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 …

With increasing population, the demand for agricultural productivity is rising to meet the goal
of “Zero Hunger”. Consequently, farmers have optimized the agricultural activities in a …

In the complex environment of an orchard, changes in illumination, leaf occlusion, and fruit
overlap make it challenging for mobile picking robots to detect and locate oil-seed camellia …

The key elements that underpin food security require the adaptation of agricultural systems
to support productivity increases while minimizing inputs and the adverse effects of climate …

During the last few years, Unmanned Aerial Vehicles (UAVs) technologies are widely used
to improve agriculture productivity while reducing drudgery, inspection time, and crop …

The demand for intelligent agriculture is increasing due to the continuous impact of world
food and environmental crises. Focusing on fruit detection, with the rapid development of …

Deep learning, which has exhibited considerable potential and effectiveness in forest
resource assessment, is vital for comprehending and managing forest resources and …

Fruit loading and packaging are still labor-intensive tasks during postharvest
commercialization, of which the key issues is to realize the real-time detection and …

The" You only look once v4"(YOLOv4) is one type of object detection methods in deep
learning. YOLOv4-tiny is proposed based on YOLOv4 to simple the network structure and …

Blockchain quickly became an important technology in many applications of precision
agriculture discipline. The need to develop smart P2P systems capable of verifying …