Abstract
Globally, insect pests are the primary reason for reduced crop yield and quality. Although pesticides are commonly used to control and eliminate these pests, they can have adverse effects on the environment, human health, and natural resources. As an alternative, integrated pest management has been devised to enhance insect pest control, decrease the excessive use of pesticides, and enhance the output and quality of crops. With the improvements in artificial intelligence technologies, several applications have emerged in the agricultural context, including automatic detection, monitoring, and identification of insects. The purpose of this article is to outline the leading techniques for the automated detection of insects, highlighting the most successful approaches and methodologies while also drawing attention to the remaining challenges and gaps in this area. The aim is to furnish the reader with an overview of the major developments in this field. This study analysed 92 studies published between 2016 and 2022 on the automatic detection of insects in traps using deep learning techniques. The search was conducted on six electronic databases, and 36 articles met the inclusion criteria. The inclusion criteria were studies that applied deep learning techniques for insect classification, counting, and detection, written in English. The selection process involved analysing the title, keywords, and abstract of each study, resulting in the exclusion of 33 articles. The remaining 36 articles included 12 for the classification task and 24 for the detection task. Two main approaches-standard and adaptable-for insect detection were identified, with various architectures and detectors. The accuracy of the classification was found to be most influenced by dataset size, while detection was significantly affected by the number of classes and dataset size. The study also highlights two challenges and recommendations, namely, dataset characteristics (such as unbalanced classes and incomplete annotation) and methodologies (such as the limitations of algorithms for small objects and the lack of information about small insects). To overcome these challenges, further research is recommended to improve insect pest management practices. This research should focus on addressing the limitations and challenges identified in this article to ensure more effective insect pest management.

Abstract

Abstract
In recent years, the use of remote sensing data obtained from satellite or unmanned aerial vehicle (UAV) imagery has grown in popularity for crop classification tasks such as yield prediction, soil classification or crop mapping. The ready availability of information, with improved temporal, radiometric, and spatial resolution, has resulted in the accumulation of vast amounts of data. Meeting the demands of analysing this data requires innovative solutions, and artificial intelligence techniques offer the necessary support. This systematic review aims to evaluate the effectiveness of deep learning techniques for crop classification using remote sensing data from aerial imagery. The reviewed papers focus on a variety of deep learning architectures, including convolutional neural networks (CNNs), long short-term memory networks, transformers, and hybrid CNN-recurrent neural network models, and incorporate techniques such as data augmentation, transfer learning, and multimodal fusion to improve model performance. The review analyses the use of these techniques to boost crop classification accuracy by developing new deep learning architectures or by combining various types of remote sensing data. Additionally, it assesses the impact of factors like spatial and spectral resolution, image annotation, and sample quality on crop classification. Ensembling models or integrating multiple data sources tends to enhance the classification accuracy of deep learning models. Satellite imagery is the most commonly used data source due to its accessibility and typically free availability. The study highlights the requirement for large amounts of training data and the incorporation of non-crop classes to enhance accuracy and provide valuable insights into the current state of deep learning models and datasets for crop classification tasks.

Abstract
Non-native plant species can have a negative impact in the ecosystems and in local economies when they spread uncontrollably. Monitoring tools can support their management and spread. In this paper, an exploratory approach is presented for pixelwise detection of Acacia dealbata from UAV-based imagery acquired from RGB and multispectral sensors. Four machine learning algorithms-k-nearest neighbors (KNN), random forest (RF), adaptive boosting (AdaBoost) and a linear kernel SVM (LSVM)-Are trained using four datasets (hue, saturation and value-HSV, multispectral-MSP, RGB and a combination of all features) and their classification performance is evaluated. RF classifier obtained the overall best performance, with an accuracy above 86% in all data combinations, with LSVM showing the poorer results. Obtained results are encouraging for monitoring invasive species and can serve as a base for future improvements to detect invasive species.

Abstract
Insect pests cause significant damage to agricultural production. Smart pest monitoring enables the automatic detection and identification of pests using artificial intelligence techniques. The automatic detection of pests is an important tool to help the farmer decide on the application of pesticides. Several studies were carried out to develop deep learning methods for detecting insect pests. However, it is still an open problem, as there are a scarcity and data features that do not allow the good performance of a deep learning method. Pest24 is a public dataset with great diversity and variability of insects, but it has a low detection rate. To improve detection performance in Pest24, this work proposes a method of automatic detection of insects using deep learning. Two experiments were carried out, applying the YOLOv5 with standard hyperparameters and the hyperparameter tuning obtained by the evolution algorithm. As a result, we obtained a performance superior to that reported in state of the art, with the YOLOv5 method with standard hyperparameters, with an mAP of 72.1%.

Abstract
The bedbug and the grape moth are the most significant pests affecting rice and vineyards, causing great damage. However, these pests are only two examples of the many insect pests that exist with great potential to cause significant crop damage. Insect traps are among the most appropriate solution for monitoring and counting, influencing the selection and dosage of the pesticide to be applied for pest control. However, the counting and monitoring operations are based on the frequent visit of technicians to the site and are supported by inefficient counting methods, which is a challenging and time-consuming task. This study proposes the automatic counting of bedbugs and grape moths in traps using deep learning algorithms. We use three different databases, Pest24, Bedbug and Grape moth. Pest24 is a public dataset with a great diversity of insects. The Bedbugs and the Grape moth datasets are private datasets provided by mySense, a precision agriculture platform developed and managed by researchers from the University of Tras-os-Montes e Alto Douro (UTAD). First, we trained the Pest24 dataset with YOLOv5, and we got an mAP of 69.3%. Then, using the weights obtained from the Pest24 dataset, we trained the Bedbug and Grape moth datasets. The best results for the bedbug dataset were obtained with the YOLOv5 with transfer learning with an AP of 96.5% and a counting error of 63.3%. The best result was obtained with YOLOv5 without transfer learning of Pest24 with an AP of 90.9% and a counting error of 6.7 for the Grape moth.