], object detection [21], plant disease classification [22], and ”
- Deep Learning for Classification and Severity Estimation of Coffee Leaf Biotic Stress (2019, background): “Although there are initiatives that are using the concept of social networks to accelerate this process, this practice can lead to unreliable data (Barbedo, 2018a).”