Seed purity is a key indicator of crop seed quality. The conventional methods for cultivar identification are time-consuming, expensive, and destructive. Fourier transform near-infrared (FT-NIR) spectroscopy combined with discriminant analyses, was studied as a rapid and nondestructive technique to classify the cultivars of sweet corn seeds. Spectra with a range of 1000–2500 nm collected from 760 seeds of two cultivars were used for the discriminant analyses. Thereafter, 126 feature wavelengths were identified from 1557 wavelengths using a genetic algorithm (GA) to build simplified classification models. Four classification algorithms, namely K-nearest neighbor (KNN), soft independent method of class analogy (SIMCA), partial least-squares discriminant analysis (PLS-DA), and support vector machine discriminant analysis (SVM-DA) were tested on full-range wavelengths and feature wavelengths, respectively. With the full-range wavelengths, all four algorithms achieved a high classification accuracy range from 97.56% to 99.59%, and the SVM-DA worked better than other models. From the feature wavelengths, no significant decline in accuracies was observed in most of the models and a high accuracy of 99.19% was still obtained by the PLS-DA model. This study demonstrated that using the FT-NIR technique with discriminant analyses could be a feasible way to classify sweet corn seed cultivars and the proper classification model could be embedded in seed sorting machinery to select high-purity seeds.

Plant architecture characteristics contribute significantly to the microclimate within peanut canopies, affecting weed suppression as well as incidence and severity of foliar and soil-borne diseases. However, plant canopy architecture is difficult to measure and describe quantitatively. In this study, a ground-based LiDAR sensor was used to scan rows of peanut plants in the field, and a data processing and analysis algorithm was developed to extract feature indices to describe the peanut canopy architecture. A data acquisition platform was constructed to carry the ground-based LiDAR and an RGB camera during field tests. An experimental field was established with three peanut cultivars at Oklahoma State University's Caddo Research Station in Fort Cobb, OK in May and the data collections were conducted once each month from July to September 2015. The ground-based LiDAR used for this research was a line-scan laser scanner with a scan-angle of 100°, an angle resolution of 0.25°, and a scanning speed of 53 ms. The collected line-scanned data were processed using the developed image processing algorithm. The canopy height, width, and shape/density were evaluated. Euler number, entropy, cluster count, and mean number of connected objects were extracted from the image and used to describe the shape of the peanut canopies. The three peanut cultivars were then classified using the shape features and indices. A high correlation was also observed between the LiDAR and ground-truth measurements for plant height. This approach should be useful for phenotyping peanut germplasm for canopy architecture.

Audible sound (20–20000 Hz) widely exists in natural world. However, the interaction between audible sound and the growth of plants is usually neglected in biophysics research. Not much effort has been put forth in studying the relation of plant and audible sound. In this work, the effect of audible sound on germination and growth of mung bean (Vigna radiate) was studied under laboratory condition. Audible sound ranging 1000–1500 Hz, 1500–2000 Hz, and 2000–2500 Hz and intensities [80 dB (A), 90 dB (A), 100 dB (A)] were used to stimulate mung bean for 72 hours. The growth of mung bean was evaluated in terms of mean germination time, total length, and total fresh weight. Experimental results indicated that the sound wave can reduce the germination period of mung bean and the mung bean under treatments of sound with intensity around 90 dB and frequency around 2000 Hz and significant increase in growth. Audible sound treatment can promote the growth of mung bean differently for distinct frequency and intensity. The study provides us with a way to understand the effects and rules of sound field on plant growth and a new way to improve the production of mung bean.

With recent developments allowing increased planter speeds, improving spatial uniformity of crop emergence will continue to be an important focus of research. Vertical vibration during planting usually interferes with the seed metering and delivery process, and thereby affects seeding quality. Two different row crop planters were instrumented with accelerometers to monitor vertical vibration of planter row units in five fields with a total area of 220 ha in both no-till and strip-tilled conditions. The test results showed that the row unit vibration linearly increased with planting speeds. The R² of each fitting equation was more than 0.90. The main frequencies of the vibration were concentrated in a low-frequency band of 3 Hz to 10 Hz. These frequencies did not show an increasing trend with the planter speed. However, the amplitude clearly increased when the planter speed increased. For the John Deere MaxEmerge™ 5 planter, the manually measured average plant spacing was close to the target plant spacing with a maximum error of 21 mm, and a max coefficient of variation (CV) of 24.3%; the standard deviation (SD) increased with travel speed and row unit vibration. For the John Deere ExactEmerge™ planter, the plant spacing was closer to the target plant spacing with a maximum error of only 5 mm and a max CV of 14.3%; the SD remained almost constant, at lower values than that of the MaxEmerge™ 5, at different speeds. For both planters, overall, the row unit vibration increased planting spatial variability. The quality of feed index decreased with increased vertical acceleration on the row unit while the miss index increased. However, the multiples index was generally unaffected by row unit vibration.

This review critically examines advancements in sensing, control, and networking technologies for agricultural robots (AgRobots) and their impact on modern farming. AgRobots—including Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs), Unmanned Surface Vehicles (USVs), and robotic arms—are increasingly adopted to address labour shortages, sustainability challenges, and rising food demand. This paper reviews sensing technologies such as cameras, LiDAR, and multispectral sensors for navigation, object detection, and environmental perception. Control approaches, from classical PID (Proportional-Integral-Derivative) to advanced nonlinear and learning-based methods, are analysed to ensure precision, adaptability, and stability in dynamic agricultural settings. Networking solutions, including ZigBee, LoRaWAN, 5G, and emerging 6G, are evaluated for enabling real-time communication, multi-robot coordination, and data management. Swarm robotics and hybrid decentralized architectures are highlighted for efficient collective operations. This review is based on the literature published between 2015 and 2025 to identify key trends, challenges, and future directions in AgRobots. While AgRobots promise enhanced productivity, reduced environmental impact, and sustainable practices, barriers such as high costs, complex field conditions, and regulatory limitations remain. This review is expected to provide a foundation for guiding research and development toward innovative, integrated solutions for global food security and sustainable agriculture.

Orchard target-oriented variable rate spraying is an effective method to reduce pesticide drift and excessive residues. To accomplish this task, the orchard targets’ characteristic information is needed to control liquid flow rate and airflow rate. One of the most important characteristics is the canopy density. In order to establish the canopy density model for a planar orchard target which is indispensable for canopy density calculation, a target density detection testing system was developed based on an ultrasonic sensor. A time-domain energy analysis method was employed to analyze the ultrasonic signal. Orthogonal regression central composite experiments were designed and conducted using man-made canopies of known density with three or four layers of leaves. Two model equations were obtained, of which the model for the canopies with four layers was found to be the most reliable. A verification test was conducted with different layers at the same density values and detecting distances. The test results showed that the relative errors of model density values and actual values of five, four, three and two layers of leaves were acceptable, while the maximum relative errors were 17.68%, 25.64%, 21.33% and 29.92%, respectively. It also suggested the model equation with four layers had a good applicability with different layers which increased with adjacent layers.