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The lysin motif (LysM) containing proteins can bind chitin and are ubiquitous in various organisms including fungi. In plant pathogenic fungi, a few LysM proteins have been characterized as effectors to suppress chitin-induced immunity in plant hosts and therefore contribute to fungal virulence. The effector mechanism is still questioned in fungus-animal interactions. In this study, we found that LysM proteins are also present in animal pathogenic fungi and have evolved divergently. The genome of the insect pathogen Beauveria bassiana encodes 12 LysM proteins, and the genes were differentially transcribed by the fungus when grown in different conditions. Deletion of six genes that were expressed by the fungus growing in insects revealed that two, Blys2 and Blys5, were required for full fungal virulence. Both proteins could bind chitin and Blys5 (containing two LysM domains) could additionally bind chitosan and cellulose. Truncation analysis of Blys2 (containing five LysM domains) indicated that the combination of LysM domains could determine protein-binding affinity and specificity for different carbohydrates. Relative to the wild-type strain, loss of Blys2 or Blys5 could impair fungal propagation in insect hemocoels and lead to the upregulation of antifungal gene in insects. Interestingly, the virulence defects of ΔBlys2 and ΔBlys5 could be fully restored by complementation with the Slp1 effector from the rice blast fungus Magnaporthe oryzae. In contrast to Slp1 and Blys2, Blys5 could potentially protect fungal hyphae against chitinase hydrolysis. The results of this study not only advance the understanding of LysM protein evolution but also establish the effector mechanism of fungus-animal interactions.

Insect pathogenic fungi produce a plethora of insecticidally and pharmaceutically active compounds, including 39 cyclohexadepsipeptide destruxins (dtxs). Even though dtxs were first discovered more than 50 y ago, the genes responsible for their biosynthesis were unknown until this study. Based on our comparative genomic information and targeted gene disruptions, we report the gene cluster for dtx biosynthesis in the insect pathogen Metarhizium robertsii. The nonribosomal peptide synthetase DtxS1 has six adenylation domains, two of which are capable of selecting different amino acids to synthesize dtx B and its analogs. The cytochrome P450 enzyme DtxS2 converts dtx B into other dtxs by a chain of reactions, each producing a new derivative. The aldo-keto reductase DtxS3 and aspartic acid decarboxylase DtxS4 are responsible for the conversion and provision of the first and last substrates for the dtx assembly line, respectively. Insect bioassays showed that dtxs could suppress both cellular and humoral immune responses thereby assisting fungal propagation in insects. The differing abilities of Metarhizium species to produce toxins is dependent on the presence of the dtxS1 gene. The toxigenic species are capable of killing multiple orders of insects, whereas the nontoxigenic Metarhizium spp. have narrow host ranges. Thus, the acquisition or retention of the dtx biosynthesis gene cluster in Metarhizium lineages has been coordinated with the evolution of fungal host specificity. The data from this study will facilitate the development of dtxs as bioinsecticides or Pharmaceuticals.

New, non-destructive sensing techniques for fast and more effective quality assessment of fruits and vegetables are needed to meet the ever-increasing consumer demand for better, more consistent and safer food products. Over the past 15 years, hyperspectral imaging has emerged as a new generation of sensing technology for non-destructive food quality and safety evaluation, because it integrates the major features of imaging and spectroscopy, thus enabling the acquisition of both spectral and spatial information from an object simultaneously. This paper first provides a brief overview of hyperspectral imaging configurations and common sensing modes used for food quality and safety evaluation. The paper is, however, focused on the three innovative hyperspectral imaging-based techniques or sensing platforms, i.e., spectral scattering, integrated reflectance and transmittance, and spatially-resolved spectroscopy, which have been developed in our laboratory for property and quality evaluation of fruits, vegetables and other food products. The basic principle and instrumentation of each technique are described, followed by the mathematical methods for processing and extracting critical information from the acquired data. Applications of these techniques for property and quality evaluation of fruits and vegetables are then presented. Finally, concluding remarks are given on future research needs to move forward these hyperspectral imaging techniques.

As an emerging cash crop, industrial hemp ( Cannabis sativa L.) grown for cannabidiol (CBD) has spurred a surge of interest in the United States. Cultivar selection and harvest timing are important to produce CBD hemp profitably and avoid economic loss resulting from the tetrahydrocannabinol (THC) concentration in the crop exceeding regulatory limits. Hence there is a need for differentiating CBD hemp cultivars and growth stages to aid in cultivar and genotype selection and optimization of harvest timing. Current methods that rely on visual assessment of plant phenotypes and chemical procedures are limited because of its subjective and destructive nature. In this study, hyperspectral imaging was proposed as a novel, objective, and non-destructive method for differentiating hemp cultivars, growth stages as well as plant organs (leaves and flowers). Five cultivars of CBD hemp were grown greenhouse conditions and leaves and flowers were sampled at five growth stages 2–10 weeks in 2-week intervals after flower initiation and scanned by a benchtop hyperspectral imaging system in the spectral range of 400–1000 nm. The acquired images were subjected to image processing procedures to extract the spectra of hemp samples. The spectral profiles and scatter plots of principal component analysis of the spectral data revealed a certain degree of separation between hemp cultivars, growth stages, and plant organs. Machine learning based on regularized linear discriminant analysis achieved the accuracy of up to 99.6% in differentiating the five hemp cultivars. Plant organ and growth stage need to be factored into model development for hemp cultivar classification. The classification models achieved 100% accuracy in differentiating the five growth stages and two plant organs. This study demonstrates the effectiveness of hyperspectral imaging for differentiating cultivars, growth stages and plant organs of CBD hemp, which is a potentially useful tool for growers and breeders of CBD hemp.

The curation of large-scale, diverse datasets for robust weed detection is extremely time-consuming and resource-intensive in practice. Generative artificial intelligence (AI) opens up opportunities for image generation to supplement real-world image acquisition and annotation efforts. However, it is not a trial task to generate high-quality, multi-class weed images that capture the nuances and variations in visual representations for enhanced weed detection. This study presents a novel investigation of advanced stable diffusion (SD) integrated with a module with image prompt capability, IP-Adapter, for weed image generation. Using the IP-Adapter-based model, two image feature encoders, CLIP (contrastive language image pre-training) and BioCLIP (a vision foundation model for biological images), were utilized to generate weed instances, which were then inserted into existing weed images. Image generation and weed detection experiments are conducted on a 10-class weed dataset captured in vegetable fields. The perceptual quality of generated images is assessed in terms of Fréchet Inception Distance (FID) and Inception Score (IS). YOLOv11 (You Only Look Once version 11) models were trained for weed detection, achieving an improved mAP@50:95 of 1.26% on average when combining inserted weed instances with real ones in training, compared to using original images alone. Both the weed dataset and software programs in this study will be made publicly available. This study offers valuable perspectives into the use of IP-adapter-based SD for generating weed images and weed detection.

Many prokaryotic and eukaryotic proteins contain domains of unknown function (DUFs). A DUF3129 family of proteins is widely encoded in the genomes of fungal pathogens. A few studies in plant and insect pathogens indicated that the DUF3129 genes are required for fungal penetration of host cuticles with an unclear mechanism. We found that a varied number of DUF3129 proteins is present in different fungal species and the proteins are evolutionarily diverged from each other at the inter- and intra-specific levels. By using the insect pathogenic fungus Metarhizium robertsii as a model, we performed experiments and found that the seven DUF3129 proteins encoded by this fungus are localized to cellular lipid droplets (LDs). Individual deletion of these genes did not affect fungal formation of the infection structure appressoria and the accumulation of LDs in fungal conidia. When compared with the wild-type (WT) strain, insect bioassays revealed that the virulence of most null mutants were significantly impaired during topical infection but not during injection of insects. Carbon starvation and the subsequent Western blot analysis indicated that the LD-specific perilipin protein was completely degraded in the WT cells whereas varied levels of perilipin could be detected in the mutant cells, which signified that depletion of LD content was delayed in mutant cells, and DUF3129 proteins are therefore involved in LD degradation. We also provided biochemical evidence that these DUF3129 genes are transcriptionally regulated by a yeast Ste12-like transcription factor. The findings of this study not only unveil the function of DUF3129 proteins but also better understand the diverse mechanism of fungus–host interactions. This article is part of the theme issue ‘Biotic signalling sheds light on smart pest management’.

Automated sorting of sweetpotatoes is necessary to reduce labor dependence and costs that are significant at today’s sweetpotato packing sheds. Although optical sorters have been widely adopted in commercial packing lines for many horticultural commodities, there remains an unmet need to develop dedicated technology for the automated grading and sorting of sweetpotatoes. Sorting mechanisms are the critical component that physically segregates products according to quality grades determined by a machine vision or imaging system. This study presents the new engineering prototypes and evaluation of three different pneumatically powered mechanisms for sorting sweetpotatoes online. Among the three sorters, the sorting mechanism, which employs a linear air cylinder to drive a paddle directly striking products, achieved the best overall accuracy and repeatability of 98% and 96.8%, respectively, at conveyor speeds of 4–12 cm/s. The sorter based on a rotary actuator also delivered decent accuracy and repeatability of 97.9% and 95.6%, respectively. The best-performing sorting mechanism was integrated with a machine vision system that graded sweetpotatoes based on size and surface defect conditions to separate graded sweetpotatoes into three quality categories. The errors of 0–1% due to the sorting process were obtained at conveyor speeds of 4–12 cm/s, confirming the efficacy of the manufactured sorting mechanisms. There was a declining trend with the conveyor speed in the performance of the sorting mechanisms when evaluated either in a standalone or integrated configuration. The proposed sorting mechanisms that are simple in construction and operation and of low cost are useful for developing a more full-fledged sorting system. More research is needed to enhance sorting performance and conduct extensive tests at higher conveyor speeds for practical application.

Pickling cucumbers are susceptible to chilling injury (CI) during postharvest refrigerated storage, which would result in quality degradation and economic loss. It is, thus, desirable to remove the defective fruit before they are marketed as fresh products or processed into pickled products. Chlorophyll fluorescence is sensitive to CI in green fruits, because exposure to chilling temperatures can induce detectable alterations in chlorophylls of tissues. This study evaluated the feasibility of using a dual-band chlorophyll fluorescence imaging (CFI) technique for detecting CI-affected pickling cucumbers. Chlorophyll fluorescence images at 675 nm and 750 nm were acquired from pickling cucumbers under the excitation of ultraviolet-blue light. The raw images were processed for vignetting corrections through bi-dimensional empirical mode decomposition and subsequent image reconstruction. The fluorescence images were effective for ascertaining CI-affected tissues, which appeared as dark areas in the images. Support vector machine models were developed for classifying pickling cucumbers into two or three classes using the features extracted from the fluorescence images. Fusing the features of fluorescence images at 675 nm and 750 nm resulted in overall accuracies of 96.9% and 91.2% for two-class (normal and injured) and three-class (normal, mildly and severely injured) classification, respectively, which are statistically significantly better than those obtained using the features at a single wavelength, especially for the three-class classification. Furthermore, a subset of features, selected based on the neighborhood component feature selection technique, achieved the highest accuracies of 97.4% and 91.3% for the two-class and three-class classification, respectively. This study demonstrated that dual-band CFI is an effective modality for CI detection in pickling cucumbers.

The rapid pace of urbanization and increasing demands for urban functionalities have led to diversification and complexity in the types of urban surface elements. The conventional approach of relying solely on remote sensing imagery for urban surface element extraction faces emerging challenges. Data-driven techniques, including deep learning and machine learning, necessitate a substantial number of annotated samples as prerequisites. In response, our study proposes a knowledge-driven approach that integrates multisource data with ontology to achieve precise urban surface element extraction. Within this framework, components from the EIONET Action Group on Land Monitoring in Europe matrix serve as ontology primitives, forming a shared vocabulary. The semantics of surface elements are deconstructed using these primitives, enabling the creation of specific descriptions for various types of urban surface elements by combining these primitives. Our approach integrates multitemporal high-resolution remote sensing data, network big data, and other heterogeneous data sources. It segments high-resolution images into individual patches, and for each unit, urban surface element classification is accomplished through semantic rule-based inference. We conducted experiments in two regions with varying levels of urban scene complexity, achieving overall accuracies of 93.03% and 97.35%, respectively. Through this knowledge-driven approach, our proposed method significantly enhances the classification performance of urban surface elements in complex scenes, even in the absence of sample data, thereby presenting a novel approach to urban surface element extraction.