Читать книгу The Digital Agricultural Revolution - Группа авторов - Страница 29

Images make up a significant portion of the data collected by remote sensing. Images can provide a complete view of agricultural landscapes in many circumstances, and they can help with a range of problems. As a result, imaging analysis is an important research field in the agricultural realm, and picture identification/classification is done using intelligent data analysis approaches [33]. One such approach is DL. A deep neural network is a network that has numerous hidden layers, each of which refines the preceding layer’s output. Feature learning, or the automatic extraction of features from raw data, is a key advantage of DL. This architecture finds its applications in the computer vision field for image classification, object identification, picture segmentation, and so on.

Because of the more complicated models utilized in DL, which allow huge parallelization, it can tackle more complicated problems exceptionally well and quickly [34]. Many researchers used DL for fruit counting, predicting future parameters, such as yield production, soil moisture content, evapotranspiration, weed detection, weather prediction, and so on.