SGSNet: a lightweight deep learning model for strawberry growth stage detection.

Introduction: Detecting strawberry growth stages is crucial for optimizing production management. Precise monitoring enables farmers to adjust management strategies based on the specific growth needs of strawberries, thereby improving yield and quality. However, dense planting patterns and complex environments within greenhouses present challenges for accurately detecting growth stages.

Simultaneous measurement of energy spectrum and fluence of neutrons using a diamond detector.

Due to the excellent radiation hardness and high-temperature endurance, diamond detectors are suitable for intense neutron measurements and promising for neutron diagnostics of scientific fusion devices. In the present work, simultaneous measurement of energy spectrum and fluence of neutrons using a diamond detector was realized for the first time. The absolute response matrix of the diamond detector was simulated based on detailed analysis of the nuclear reactions and the proper selection of nuclear reaction data.