Current progress on innovative pest detection techniques for stored cereal grains and thereof powders

Cascaded Aggregation Convolution Network for Salient Grain Pests Detection

Pest infestation poses significant threats to grain storage due to pests' behaviors of feeding, respiration, excretion, and reproduction. Efficient pest detection and control are essential to mitigate these risks. However, accurate detection of small grain pests remains challenging due to their small size, high variability, low contrast, and cluttered background. Salient pest detection focuses on the visual features that stand out, improving the accuracy of pest identification in complex environments. Drawing inspiration from the rapid pest recognition abilities of humans and birds, we propose a novel Cascaded Aggregation Convolution Network (CACNet) for pest detection and control in stored grain. Our approach aims to improve detection accuracy by employing a reverse cascade feature aggregation network that imitates the visual attention mechanism in humans when observing and focusing on objects of interest. The CACNet uses VGG16 as the backbone network and incorporates two key operations, namely feature enhancement and feature aggregation. These operations merge the high-level semantic information and low-level positional information of salient objects, enabling accurate segmentation of small-scale grain pests. We have curated the GrainPest dataset, comprising 500 images showcasing zero to five or more pests in grains. Leveraging this dataset and the MSRA-B dataset, we validated our method's efficacy, achieving a structure S-measure of 91.9%, and 90.9%, and a weighted F-measure of 76.4%, and 91.0%, respectively. Our approach significantly surpasses the traditional saliency detection methods and other state-of-the-art salient object detection models based on deep learning. This technology shows great potential for pest detection and assessing the severity of pest infestation based on pest density in grain storage facilities. It also holds promise for the prevention and control of pests in agriculture and forestry.

Synergistic and antagonistic effects of temperature and moisture differences on movement and distribution of Cryptolestes ferrugineus (Coleoptera: Laemophloeidae) adults in horizontal columns of wheat

The distribution of insects in stored grain bulks is significantly influenced by temperature and moisture, or their gradients or differences. This study examined the movement and distribution of Cryptolestes ferrugineus (Stephens) adults under different combinations of temperature (5 or 10°C) and moisture differences (2.5 or 5 percentage point difference) in horizontal 1 m wheat columns in 24 h. Adults showed a nonoriented distribution in dry or damp wheat (less than 15% moisture content), while the distribution was partially biased in wet wheat (17.5% moisture content) due to slightly increased temperature or spoilage of the wet wheat in 1 replicate. Adults showed a positive response to warm and damp or wet wheat. Under any levels of temperature (5 or 10°C) and moisture differences (2.5 or 5 percentage points) in 24 h, about 75% of adults were recovered from moist wheat where insects were introduced. Adults equally preferred both moist cool grain and dry warm grain located at ± 0.25 m. However, the preference for dry warm grain was stronger than moist cool grain when the movement distance was 0.45 m. The sensing ability of adults and their preferences were not only determined by movement distance but also by the magnitude of temperature and moisture differences. Thus, the findings of the present study will help in better understanding adult response to realistic temperature and moisture distributions that commonly occur in storage structures and to develop stored grain ecosystems mathematical models.

Development of sequential online extraction electrospray ionization mass spectrometry for accurate authentication of highly-similar Atractylodis Macrocephalae

The accurate and rapid authentication techniques and strategies for highly-similar foods are still lacking. Herein, a novel sequential online extraction electrospray ionization mass spectrometry (S-oEESI-MS) was developed to achieve spatio-temporally resolved ionization and comprehensive characterization of complex foods with multi-components (high, medium, and low polarity substances). Meanwhile, a characteristic marker screening method and an integrated research strategy based on MS fingerprinting, characteristic marker and chemometrics modeling were established, which are especially suitable for the accurate and rapid authentication of highly-similar foods that are difficult to be authenticated by traditional techniques (e.g., LC-MS). Thirty-two batches of highly-similar Atractylodis macrocephalae rhizome from four different origins were used as model samples. As a result, S-oEESI-MS enabled a more comprehensive MS characterization of substance profiles in complex plant samples in 1.0 min. Further, 22 characteristic markers of Atractylodis macrocephalae were ingeniously screened out and combined with multivariate statistical analysis model, the accurate authentication of highly-similar Atractylodis macrocephalae was realized. This study presents a comprehensive strategy for accurate authentication and origin analysis of highly-similar foods, which has potentially significant applications for ensuring food quality and safety.

Assessment of Mechanical Damage and Germinability in Flaxseeds Using Hyperspectral Imaging

The high demand for flax as a nutritious edible oil source combined with increasingly restrictive import regulations for oilseeds mandates the exploration of novel quantity and quality assessment methods. One pervasive issue that compromises the viability of flaxseeds is the mechanical damage to the seeds during harvest and post-harvest handling. Currently, mechanical damage in flax is assessed via visual inspection, a time-consuming, subjective, and insufficiently precise process. This study explores the potential of hyperspectral imaging (HSI) combined with chemometrics as a novel, rapid, and non-destructive method to characterize mechanical damage in flaxseeds and assess how mechanical stresses impact the germination of seeds. Flaxseed samples at three different moisture contents (MCs) (6%, 8%, and 11.5%) were subjected to four levels of mechanical stresses (0 mJ (i.e., control), 2 mJ, 4 mJ, and 6 mJ), followed by germination tests. Herein, we acquired hyperspectral images across visible to near-infrared (Vis-NIR) (450-1100 nm) and short-wave infrared (SWIR) (1000-2500 nm) ranges and used principal component analysis (PCA) for data exploration. Subsequently, mean spectra from the samples were used to develop partial least squares-discriminant analysis (PLS-DA) models utilizing key wavelengths to classify flaxseeds based on the extent of mechanical damage. The models developed using Vis-NIR and SWIR wavelengths demonstrated promising performance, achieving precision and recall rates >85% and overall accuracies of 90.70% and 93.18%, respectively. Partial least squares regression (PLSR) models were developed to predict germinability, resulting in R2-values of 0.78 and 0.82 for Vis-NIR and SWIR ranges, respectively. The study showed that HSI could be a potential alternative to conventional methods for fast, non-destructive, and reliable assessment of mechanical damage in flaxseeds.

Thermal imaging: The digital eye facilitates high-throughput phenotyping traits of plant growth and stress responses

Plant phenotyping is important for plants to cope with environmental changes and ensure plant health. Imaging techniques are perceived as the most critical and reliable tools for studying plant phenotypes. Thermal imaging has opened up new opportunities for nondestructive imaging of plant phenotyping. However, a comprehensive summary of thermal imaging in plant phenotyping is still lacking. Here we discuss the progress and future prospects of thermal imaging for assessing plant growth and stress responses. First, we classify thermal imaging into ground-based and aerial platforms based on their adaptability to different experimental environments (including laboratory, greenhouse, and field). It is convenient to collect phenotypic information of different dimensions. Second, in order to enhance the efficiency of thermal image processing, automatic algorithms based on deep learning are employed instead of traditional manual methods, greatly reducing the time cost of experiments. Considering its ease of implementation, handling and instant response, thermal imaging has been widely used in research on environmental stress, crop yield, and seed vigor. We have found that thermal imaging can detect thermal energy dissipation caused by living organisms (e.g., pests, viruses, bacteria, fungi, and oomycetes), enabling early disease diagnosis. It also recognizes changes leaf surface temperatures resulting from reduced transpiration rates caused by nutrient deficiency, drought, salinity, or freezing. Furthermore, thermal imaging predicts crop yield under different water states and forecasts the viability of dormant seeds after water absorption by monitoring temperature changes in the seeds. This work will assist biologists and agronomists in studying plant phenotypes and serve a guide for breeders to develop high-yielding, stress-tolerant, and superior crops.

High-performance electrochemiluminescence sensors based on ultra-stable perovskite quantum dots@ZIF-8 composites for aflatoxin B1 monitoring in corn samples

Aflatoxin B1 (AFB1) that is prone to contaminate corns brings a serious threat to human health. Therefore, it is of great significance to construct novel detection methods for AFB1 tracing. Here, methylamine perovskite quantum dots (MP QDs) encapsulated by ZIF-8 metal-organic frameworks (MP QDs@ZIF-8) were prepared and then ultra-stable electrochemiluminescence (ECL) sensors were developed. By the confinement of cavities structure, multiple MP QDs were crystallized and embedded inside ZIF-8 to form MP QDs@ZIF-8, achieving stable and robust ECL responds in aqueous environment. Further combined with AFB1-imprinted polymer, the constructed ECL sensor showed good selectivity and ultra-sensitivity (the detection limit was 3.5 fg/mL, S/N = 3) with a wide linear range from 11.55 fg/mL to 20 ng/mL for AFB1 quantification. Satisfactory recoveries in corn samples indicated the reliable practicability of the proposed sensor for AFB1 assay. This work provided a novel pathway in designing high-performance ECL sensing platform for food safety.