Digital plant pathology: a foundation and guide to modern agriculture

Optimizing greenhouse microclimate for plant pathology: challenges and cooling solutions for pathogen control in arid regions

Crop production using greenhouse technology has become increasingly essential for intensifying agricultural output, particularly in regions with challenging climatic conditions. More so, greenhouses do not only support continuous crop supply but also provide a controlled environment crucial for studying plant-pathogen interaction. Likewise, pests and diseases are a constant threat to crop production, which requires innovative control methods. Providing a suitable and sustainable control method requires a detailed probe into the relationship between plants and biotic disturbance under controlled settings. Therefore this review explores the relationships between plants and pathogens, highlighting the impact of extreme greenhouse microclimates on plant pathology assays. Given the extreme weather conditions in the Arabian peninsula, the efficiency of greenhouses, especially during summer, is compromised without adequate cooling systems. This review discusses the current strategies employed to optimize greenhouse conditions in hot arid regions, aiming to enhance plant health by mitigating pathogen activity while minimizing energy, and water consumption. The review also provides an overview of how microclimatic parameters within greenhouses influence plant-pathogen dynamics, ensuring conditions that are conducive to managing both biotic and abiotic diseases. Additionally, the review aims to evaluate various cooling techniques available and most widely accepted in hot arid regions. Moreover, the performance indicators, principles, and effectiveness of each technique are discussed. Promising advances in the manipulations and combination of these techniques have proven to maintain an appropriate greenhouse microclimate with minimal resource use.

High-Resolution Disease Phenotyping Reveals Distinct Resistance Mechanisms of Tomato Crop Wild Relatives against Sclerotinia sclerotiorum

Besides the well-understood qualitative disease resistance, plants possess a more complex quantitative form of resistance: quantitative disease resistance (QDR). QDR is commonly defined as a partial but more durable form of resistance and, therefore, might display a valuable target for resistance breeding. The characterization of QDR phenotypes, especially of wild crop relatives, displays a bottleneck in deciphering QDR's genomic and regulatory background. Moreover, the relationship between QDR parameters, such as infection frequency, lag-phase duration, and lesion growth rate, remains elusive. High hurdles for applying modern phenotyping technology, such as the low availability of phenotyping facilities or complex data analysis, further dampen progress in understanding QDR. Here, we applied a low-cost (<1.000 €) phenotyping system to measure lesion growth dynamics of wild tomato species (e.g., Solanum pennellii or Solanum pimpinellifolium). We provide insight into QDR diversity of wild populations and derive specific QDR mechanisms and their cross-talk. We show how temporally continuous observations are required to dissect end-point severity into functional resistance mechanisms. The results of our study show how QDR can be maintained by facilitating different defense mechanisms during host-parasite interaction and that the capacity of the QDR toolbox highly depends on the host's genetic context. We anticipate that the present findings display a valuable resource for more targeted functional characterization of the processes involved in QDR. Moreover, we show how modest phenotyping technology can be leveraged to help answer highly relevant biological questions.

Comparative Analysis of Hulless Barley Transcriptomes to Regulatory Effects of Phosphorous Deficiency

Hulless barley is a cold-resistant crop widely planted in the northwest plateau of China. It is also the main food crop in this region. Phosphorus (P), as one of the important essential nutrient elements, regulates plant growth and defense. This study aimed to analyze the development and related molecular mechanisms of hulless barley under P deficiency and explore the regulatory genes so as to provide a basis for subsequent molecular breeding research. Transcriptome analysis was performed on the root and leaf samples of hulless barley cultured with different concentrations of KH2PO4 (1 mM and 10 μM) Hoagland solution. A total of 46,439 genes were finally obtained by the combined analysis of leaf and root samples. Among them, 325 and 453 genes had more than twofold differences in expression. These differentially expressed genes (DEGs) mainly participated in the abiotic stress biosynthetic process through Gene Ontology prediction. Moreover, the Kyoto Encyclopedia of Genes and Genomes showed that DEGs were mainly involved in photosynthesis, plant hormone signal transduction, glycolysis, phenylpropanoid biosynthesis, and synthesis of metabolites. These pathways also appeared in other abiotic stresses. Plants initiated multiple hormone synergistic regulatory mechanisms to maintain growth under P-deficient conditions. Transcription factors (TFs) also proved these predictions. The enrichment of ARR-B TFs, which positively regulated the phosphorelay-mediated cytokinin signal transduction, and some other TFs (AP2, GRAS, and ARF) was related to plant hormone regulation. Some DEGs showed different values in their FPKM (fragment per kilobase of transcript per million mapped reads), but the expression trends of genes responding to stress and phosphorylation remained highly consistent. Therefore, in the case of P deficiency, the first response of plants was the expression of stress-related genes. The effects of this stress on plant metabolites need to be further studied to improve the relevant regulatory mechanisms so as to further understand the importance of P in the development and stress resistance of hulless barley.

Deep transfer learning with gravitational search algorithm for enhanced plant disease classification

Plant diseases annually cause damage and loss of much of the crop, if not its complete destruction, and this constitutes a significant challenge for farm owners, governments, and consumers alike. Therefore, identifying and classifying diseases at an early stage is very important in order to sustain local and global food security. In this research, we designed a new method to identify plant diseases by combining transfer learning and Gravitational Search Algorithm (GSA). Two state-of-the-art pretrained models have been adopted for extracting features in this study, which are MobileNetV2 and ResNe50V2. Multilayer feature extraction is applied in this study to ensure representations of plant leaves from different levels of abstraction for precise classification. These features are then concatenated and passed to GSA for optimizing them. Finally, optimized features are passed to Multinomial Logistic Regression (MLR) for final classification. This integration is essential for categorizing 18 different types of infected and healthy leaf samples. The performance of our approach is strengthened by a comparative analysis that incorporates features optimized by the Genetic Algorithm (GA). Additionally, the MLR algorithm is contrasted with K-Nearest Neighbors (KNN). The empirical findings indicate that our model, which has been refined using GSA, achieves very high levels of precision. Specifically, the average precision for MLR is 99.2%, while for KNN it is 98.6%. The resulting results significantly exceed those achieved with GA-optimized features, thereby highlighting the superiority of our suggested strategy. One important result of our study is that we were able to decrease the number of features by more than 50%. This reduction greatly reduces the processing requirements without sacrificing the quality of the diagnosis. This work presents a robust and efficient approach to the early detection of plant diseases. The work demonstrates the utilization of sophisticated computational methods in agriculture, enabling the development of novel data-driven strategies for plant health management, therefore enhancing worldwide food security.

Integrated Nematode Management in a World in Transition: Constraints, Policy, Processes, and Technologies for the Future

Plant-parasitic nematodes are one of the most insidious pests limiting agricultural production, parasitizing mostly belowground and occasionally aboveground plant parts. They are an important and underestimated component of the estimated 30% yield loss inflicted on crops globally by biotic constraints. Nematode damage is intensified by interactions with biotic and abiotic factors constraints: soilborne pathogens, soil fertility degradation, reduced soil biodiversity, climate variability, and policies influencing the development of improved management options. This review focuses on the following topics: (a) biotic and abiotic constraints, (b) modification of production systems, (c) agricultural policies, (d) the microbiome, (e) genetic solutions, and (f) remote sensing. Improving integrated nematode management (INM) across all scales of agricultural production and along the Global North-Global South divide, where inequalities influence access to technology, is discussed. The importance of the integration of technological development in INM is critical to improving food security and human well-being in the future.

Integrating Community Ecology into Models of Vector-Borne Virus Transmission

Vector-borne plant viruses are a diverse and dynamic threat to agriculture with hundreds of economically damaging viruses and insect vector species. Mathematical models have greatly increased our understanding of how alterations of vector life history and host-vector-pathogen interactions can affect virus transmission. However, insect vectors also interact with species such as predators and competitors in food webs, and these interactions affect vector population size and behaviors in ways that mediate virus transmission. Studies assessing how species' interactions affect vector-borne pathogen transmission are limited in both number and scale, hampering the development of models that appropriately capture community-level effects on virus prevalence. Here, we review vector traits and community factors that affect virus transmission, explore the existing models of vector-borne virus transmission and areas where the principles of community ecology could improve the models and management, and finally evaluate virus transmission in agricultural systems. We conclude that models have expanded our understanding of disease dynamics through simulations of transmission but are limited in their ability to reflect the complexity of ecological interactions in real systems. We also document a need for experiments in agroecosystems, where the high availability of historical and remote-sensing data could serve to validate and improve vector-borne virus transmission models.

Recent Advancements and Challenges of AIoT Application in Smart Agriculture: A Review

As the most popular technologies of the 21st century, artificial intelligence (AI) and the internet of things (IoT) are the most effective paradigms that have played a vital role in transforming the agricultural industry during the pandemic. The convergence of AI and IoT has sparked a recent wave of interest in artificial intelligence of things (AIoT). An IoT system provides data flow to AI techniques for data integration and interpretation as well as for the performance of automatic image analysis and data prediction. The adoption of AIoT technology significantly transforms the traditional agriculture scenario by addressing numerous challenges, including pest management and post-harvest management issues. Although AIoT is an essential driving force for smart agriculture, there are still some barriers that must be overcome. In this paper, a systematic literature review of AIoT is presented to highlight the current progress, its applications, and its advantages. The AIoT concept, from smart devices in IoT systems to the adoption of AI techniques, is discussed. The increasing trend in article publication regarding to AIoT topics is presented based on a database search process. Lastly, the challenges to the adoption of AIoT technology in modern agriculture are also discussed.