This review explores the use of machine learning (ML) techniques for detecting pests and diseases in crops, which is a significant challenge in agriculture, leading to substantial yield losses worldwide. This study focuses on the integration of ML models, particularly Convolutional Neural Networks (CNNs), which have shown promise in accurately identifying and classifying plant diseases from images. By analyzing studies published from 2019 to 2024, this work summarizes the common methodologies involving stages of data acquisition, preprocessing, segmentation, feature extraction, and prediction to develop robust ML models. The findings indicate that the incorporation of advanced image processing and ML algorithms significantly enhances disease detection capabilities, leading to the early and precise diagnosis of crop ailments. This can not only improve crop yield and quality but also reduce the dependency on chemical pesticides, contributing to more sustainable agricultural practices. Future research should focus on enhancing the robustness of these models to varying environmental conditions and expanding the datasets to include a wider variety of crops and diseases. CNN-based models, particularly specialized architectures like ResNet, are the most widely used in the studies reviewed, making up 42.36% of all models, with ResNet alone contributing 7.65%. This highlights ResNet’s appeal for tasks that demand deep architectures and sophisticated feature extraction. Additionally, SVM models account for 9.41% of the models examined. The prominence of both ResNet and MobileNet reflects a trend toward architectures with residual connections for deeper networks, alongside efficiency-focused designs like MobileNet, which are well-suited for mobile and edge applications.

This study presents a comparative analysis of YOLO detection models for the accurate identification of bean leaf diseases caused by Coleoptera pests in natural environments. By using a manually collected dataset of healthy and infected bean leaves in natural conditions, we labeled at the leaf level and evaluated the performance of the YOLOv5, YOLOv8, YOLOv9, YOLOv10, and YOLOv11 models. Mean average precision (mAP) was used to assess the performance of the models. Among these, YOLOv9e exhibited the best performance, effectively balancing precision and recall for datasets with limited size and variability. In addition, we integrated the Sophia optimizer and PolyLoss function into YOLOv9e and enhanced it, providing even more accurate detection results. This paper highlights the potential of advanced deep learning models, optimized with second-order optimizers and custom loss functions, in improving pest detection, crop management, and overall agricultural yield.

Three-dimensional (3D) reconstruction from images has significantly advanced due to recent developments in deep learning, yet methodological variations and diverse application contexts pose ongoing challenges. This systematic review examines the state-of-the-art deep learning techniques employed for image-based 3D reconstruction from 2019 to 2025. Through an extensive analysis of peer-reviewed studies, predominant methodologies, performance metrics, sensor types, and application domains are identified and assessed. Results indicate multi-view stereo and monocular depth estimation as prevailing methods, while hybrid architectures integrating classical and deep learning techniques demonstrate enhanced performance, especially in complex scenarios. Critical challenges remain, particularly in handling occlusions, low-texture areas, and varying lighting conditions, highlighting the importance of developing robust, adaptable models. Principal conclusions highlight the efficacy of integrated quantitative and qualitative evaluations, the advantages of hybrid methods, and the pressing need for computationally efficient and generalizable solutions suitable for real-world applications.