Abstract: In agricultural production and breeding, implementing automatic picking robot in orchard farming to reduce human labour and error is challenging. The core function of it is automatic identification based on machine vision. This paper focuses on apple detection and counting in orchards and implements several deep learning methods. Extensive datasets are used and a semi-automatic annotation method is proposed. The proposed deep learning models are in state-of-the-art YOLO family. In view of the essence of the models with various backbones, a multi-dimensional comparison in details is made in terms of counting accuracy, mAP and model memory, laying the foundation for realising automatic precision agriculture.
Abstract: The layered structure LiNi1/3Co1/3Mn1/3-xAlxO2 (x = 0 ~
0.04) series cathode materials were synthesized by a carbonate
co-precipitation method, followed by a high temperature calcination
process. The influence of Al substitution on the microstructure and
electrochemical performances of the prepared materials was
investigated by X-Ray diffraction (XRD), scanning electron
microscopy (SEM), and galvanostatic charge/discharge test. The
results show that the LiNi1/3Co1/3Mn1/3-xAlxO2 has a well-ordered
hexagonal α-NaFeO2 structure. Although the discharge capacity of
Al-doped samples decreases as x increases,
LiNi1/3Co1/3Mn1/3-0.02Al0.02O2 exhibits superior capacity retention at
high voltage (4.6 V). Therefore, LiNi1/3Co1/3Mn1/3-0.02Al0.02O2 is a
promising material for “green” vehicles.