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
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.

Abstract
Accurate estimation of soil moisture is vital for sustainable water management in agriculture, particularly in olive orchards where precise irrigation strategies are crucial for maintaining productivity and crop quality. Climate change intensifies water scarcity, intensifying the need for advanced methodologies to optimize agricultural water use. Remote sensing technologies, such as Synthetic Aperture Radar (SAR), have emerged as promising tools for monitoring soil moisture over large areas. When combined with in situ measurements and data-driven models like Artificial Neural Networks (ANNs), these technologies offer scalable solutions for addressing the challenges of soil moisture estimation in heterogeneous agricultural landscapes.This study integrates Sentinel-1 SAR data with ANN models to estimate soil moisture in olive orchards located in the Vilariça Valley, northeastern Portugal. Soil moisture measurements were recorded at a depth of 10 cm every 30 minutes from July 2020 to December 2021. Sentinel-1 SAR images were acquired in dual polarizations (VV and VH), and synthetic bands were generated through arithmetic operations combining polarization and calibration metrics (Beta, Sigma, Gamma, Gamma TF), yielding 24 features per image. Two datasets were constructed to evaluate the impact of orbit geometry: (1) D1, containing 161 images from ascending orbits, and (2) D2, comprising 246 images from ascending and descending orbits.The ANN regression model, comprising six hidden layers and K-fold cross-validation (20 splits), demonstrated greater performance with the D1 dataset, achieving a Root Mean Square Error (RMSE) of 2.78, a coefficient of determination (R²) of 0.69, and a Mean Absolute Percentage Error (MAPE) of 8.26%. In contrast, the D2 dataset showed reduced accuracy (RMSE: 3.96, R²: 0.59, MAPE: 12.41%), likely due to variability introduced by combining ascending and descending orbits. These findings underscore the importance of dataset homogeneity in SAR-based soil moisture modeling.This study highlights the potential of integrating Sentinel-1 SAR data with ANN models for soil moisture estimation in olive orchards, contributing to the development of sustainable agricultural practices. Future work should focus on addressing dataset imbalances by expanding the range of observed conditions, incorporating topographic features, and exploring advanced data augmentation techniques to enhance model robustness and scalability. AcknowledgmentsThis work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project LA/P/0063/2020. DOI 10.54499/LA/P/0063/2020 https://doi.org/10.54499/LA/P/0063/2020 and a doctoral scholarship in a non-academic environment at Fundação Côa Parque (PRT/BD/154871/2023).