Multi-robot collision avoidance method in sweet potato fields.

Currently, precise spraying of sweet potatoes is mainly accomplished through semi-mechanized or single spraying robots, which results in low operating efficiency. Moreover, it is time-consuming and labor-intensive, and the pests and diseases cannot be eliminated in time. Based on multi robot navigation technology, multiple robots can work simultaneously, improving work efficiency.

Spectral intelligent detection for aflatoxin B1 via contrastive learning based on Siamese network.

Aflatoxins, harmful substances found in peanuts, corn, and their derivatives, pose significant health risks. Addressing this, the presented research introduces an innovative MSGhostDNN model, merging contrastive learning with multi-scale convolutional networks for precise aflatoxin detection. The method significantly enhances feature discrimination, achieving an impressive 97.

A comb-brushing-type green soybean pod harvesting equipment: Design and experiment.

To solve the problem of low efficiency of manual harvesting of green soybeans and lack of adaptable harvesters, in this study, a brushing-type green soybean harvester was designed. The comb-brushing type green soybean pod harvesting equipment is composed of a front-mounted separation drum, a full-width material delivery mechanism, a negative pressure cleaning system, and a stalk-pod separation system. Based on the operation requirements of the front-mounted brushing-type detachment drum, the drum parameters, parameters of comb arrangement, and structural parameters of the comb, the force analysis in detachment was performed.

Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning.

Seed quality affects crop yield and the quality of agricultural products, and traditional identification methods are time-consuming, complex, and irreversibly destructive. This study aims to establish a fast, non-destructive, and effective approach for defect detection in maize seeds based on hyperspectral imaging (HSI) technology combined with deep learning. Raw spectra collected from maize seeds (200 each healthy and worm-eaten) were pre-processed using detrending (DE) and multiple scattering correction (MSC) to highlight the spectral differences between samples.