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The cyber-agricultural system (CAS) represents an overarching framework of agriculture that leverages recent advances in ubiquitous sensing, artificial intelligence, smart actuators, and scalable cyberinfrastructure (CI) in both breeding and production agriculture. We discuss the recent progress and perspective of the three fundamental components of CAS - sensing, modeling, and actuation - and the emerging concept of agricultural digital twins (DTs). We also discuss how scalable CI is becoming a key enabler of smart agriculture. In this review we shed light on the significance of CAS in revolutionizing crop breeding and production by enhancing efficiency, productivity, sustainability, and resilience to changing climate. Finally, we identify underexplored and promising future directions for CAS research and development., Competing Interests: Declaration of interests The authors declare no conflicts of interest., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Purpose: Inappropriate inflammatory responses within the nervous system (neuroinflammation) have been implicated in several neurological conditions. Class II transactivator (CIITA), a principal regulator of the major histocompatibility complex II (MHCII), is known to play essential roles in inflammation. Hence, CIITA and its interactors could be potentially involved in multiple neurological disorders. However, the molecular mechanisms underlying CIITA-mediated neuroinflammation (NI) are yet to be understood., Materials and Methods: In this regard, we analyzed the potential involvement of CIITA and its interactome in the regulation of neuroinflammation. In the present study, using various computational tools, we aimed (1) to identify NI-related proteins, (2) to filter the critical interactors in the CIITA-NI network, and (3) to analyze the protein-disease interactions and the associated molecular pathways through which CIITA could influence neuroinflammation., Results: CIITA was found to interact with P T GS2, GSK3B, and NR3C1 and may influence depressive disorders. Further, the IL4/IL13 pathway was found to be potentially underlying the CIITA-interactomemediated effects on neurological disorders. Moreover, CIITA was found to be connected to genes associated with depressive disorder through IL4, wherein CIITA was found to be potentially involved in depressive disorders through IL-4/IL-13 and hippo pathways. However, the present study is based on the existing data on protein interactomes and could be re-evaluated as newer interactions are discovered. Also, the functional mechanisms of CIITA's roles in neuroinflammation must be evaluated further., Conclusion: Notwithstanding these limitations, the results presented here, could form a basis for further experimental studies to assess CIITA as a potential therapeutic target in managing depression and other neuroinflammatory disorders.

Major histocompatibility complex II (MHCII), a mediator of the innate and adaptive immune system, plays a central role in regulating inflammation and its progression. Class II transactivator (CIITA) is a master regulator of MHCII expression and controls antigen presentation followed by T-cell activation. Regulation of inflammation by modulation of CIITA has been suggested as a promising intervention for several disorders, including neuroinflammation, rheumatoid arthritis and other autoimmune diseases. This study aimed to (i) identify possible pharmacological agents which could bind to and inhibit isoform I of CIITA (CIITA-I) and (ii) determine their strength of interactions. The structure of CIITA-I isoform was predicted using phyre2 and refined via 3D refine. Loops were refined using ModBase, followed by quality assessment based on ERRAT value. The refined 3D structure was subjected to docking via Maestro (from Schrodinger) using glide module against small molecule databases. Molecules having the least glide score and favorable ADME properties were subjected to molecular simulation by GROMACS. We used the 3D refined structure of CIITA-I, with a score of 83.4% in ERRAT for docking studies. The ligand 4-(2-((6-oxo-4-phenyl-1,6-dihydropyrimidin-2-yl) thio) acetamido) benzamide (ZINC5154833), showed maximum glide score (-6.591) followed by N-[4-(3-oxo-3-{4-[3-(trifluoromethyl) phenyl] piperazin-1-yl} propyl)-1,3-thiazol-2-yl] benzamide (F5254-0161, glide score -6.41). Simulation studies using GROMACS showed F5254-0161 to have a more stable interaction with CIITA-I. Based on our analysis, we propose ZINC5154833 and F5254-0161 as potential modulators for CIITA-I.Communicated by Ramaswamy H. Sarma.

Introduction: Computer vision and deep learning (DL) techniques have succeeded in a wide range of diverse fields. Recently, these techniques have been successfully deployed in plant science applications to address food security, productivity, and environmental sustainability problems for a growing global population. However, training these DL models often necessitates the large-scale manual annotation of data which frequently becomes a tedious and time-and-resource- intensive process. Recent advances in self-supervised learning (SSL) methods have proven instrumental in overcoming these obstacles, using purely unlabeled datasets to pre-train DL models., Methods: Here, we implement the popular self-supervised contrastive learning methods of NNCLR Nearest neighbor Contrastive Learning of visual Representations) and SimCLR (Simple framework for Contrastive Learning of visual Representations) for the classification of spatial orientation and segmentation of embryos of maize kernels. Maize kernels are imaged using a commercial high-throughput imaging system. This image data is often used in multiple downstream applications across both production and breeding applications, for instance, sorting for oil content based on segmenting and quantifying the scutellum's size and for classifying haploid and diploid kernels., Results and Discussion: We show that in both classification and segmentation problems, SSL techniques outperform their purely supervised transfer learning-based counterparts and are significantly more annotation efficient. Additionally, we show that a single SSL pre-trained model can be efficiently finetuned for both classification and segmentation, indicating good transferability across multiple downstream applications. Segmentation models with SSL-pretrained backbones produce DICE similarity coefficients of 0.81, higher than the 0.78 and 0.73 of those with ImageNet-pretrained and randomly initialized backbones, respectively. We observe that finetuning classification and segmentation models on as little as 1% annotation produces competitive results. These results show SSL provides a meaningful step forward in data efficiency with agricultural deep learning and computer vision., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Dong, Nagasubramanian, Wang, Frei, Jubery, Lübberstedt and Ganapathysubramanian.)

Plant stress phenotyping is essential to select stress-resistant varieties and develop better stress-management strategies. Standardization of visual assessments and deployment of imaging techniques have improved the accuracy and reliability of stress assessment in comparison with unaided visual measurement. The growing capabilities of machine learning (ML) methods in conjunction with image-based phenotyping can extract new insights from curated, annotated, and high-dimensional datasets across varied crops and stresses. We propose an overarching strategy for utilizing ML techniques that methodically enables the application of plant stress phenotyping at multiple scales across different types of stresses, program goals, and environments., (Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Premise: Trichomes are hair-like appendages extending from the plant epidermis. They serve many important biotic roles, including interference with herbivore movement. Characterizing the number, density, and distribution of trichomes can provide valuable insights on plant response to insect infestation and define the extent of plant defense capability. Automated trichome counting would speed up this research but poses several challenges, primarily because of the variability in coloration and the high occlusion of the trichomes., Methods and Results: We developed a simplified method for image processing for automated and semi-automated trichome counting. We illustrate this process using 30 leaves from 10 genotypes of soybean ( Glycine max ) differing in trichome abundance. We explored various heuristic image-processing methods including thresholding and graph-based algorithms to facilitate trichome counting. Of the two automated and two semi-automated methods for trichome counting tested and with the help of regression analysis, the semi-automated manually annotated trichome intersection curve method performed best, with an accuracy of close to 90% compared with the manually counted data., Conclusions: We address trichome counting challenges including occlusion by combining image processing with human intervention to propose a semi-automated method for trichome quantification. This provides new opportunities for the rapid and automated identification and quantification of trichomes, which has applications in a wide variety of disciplines., (© 2020 Mirnezami et al. Applications in Plant Sciences is published by Wiley Periodicals LLC on behalf of the Botanical Society of America.)

Background: Hyperspectral imaging is emerging as a promising approach for plant disease identification. The large and possibly redundant information contained in hyperspectral data cubes makes deep learning based identification of plant diseases a natural fit. Here, we deploy a novel 3D deep convolutional neural network (DCNN) that directly assimilates the hyperspectral data. Furthermore, we interrogate the learnt model to produce physiologically meaningful explanations. We focus on an economically important disease, charcoal rot, which is a soil borne fungal disease that affects the yield of soybean crops worldwide., Results: Based on hyperspectral imaging of inoculated and mock-inoculated stem images, our 3D DCNN has a classification accuracy of 95.73% and an infected class F1 score of 0.87. Using the concept of a saliency map, we visualize the most sensitive pixel locations, and show that the spatial regions with visible disease symptoms are overwhelmingly chosen by the model for classification. We also find that the most sensitive wavelengths used by the model for classification are in the near infrared region (NIR), which is also the commonly used spectral range for determining the vegetative health of a plant., Conclusion: The use of an explainable deep learning model not only provides high accuracy, but also provides physiological insight into model predictions, thus generating confidence in model predictions. These explained predictions lend themselves for eventual use in precision agriculture and research application using automated phenotyping platforms., Competing Interests: Competing interestsThe authors declare that they have no competing interests.

The rate of advancement made in phenomic-assisted breeding methodologies has lagged those of genomic-assisted techniques, which is now a critical component of mainstream cultivar development pipelines. However, advancements made in phenotyping technologies have empowered plant scientists with affordable high-dimensional datasets to optimize the operational efficiencies of breeding programs. Phenomic and seed yield data was collected across six environments for a panel of 292 soybean accessions with varying genetic improvements. Random forest, a machine learning (ML) algorithm, was used to map complex relationships between phenomic traits and seed yield and prediction performance assessed using two cross-validation (CV) scenarios consistent with breeding challenges. To develop a prescriptive sensor package for future high-throughput phenotyping deployment to meet breeding objectives, feature importance in tandem with a genetic algorithm (GA) technique allowed selection of a subset of phenotypic traits, specifically optimal wavebands. The results illuminated the capability of fusing ML and optimization techniques to identify a suite of in-season phenomic traits that will allow breeding programs to decrease the dependence on resource-intensive end-season phenotyping (e.g., seed yield harvest). While we illustrate with soybean, this study establishes a template for deploying multitrait phenomic prediction that is easily amendable to any crop species and any breeding objective., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2019 Kyle Parmley et al.)

Background: Charcoal rot is a fungal disease that thrives in warm dry conditions and affects the yield of soybeans and other important agronomic crops worldwide. There is a need for robust, automatic and consistent early detection and quantification of disease symptoms which are important in breeding programs for the development of improved cultivars and in crop production for the implementation of disease control measures for yield protection. Current methods of plant disease phenotyping are predominantly visual and hence are slow and prone to human error and variation. There has been increasing interest in hyperspectral imaging applications for early detection of disease symptoms. However, the high dimensionality of hyperspectral data makes it very important to have an efficient analysis pipeline in place for the identification of disease so that effective crop management decisions can be made. The focus of this work is to determine the minimal number of most effective hyperspectral wavebands that can distinguish between healthy and diseased soybean stem specimens early on in the growing season for proper management of the disease. 111 hyperspectral data cubes representing healthy and infected stems were captured at 3, 6, 9, 12, and 15 days after inoculation. We utilized inoculated and control specimens from 4 different genotypes. Each hyperspectral image was captured at 240 different wavelengths in the range of 383-1032 nm. We formulated the identification of best waveband combination from 240 wavebands as an optimization problem. We used a combination of genetic algorithm as an optimizer and support vector machines as a classifier for the identification of maximally-effective waveband combination., Results: A binary classification between healthy and infected soybean stem samples using the selected six waveband combination (475.56, 548.91, 652.14, 516.31, 720.05, 915.64 nm) obtained a classification accuracy of 97% for the infected class. Furthermore, we achieved a classification accuracy of 90.91% for test samples from 3 days after inoculation using the selected six waveband combination., Conclusions: The results demonstrated that these carefully-chosen wavebands are more informative than RGB images alone and enable early identification of charcoal rot infection in soybean. The selected wavebands could be used in a multispectral camera for remote identification of charcoal rot infection in soybean.