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Four CoFe2O4/Pb(Zr0.52Ti0.48)O3 (CFO/PZT) bilayered thin films with increasing PZT constituent layer thicknesses have been fabricated in order to investigate the optimized PZT layer thickness and the thickness effect on the magnetic and ferroelectric behaviors of the heterolayered multiferroic thin films. These heterostructured thin films were fabricated via a combined route of spin coating and rf sputtering. Both magnetic CFO and ferroelectric PZT phases were successfully retained in the heterostructured thin film. The improved saturation magnetization (Ms) of the CFO/PZT thin films has been observed and the enhancement increases with increasing PZT layer thickness. Both ferroelectric and dielectric measurements suggest the presence of interfacial space charge polarization arising from the ferroelectric/magnetic interface. Among the multiferroic thin films containing different PZT layer thicknesses fabricated, the thin film that consisted of six PZT constituent layers demonstrated the highest Ms value of 397 emu/cm3 and the best ferroelectric property with Pr and Ec of 29.91 μC/cm2 and 138.67 kV/cm, respectively, at 500 kV/cm, together with εr and tan δ values of 116.420 and 0.717, respectively, at 100 kHz. Investigation into its magnetoelectric effect demonstrated an αE,33 of 238 mV/cm Oe, which was measured by superimposing an ac magnetic field of 5 Oe at 5 kHz onto a dc magnetic field of 4.4 kOe.

Phenotyping of plant growth improves the understanding of complex genetic traits and eventually expedites the development of modern breeding and intelligent agriculture. In phenotyping, segmentation of 3D point clouds of plant organs such as leaves and stems contributes to automatic growth monitoring and reflects the extent of stress received by the plant. In this work, we first proposed the Voxelized Farthest Point Sampling (VFPS), a novel point cloud downsampling strategy, to prepare our plant dataset for training of deep neural networks. Then, a deep learning network—PSegNet, was specially designed for segmenting point clouds of several species of plants. The effectiveness of PSegNet originates from three new modules including the Double-Neighborhood Feature Extraction Block (DNFEB), the Double-Granularity Feature Fusion Module (DGFFM), and the Attention Module (AM). After training on the plant dataset prepared with VFPS, the network can simultaneously realize the semantic segmentation and the leaf instance segmentation for three plant species. Comparing to several mainstream networks such as PointNet++, ASIS, SGPN, and PlantNet, the PSegNet obtained the best segmentation results quantitatively and qualitatively. In semantic segmentation, PSegNet achieved 95.23%, 93.85%, 94.52%, and 89.90% for the mean Prec, Rec, F1, and IoU, respectively. In instance segmentation, PSegNet achieved 88.13%, 79.28%, 83.35%, and 89.54% for the mPrec, mRec, mCov, and mWCov, respectively.

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