Abstract
In the past years, several machine-learning-based techniques have arisen for providing effective crop protection. For instance, deep neural networks have been used to identify different types of weeds under different real-world conditions. However, these techniques usually require extensive involvement of experts working iteratively in the development of the most suitable machine learning system. To support this task and save resources, a new technique called Automated Machine Learning has started being studied. In this work, a complete open-source Automated Machine Learning system was evaluated with two different datasets, (i) The Early Crop Weeds dataset and (ii) the Plant Seedlings dataset, covering the weeds identification problem. Different configurations, such as the use of plant segmentation, the use of classifier ensembles instead of Softmax and training with noisy data, have been compared. The results showed promising performances of 93.8% and 90.74% F1 score depending on the dataset used. These performances were aligned with other related works in AutoML, but they are far from machine-learning-based systems manually fine-tuned by human experts. From these results, it can be concluded that finding a balance between manual expert work and Automated Machine Learning will be an interesting path to work in order to increase the efficiency in plant protection.
Highlights
Nowadays, the damage caused by weeds accounts for important global yield losses and is expected to increase in the coming years [1]
The performance of the Automated Machine Learning (AutoML) system was measured with the F1 score (Equation (1))
The results have shown that AutoML can provide classifiers with performances over
Summary
The damage caused by weeds accounts for important global yield losses and is expected to increase in the coming years [1]. Traditionally pesticides were homogeneously applied to solve this problem, there is a tendency in the EU policy to reduce the use of plant protection products since they can cause ground environmental pollution, chemical residues on the crops, and future drug resistance [2]. Convolution Neural Networks (CNNs) are currently the most popular technique in the agricultural domain since, theoretically, they can mitigate some challenges such as inter-class similarities within a plant family and large intra-class variations in background, occlusion, pose, color, and illumination. Besides their good classification performances, some of these works presented deep neural networks whose inference times are suitable for real-time agricultural weed control [11]
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