Machine learning approaches often involve evaluating a wide range of models due to various available architectures. This standard strategy can lead to a lack of depth in exploring established methods.

Proposed innovative approach for robust surveillance and monitoring of Vicia faba crops. Developed a novel deep-learning model based on neural architecture search to identify growth stages.

Researchers have made significant progress in the field of artificial intelligence (AI) by applying deep learning techniques to automate the detection and classification of crop leaf diseases.

The research highlights the potential of deep learning methods to enhance the efficiency of field plant phenotyping, offering valuable insights for future crop breeding and management.

Deep learning techniques, especially convolutional neural networks (CNNs) and YOLOv8 algorithms, have emerged as highly accurate and efficient tools for image-based pest and disease detection. Using ...

This paper focuses on this issue and provides an application for the detection and classification of diseases in maize crop using deep learning models. In addition to this, the developed application ...

Again for fruit detection in an orchard, Zhang et al. (2019) used a multi-task cascaded convolutional network and showed that network fusion had benefits for detection, however, the cascaded network ...