We propose an asymmetric optical image encryption scheme with silhouette removal by using interference and equal modulus decomposition (EMD). Plaintext is first separated into two complex value masks with the same modulus using EMD in the Fresnel transform domain. The two masks are encoded into four phase-only masks (POMs), two of which are treated as ciphertexts and other two as plaintext-dependent private keys by using the inverse Fresnel transform with different diffraction distances and interference-based encryption. Any information about the plaintext, including its silhouette, cannot be retrieved using one, two, or even three of the four POMs. Our scheme also avoids the constraint of the same modulus in EMD and eliminates the vulnerability against the iterative amplitude-phase attack and advanced iterative amplitude-phase attack. Numerical simulations were used to verify the validity and security of our proposed method.

To tackle the issues of low seeding accuracy and seed injury caused by the seeders utilized at a small scale and in the plot seeding of sorghum in mountainous or hilly regions, this study presents the design of an oil–electric hybrid air suction sorghum plot seeder. The main working parts of the seeder are described, and the performance of the seed-mixing device is simulated using EDEM software. An oil–electric hybrid drive mode is used to provide power for operation and to the seed-metering device and fan. Additionally, a sowing control and monitoring system is designed using a single-chip microcomputer controller to ensure uniform plant spacing at different forward speeds. A multi-factor experiment is conducted using the central synthesis method to determine the optimal operating parameters of the seed-metering device through bench tests. The results show that a profile hole diameter of 2.5 mm on the seed tray, a negative-pressure chamber vacuum of 8.0 kPa, and a seed-metering device speed of 28 r/min result in a 95.95% pass rate, 0.5% missing rate, and 3.55% reseeding rate. The deviation between the experimental and analytical results that validate the optimum parameters is kept within acceptable limits. Field tests are conducted at different forward speeds using the optimum parameter combinations, and a comparison is made with the widely used duckbill planter. The results show pass, missing, and reseeding rates of 94.41%, 2.3%, and 3.29%, respectively. The missing monitoring error is less than 7.19%. All of the indices of the oil–electric hybrid air suction sorghum plot seeder are superior to those of the duckbill planter; thus, it fulfills the agronomic requirements for seeding a sorghum plot.

This study proposes an intelligent agricultural pest monitoring system that integrates mechanical control with deep learning to address issues in traditional systems, such as pest accumulation interference, image contrast degradation under complex lighting, and poor balance between model accuracy and real-time performance. A three-axis coordinated separation device is employed, achieving a 92.41% single-attempt separation rate and 98.12% after three retries. Image preprocessing combines the Multi-Scale Retinex with Color Preservation (MSRCP) algorithm and bilateral filtering to enhance illumination correction and reduce noise. For overlapping pest detection, EfficientNetv2-S replaces the YOLOv5s backbone and is combined with an Adaptive Feature Pyramid Network (AFPN), achieving 95.72% detection accuracy, 94.04% mAP, and 127 FPS. For pest species recognition, the model incorporates a Squeeze-and-Excitation (SE) attention module and α-CIoU loss function, reaching 91.30% precision on 3428 field images. Deployed on an NVIDIA Jetson Nano, the system demonstrates a detection time of 0.3 s, 89.64% recall, 86.78% precision, and 1.136 s image transmission delay, offering a reliable solution for real-time pest monitoring in complex field environments.