Abstract:The current detection technology for vegetable pests mainly relies on artificial statistics, which exists many shortages such as requiring a large amount of labor, low efficiency, feedback delay and artificial faults. By rapid detection and image processing technology targeting at vegetable pests, not only can reduce manpower and pesticide use, but also provide decision support for precise spraying and improve the quality of vegetables. Practical research achievements are still relatively lacking on the rapid identification technology based on image processing technology in vegetable pests. Given the above background, this paper presents a classification and recognition scheme based on the bag-of-words model and support vector machine (BOF-SVM) on four important southern vegetable pests including Whiteflies, Phyllotreta Striolata, Plutella Xylostella and Thrips. This paper consists of four sub-algorithms. The first sub-algorithm is to compute the character description of pest images based on scale-invariant feature transformation. The second sub-algorithm is to compute the visual vocabulary based on bag of features. The third sub-algorithm is to compute the classifier of pests based on support vector machines. The last one is to classify the pest images using the classifier. In this study, C++ and Python language were used as implementation technologies with OpenCV and LibSVM function library based on BOF-SVM classification algorithm. Experiments showed that the average recognition accuracy was 91.56% for a single image category judgment with 80 images from the real environment, and the average time was 0.39 seconds. This algorithm has achieved the ideal operating speed and precision. It can provide decision support for UAV precise spraying, and also has good application prospect in agriculture.

The development of botanical applications of nanomaterials has produced a new generation of technologies that can profoundly impact botanical research. Semiconductor quantum dots (QDs) are an archetype nanomaterial and have received significant interest from diverse research communities, owing to their unique and optimizable optical properties. In this review, we describe the most recent progress on QD-based botanical research and discuss the uptake, translocation, and effects of QDs on plants and the potential applications of QDs in botany. A critical evaluation of the current limitations of QD technologies is discussed, along with the future prospects in QD-based botanical research.

Graphene quantum dots (GQD) could significantly enhance the chemiluminescence (CL) reaction of Ce(IV) with NaHSO3 through energy and electron transfer. The chemiluminescence resonance energy transfer (CRET) occurred between SO2*/1O2 and GQD, and radiative electron–hole annihilation came from the combining of the hole‐injected GQD (GQD•+) and electron‐injected GQD (GQD•–). Tyrosine (Tyr) is easily oxidized by OH• and Ce(IV), which results in the decrease of OH• and Ce(IV), and the CL intensity of the GQD–Ce(IV)–NaHSO3 system is inhibited by Tyr. In this way, the GQD‐based CL as a sensitive and selective method was applied for the detection of Tyr in the range 0.02–8.00 μM with the detection limit of 4.0 nM. The method was applied to the determination of Tyr in food samples with satisfactory results.

Wheat is a staple crop in China’s arid and semi-arid regions. Drought and low nitrogen (LN) are two major constraints to wheat growth and production. However, the molecular mechanism underlying wheat response to both drought and LN stress remains unknown. Accordingly, we conducted a proteomic study on the roots of two wheat varieties, Chang6878 (drought tolerant) and Shi4185 (drought sensitive) and compared the differences between drought and combined drought and LN stress treatments. In total, 5143 proteins were identified, of which 163 differentially abundant proteins (DAPs) were uniquely upregulated under drought and LN stress in Chang6878. Enrichment analysis showed that DAPs were mainly involved in mitogen-activated protein kinase signaling, phenylpropanoid biosynthesis, glutathione metabolism, ethylene biosynthesis, ethylene signal transduction, and oxidation–reduction reactions. These DAPs were verified via parallel reaction monitoring and quantitative real-time polymerase chain reaction. Chang6878 was treated with the ethylene synthesis precursor 1-aminocyclopropanecarboxylic acid, and its resistance to drought and LN stress improved. After treatment with the ethylene synthesis inhibitor silver nitrate and ethylene signal transduction inhibitor 1-methylcyclopropene, drought and LN stress resistance reduced. These results provide novel insights into the tolerance mechanisms of Chang6878 to drought and LN stress by altering ethylene synthesis and signal transduction. This study provides a reference for breeding drought- and low-nitrogen-tolerant wheat germplasm resources and a theoretical basis for maintaining food security in arid, barren areas.

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