Moisture content is an important index to assess the grain quality and food processing conditions. A measurement system based on the traveling–standing microwave attenuation method is designed for a fast and nondestructive grain moisture content determination. The proposed system consists of a microwave cavity oscillator, microwave transmitting and receiving horn antennas, microwave detector, slide rail, sample container, weight sensor, temperature sensor, and controller. The traveling–standing wave caused by free space microwave multiple reflection is discussed. The moisture content calibration functions eliminated the interference of bulk density and temperature are proposed based on the attenuation of the maximum field strength of the transmission traveling–standing wave. The moisture content of rice, which ranges from 10.75% to 27.62%, is obtained with a standard error of prediction (SEP) of 0.586% and a coefficient of determination ( R²) of 0.988, whereas the moisture content of corn, which ranges from 7.72% to 24.46%, is obtained with a SEP of 0.340% and R² of 0.991. The main results might provide technical support for the development of accurate and intelligent grain quality detection equipment.

Field weeds identification is challenging for precision spraying, i.e., the automation identification of the weeds from the crops. For rapidly obtaining weed distribution in field, this study developed a weed density detection method based on absolute feature corner point (AFCP) algorithm for the first time. For optimizing the AFCP algorithm, image preprocessing was firstly performed through a sub-module processing capable of segmenting and optimizing the field images. The AFCP algorithm improved Harris corner to extract corners of single crop and weed and then sub-absolute corner classifier as well as absolute corner classifier were proposed for absolute corners detection of crop rows. Then, the AFCP algorithm merged absolute corners to identify crop and weed position information. Meanwhile, the weed distribution was obtained based on two weed density parameters (weed pressure and cluster rate). At last, the AFCP algorithm was validated based on the images that were obtained using one typical digital camera mounted on the tractor in field. The results showed that the proposed weed detection method manifested well given its ability to process an image of 2748 × 576 pixels using 782 ms as well as its accuracy in identifying weeds reaching 90.3%. Such results indicated that the weed detection method based on AFCP algorithm met the requirements of practical weed management in field, including the real-time images computation processing and accuracy, which provided the theoretical base for the precision spraying operations.

Crop disease has been a severe issue for agriculture, causing economic loss for growers. Thus, disease identification urgently needs to be addressed, especially for precision agriculture. As of today, deep learning has been widely used for crop disease identification combined with optical imaging sensors. In this study, a lightweight convolutional neural network model is designed and validated on two publicly available imaging datasets and one self-built dataset with 28 types of leaf and leaf disease images of 6 crops as the research object. This model is an improvement of the existing convolutional neural network, reducing the floating-point operations by 65%. In addition, dilated depth-wise convolutions were used to increase the network receptive field and improve the model recognition accuracy without affecting the network computational speed. Meanwhile, two attention mechanisms are optimized to reduce attention module computation, improving the capability of the model to select the correct regions of interest. After training, this model achieved an average accuracy of 99.86%, and the image calculation speed was 0.173 s. Comparing with 11 backbone models and 5 latest crop leaf disease identification studies, the proposed model achieved the highest accuracy. Therefore, this model with an advantage of balancing between the calculation speed and recognition accuracy. Furthermore, the proposed model provides a theoretical basis and technical support for the practical application and mobile terminal applications of crop disease recognition in precision agriculture.

This study addresses the urgent need for the rapid, non-destructive assessment of key soybean components, including moisture, fat, and protein, using near-infrared (NIR) spectroscopy. This study provides technical and theoretical support for achieving the efficient and accurate detection of major soybean components and for the development of portable near-infrared (NIR) instruments. Thirty soybean samples from diverse sources were collected, and 360 spectral measurements were acquired using a 900–1700 nm NIR spectrometer after grinding and standardized sampling. To improve model robustness, preprocessing strategies such as standard normal variate (SNV), multiplicative scatter correction (MSC), and Savitzky–Golay derivatives were applied. Feature selection was conducted using competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and uninformative variable elimination (UVE), followed by model construction with partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF). Comparative analysis revealed that the RF model consistently outperformed the others across most combinations. Specifically, the SPASNV + D₁–RF combination achieved an RPD of 14.7 for moisture, CARS–SNV + D₁–RF reached 5.9 for protein, and CARS–SG + D₂–RF attained 12.0 for fat, all significantly surpassing alternative methods and demonstrating a strong nonlinear learning capacity and predictive precision. These findings show that integrating optimal preprocessing and feature selection strategies can markedly enhance the predictive accuracy in NIR-based soybean analyses. The RF model offers exceptional stability and performance, providing both technical reference and theoretical support for the development of portable NIR devices and practical rapid-quality assessment systems for soybeans in industrial applications.

Moisture content is an important indicator of grain storage safety, processing quality, and circulation efficiency. However, the performance of near-infrared spectroscopy (NIRS) models is affected by differences in grain crops, sample morphologies, preprocessing strategies, feature selection methods, and regression models, and the adaptation relationships among these factors remain insufficiently understood. In this study, soybean, maize, and wheat samples with whole-grain and powder morphologies were investigated to reveal the method adaptation patterns of NIRS-based moisture prediction under consistent experimental conditions. Spectra in the range of 900–1700 nm were collected, and 54 analytical pathways were established by combining six preprocessing strategies, three feature selection algorithms (SPA, CARS, and UVE), and three regression models (PLSR, SVR, and RF). The results showed that representative optimal pathways achieved Rp values above 0.9730 for powder samples and above 0.9587 for whole-grain samples. Different grain crops and sample morphologies exhibited distinct analytical pathway preferences, indicating that appropriate analytical strategies should be selected according to specific detection objects. Spectral variation analysis further demonstrated higher spectral variability in whole-grain samples than in powder samples. This study provides insights into the selection of suitable NIRS analytical strategies for grain moisture prediction and quality assessment.