The rapid advances in Deep Learning (DL) techniques have enabled rapid detection,
localisation, and recognition of objects from images or videos. DL techniques are now being …

Deep Learning (DL) has been described as one of the key subfields of Artificial Intelligence
(AI) that is transforming weed detection for site-specific weed management (SSWM). In the …

Deep learning (DL) is a robust data-analysis and image-processing technique that has
shown great promise in the agricultural sector. In this study, 129 papers that are based on …

Weeds are one of the most important factors affecting agricultural production. The waste and
pollution of farmland ecological environment caused by full-coverage chemical herbicide …

Fast and accurate plant disease detection is critical to increasing agricultural productivity in
a sustainable way. Traditionally, human experts have been relied upon to diagnose …

Weed identification in vegetable plantation is more challenging than crop weed identification
due to their random plant spacing. So far, little work has been found on identifying weeds in …

Improvement of deep learning algorithms in smart agriculture is important to support the
early detection of plant diseases, thereby improving crop yields. Data acquisition for …

Classification of land use and land cover from remote sensing images has been widely used
in natural resources and urban information management. The variability and complex …

Abstract In this study, Deep Learning (DL) was used to detect powdery mildew (PM),
persistent fungal disease in strawberries to reduce the amount of unnecessary fungicide …

Automation, including machine learning technologies, are becoming increasingly crucial in
agriculture to increase productivity. Machine vision is one of the most popular parts of …