Article Testing the Suitability of Automated Machine Learning for Weeds Identification Borja Espejo-Garcia *, Ioannis Malounas, Eleanna Vali and Spyros Fountas   Citation: Espejo-Garcia, B.; Malounas, I.; Vali, E.; Fountas, S. Testing the Suitability of Automated Machine Learning for Weeds Identification. AI 2021, 2, 34–47. https://doi.org/10.3390/ai2010004 Academic Editor: Amir Mosavi Received: 28 December 2020 Accepted: 3 February 2021 Published: 9 February 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Agricultural University of Athens, 11855 Athens, Greece; gmalounas@aua.gr (I.M.); eleannaval1996@gmail.com (E.V.); sfountas@aua.gr (S.F.) * Correspondence: borjaeg@aua.gr 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% F 1 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. Keywords: automated machine learning; AutoML; weeds identification; deep learning; precision agriculture 1. Introduction Nowadays, the damage caused by weeds accounts for important global yield losses and is expected to increase in the coming years [1]. Although 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]. More specifically, the EU has set a target to reduce pesticide use by 50% in the next 10 years [3]. Currently, for applying less dosage of chemical herbicides to weed targets, automatic weed control arises as a possible solution [4–6]. Recent advances in image classification techniques provide an opportunity for the improvement of automatic weed control. Despite the delay in the introduction of such techniques to the agricultural domain, the pace that such technologies are being adopted is extremely fast. The use of machine-learning-based image analysis presents a relatively quick, non-invasive, and non-destructive way of controlling weeds spread. In agriculture, deep learning models have been used in the detection of plant diseases and weeds iden- tification [7–10]. 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]. AI 2021, 2, 34–47. https://doi.org/10.3390/ai2010004 https://www.mdpi.com/journal/ai