Since plug seedling plays a key role in automatic transplanting, this work aimed to explore the effect biochar has on the root growth of plug seedlings. The physicochemical properties tests showed that the addition of biochar in the peats could increase the porosity, pH, and EC values of the substrate, and the substrates treated with 0%, 10%, 20%, and 30% biochar could meet the requirements of seedling raising. The water retention of the substrate was superior with the increase of biochar proportion, and the nitrogen release significantly decreased with the increase of the biochar proportion. Our results demonstrated that the substrate with 10% biochar-treated apparently promoted the growth of seedlings and root systems, even the length of the root-tip cells. However, the substrates with 40% and 50% biochar-treated obviously inhibited the growth of seedlings and root systems. It was noticed that the strength of substrate with appropriate biochar proportion was enhanced, as well. Under the interaction of strong root system and solid substrate, the compressive strength of the substrate with 20% and 10% biochar-treated was much better than others, especially that of 40% and 50% biochar-treated, which efficiently satisfied the requirements of automatic seedling picking. The biochar may have a good application prospect in seedling raising.

The operational performance of cereal seeding machinery influences the yield and quality of cereals. In this article, we review the existing literature on intelligent technologies for cereal seeding machinery, encompassing active controllable seeding actuators, intelligent seeding rate control, and intelligent seed position control systems. In this manuscript, (1) the characteristics and innovative structures of existing motor-driven seed-metering devices and ground surface profiling mechanisms are expounded; (2) state-of-the-art detection principles and applications for soil property sensors are described based on different soil properties; (3) optimal seeding rate decision approaches based on soil properties are summarized; (4) the research state of seeding rate measuring and control technologies is expounded in detail; (5) trajectory control methods for seeding machinery and seeding depth control systems are described based on measurement and control principles; and (6) the present state, limitations, and future development directions of intelligent cereal seeding machinery are described. In the future, more advanced multi-algorithm and multi-sensor fusion technologies for soil property detection, optimal seeding rate decisions, seeding rates, and seed position control are likely to evolve. This review not only expounds the latest studies on intelligent actuating, sensing, and control technologies for intelligent cereal seeding machinery, but also discusses the shortcomings of existing intelligent seeding technologies and future developing trends in detail. This review, therefore, offers a reference for future research in the domain of intelligent seeding machinery for cereals.

Robotically selective broccoli harvesting requires precise lightweight detection models to efficiently detect broccoli heads. Therefore, this study introduces a lightweight and high-precision detection model named LBDC-YOLO (Lightweight Broccoli Detection in Complex Environment—You Look Only Once), based on the improved YOLOv8 (You Look Only Once, Version 8). The model incorporates the Slim-neck design paradigm based on GSConv to reduce computational complexity. Furthermore, Triplet Attention is integrated into the backbone network to capture cross-dimensional interactions between spatial and channel dimensions, enhancing the model’s feature extraction capability under multiple interfering factors. The original neck network structure is replaced with a BiFPN (Bidirectional Feature Pyramid Network), optimizing the cross-layer connection structure, and employing weighted fusion methods for better integration of multi-scale features. The model undergoes training and testing on a dataset constructed in real field conditions, featuring broccoli images under various influencing factors. Experimental results demonstrate that LBDC-YOLO achieves an average detection accuracy of 94.44% for broccoli. Compared to the original YOLOv8n, LBDC-YOLO achieves a 32.1% reduction in computational complexity, a 47.8% decrease in parameters, a 44.4% reduction in model size, and a 0.47 percentage point accuracy improvement. When compared to models such as YOLOv5n, YOLOv5s, and YOLOv7-tiny, LBDC-YOLO exhibits higher detection accuracy and lower computational complexity, presenting clear advantages for broccoli detection tasks in complex field environments. The results of this study provide an accurate and lightweight method for the detection of broccoli heads in complex field environments. This work aims to inspire further research in precision agriculture and to advance knowledge in model-assisted agricultural practices.

Automating vegetable seedling transplanting has led to labor-saving opportunities and improved productivity. Some changes in seedling agronomy are necessary for efficient transplanting. In this study, the local nursery substrates were added with the herbaceous peat, the sphagnum peat, and the coir peat, respectively. Effects of the new compound substrates were investigated on the seedling growth qualities and the root substrate strength. In the results, we found that the addition of three compound mediums significantly affected the physiochemical properties of the original substrates. Under the same conditions of cultivating seedlings, appropriate additions of new compounds promoted the seedling growth. Moreover, deficient or excessive additions inhibited the growing development of seedlings and their roots. The corresponding additions also improved the structural characteristics of the root lumps. Compared with the two other compounds, the nursery substrates added with the sphagnum peat were optimized in contribution to the seedling qualities and the root substrate strengths. As the local substrate and the sphagnum peat were mixed at a volume ratio of 2:1, the dry matter accumulation of seedlings was 2.18 times more than the original. Their root lumps had the best consolidation strength. This new compound of substrates may be an effective application for the necessary qualities of seedlings for automatic transplanting.

Highly sensitive detection of daminozide using terahertz metamaterial sensor

Airborne fungal spores have always played an important role in the spread of fungal crop diseases, causing great concern. The traditional microscopic spore classification method mainly relies on naked eye observations and classification by professional and technical personnel in a laboratory. Due to the large number of spores captured, this method is labor-intensive, time-consuming, and inefficient, and sometimes leads to huge errors. Thus, an alternative method is required. In this study, a method was proposed to identify airborne disease spores from greenhouse crops using digital image processing. First, in an indoor simulation, images of airborne disease spores from three greenhouse crops were collected using portable volumetric spore traps. Then, a series of image preprocessing methods were used to identify the spores, including mean filtering, Gaussian filtering, OTSU (maximum between-class variance) method binarization, morphological operations, and mask operations. After image preprocessing, 90 features of the spores were extracted, including color, shape, and texture features. Based on these features, logistics regression (LR), K nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) classification models were built. The test results showed that the average accuracy rates for the 3 classes of disease spores using the SVM model, LR model, KNN model, and RF model were 94.36%, 90.13%, 89.37%, and 89.23%, respectively. The harmonic average of the accuracy and the recall rate value (F value) were higher for the SVM model and its overall average value reached 91.68%, which was 2.03, 3.59, and 3.96 percentage points higher than the LR model, KNN model, and RF model, respectively. Therefore, this method can effectively identify 3 classes of diseases spores and this study can provide a reference for the identification of greenhouse disease spores.