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<jats:p>Crop protection is a key activity for the sustainability and feasibility of agriculture in a current context of climate change, which is causing the destabilization of agricultural practices and an increase in the incidence of current or invasive pests, and a growing world population that requires guaranteeing the food supply chain and ensuring food security. In view of these events, this article provides a contextual review in six sections on the role of artificial intelligence (AI), machine learning (ML) and other emerging technologies to solve current and future challenges of crop protection. Over time, crop protection has progressed from a primitive agriculture 1.0 (Ag1.0) through various technological developments to reach a level of maturity closelyin line with Ag5.0 (section 1), which is characterized by successfully leveraging ML capacity and modern agricultural devices and machines that perceive, analyze and actuate following the main stages of precision crop protection (section 2). Section 3 presents a taxonomy of ML algorithms that support the development and implementation of precision crop protection, while section 4 analyses the scientific impact of ML on the basis of an extensive bibliometric study of &amp;gt;120 algorithms, outlining the most widely used ML and deep learning (DL) techniques currently applied in relevant case studies on the detection and control of crop diseases, weeds and plagues. Section 5 describes 39 emerging technologies in the fields of smart sensors and other advanced hardware devices, telecommunications, proximal and remote sensing, and AI-based robotics that will foreseeably lead the next generation of perception-based, decision-making and actuation systems for digitized, smart and real-time crop protection in a realistic Ag5.0. Finally, section 6 highlights the main conclusions and final remarks.</jats:p>

<jats:p>While <jats:italic>Cannabis sativa</jats:italic> L. varieties have been traditionally characterized by their major cannabinoid profile, it is now well established that other plant metabolites can also have physiological effects, including minor cannabinoids, terpenes, and flavonoids. Given the multiple applications of cannabis in the medical field, it is therefore critical to characterize it according to its chemical composition (i.e., its metabolome) and not only its botanical traits. With this in mind, the cannabinoid and metabolomic profiles from inflorescences of two <jats:italic>C. sativa</jats:italic> varieties with either high Δ<jats:sup>9</jats:sup>-tetrahydrocannabinolic acid (THCA) or high cannabidiolic acid (CBDA) contents harvested at different times were studied. According to results from HPLC and NMR-based untargeted metabolomic analyses of organic and aqueous plant material extracts, we show that in addition to expected variations according to cannabinoid profiles, it is possible to distinguish between harvests of the same variety. In particular, it was possible to correlate variations in the metabolome with presence of powdery mildew, leading to the identification of molecular markers associated with this fungal infection in <jats:italic>C. sativa</jats:italic>.</jats:p>

<jats:p>Plant-associated microorganisms are increasingly recognized to play key roles in host health. Among several strategies, associated microorganisms can promote the production of specific metabolites by their hosts. However, there is still a huge gap in the understanding of such mechanisms in plant-microorganism interaction. Here, we want to determine whether different levels of olive leaf spot (OLS) disease incidence were related to differences in the composition of fungal and secondary metabolites (<jats:italic>i.e.</jats:italic> phenolic and volatile compounds) in leaves from olive tree cultivars with contrasting OLS susceptibilities (ranging from tolerant to highly susceptible). Accordingly, leaves with three levels of OLS incidence from both cultivars were used to assess epiphytic and endophytic fungal communities, by barcoding of cultivable isolates, as well as to evaluate leaf phenolic and volatile composition. Fungal and metabolite compositions variations were detected according to the level of disease incidence. Changes were particularly noticed for OLS-tolerant cultivars, opposing to OLS-susceptible cultivars, suggesting that disease development is linked, not only to leaf fungal and metabolite composition, but also to host genotype. A set of metabolites/fungi that can act as predictive biomarkers of plant tolerance/susceptibility to OLS disease were identified. The metabolites α-farnesene and p-cymene, and the fungi <jats:italic>Fusarium</jats:italic> sp. and <jats:italic>Alternaria</jats:italic> sp. were more related to disease incidence, while <jats:italic>Pyronema domesticum</jats:italic> was related to the absence of disease symptoms. Cultivar susceptibility to OLS disease is then suggested to be driven by fungi, volatile and phenolic host leaves composition, and above all to plant-fungus interaction. A deeper understanding of these complex interactions may unravel plant defensive responses.</jats:p>

<jats:sec><jats:title>Introduction</jats:title><jats:p>Intensive plum production usually involves high yields but also high environmental costs due to excessive fertilizer inputs. Quantitative analysis of the environmental effects of plum production is thereby required in the development of optimum strategies to promote sustainable fruit production.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We collected survey questionnaires from 254 plum production farms in Zhao’an county, Fujian province, southeast China to assess the environmental impacts by life cycle assessment (LCA) methodology. The farms were categorized into four groups based on yield and environmental impacts, i.e., LL (low yield and low environmental impact), LH (low yield but high environmental impact), HL (high yield but low environmental impact), and HH (high yield and high environmental impact).</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>The environmental impacts, i.e., average energy depletion, global warming, acidification, and eutrophication potential in plum production were 18.17 GJ ha<jats:sup>-1</jats:sup>, 3.63 t CO<jats:sub>2</jats:sub> eq ha<jats:sup>-1</jats:sup>, 42.18 kg SO<jats:sub>2</jats:sub> eq ha<jats:sup>-1</jats:sup>, and 25.06 kg PO<jats:sub>4</jats:sub> eq ha<jats:sup>-1</jats:sup>, respectively. Only 19.7% of farmers were in the HL group, with 13.3% in the HH group, 39.0% in LL, and 28.0% LH. Plum yields of the HL group were 109-114% higher than the mean value of all 254 farms. Additionally, the HL group had a lower environmental impact per unit area compared to the overall mean value, with a reduction ranging from 31.9% to 36.7%. Furthermore, on a per tonne of plum production basis, the energy depletion, global warming potential, acidification potential, and eutrophication potential of HL farms were lower by 75.4%, 75.0%, 75.6%, and 75.8%, respectively. Overall, the total environmental impact index of LL, LH, HL, and HH groups were 0.26, 0.42, 0.06, and 0.21, respectively.</jats:p></jats:sec><jats:sec><jats:title>Discussion</jats:title><jats:p>Excessive fertilizer N application was the main source of the environmental impacts, the potential to reduce fertilizer N rate can be achieved without compromising plum yield by studying the HH group. The results provide an important foundation for enhancing the management of plum production, in order to promote ‘green’ agricultural development by reducing environmental impacts.</jats:p></jats:sec>

<jats:p>Intercropping has been acknowledged as a sustainable practice for enhancing crop productivity and water use efficiency under rainfed conditions. However, the contribution of different planting rows towards crop physiology and yield is elusive. In addition, the influence of nitrogen (N) fertilization on the physiology, yield, and soil water storage of rainfed intercropping systems is poorly understood; therefore, the objective of this experiment was to study the contribution of different crop rows on the physiological, yield, and related traits of wheat/maize relay-strip intercropping (RSI) with and without N application. The treatments comprised of two factors viz. intercropping with three levels (sole wheat, sole maize, and RSI) and two N application rates, with and without N application. Results showed that RSI significantly improved the land use efficiency and grain yield of both crops under rainfed conditions. Intercropping with N application (+N treatment) resulted in the highest wheat grain yield with 70.37 and 52.78% increase as compared with monoculture and without N application in 2019 and 2020, respectively, where border rows contributed the maximum followed by second rows. The increase in grain yield was attributed to higher values of the number of ears per square meter (10-25.33% more in comparison to sole crop without N application) during both study years. The sole wheat crop without any N application recorded the least values for all yield-related parameters. Despite the absence of significant differences, the relative decrease in intercropped maize under both N treatments was over 9% compared to the sole maize crop, which was mainly ascribed to the border rows (24.65% decrease compared to the sole crop) that recorded 12 and 13% decrease in kernel number and thousand-grain weight, respectively than the sole crop. This might be attributed to the reduced photosynthesis and chlorophyll pigmentation in RSI maize crop during the blended growth period. In a nutshell, it can be concluded that wheat/maize RSI significantly improved the land use efficiency and the total yield compared to the sole crops’ yield in arid areas in which yield advantages were mainly ascribed to the improvement in wheat yield.</jats:p>

<jats:p>Viruses deploy numerous strategies to infect plants, typically by forming complexes with another virus, leading to more efficient infection. However, the detailed plant responses to viral infection and the underlying mechanisms of co-infection remain unclear. Previously, we found that <jats:italic>tomato spotted wilt orthotospovirus</jats:italic> (TSWV) and Hippeastrum chlorotic ringspot orthotospovirus (HCRV) could infect plants in the field by forming a complex. In this study, we found that TSWV infected tobacco (<jats:italic>Nicotiana benthamiana</jats:italic>) plants in cooperation with HCRV, leading to a more efficient infection rate of both viruses. We then used the in-depth full-length transcriptome to analyze the responses of <jats:italic>N. benthamiana</jats:italic> to complex infection by TSWV–HCRV (TH). We found that infection with individual TSWV and HCRV triggered plant defense responses, including the jasmonic acid signaling pathway, autophagy, and secondary metabolism. However, TH co-infection could not trigger and even suppress some genes that are involved in these basal resistance responses, suggesting that co-infection is advantageous for the virus and not for the plants. Typically, the TH complex inhibits <jats:italic>NbPR1</jats:italic> expression to suppress tobacco resistance. Moreover, the TH complex could alter the expression of microRNAs (miRNAs), especially novel-m0782-3p and miR1992-3p, which directly interact with <jats:italic>NbSAM</jats:italic> and <jats:italic>NbWRKY6</jats:italic> and suppress their expression in tobacco, leading to downregulation of <jats:italic>NbPR1</jats:italic> and loss of resistance in tobacco to TSWV and HCRV viruses. Overall, our results elucidated the co-infection mechanisms of TH in tobacco by deploying the miRNA of plants to suppress plant basal resistance and contributed to developing a novel strategy to control crop disease caused by this virus complex.</jats:p>

<jats:p>Wheat stem rust is caused by <jats:italic>Puccinia graminis</jats:italic> f. sp. <jats:italic>tritici</jats:italic>. This major disease has been effectively controlled via resistance genes since the 1970s. The appearance and spread of new races of <jats:italic>P</jats:italic>. <jats:italic>graminis</jats:italic> f. sp. <jats:italic>tritici</jats:italic> (eg., Ug99, TKTTF, and TTRTF) have renewed the interest in identifying the resistance gene and breeding cultivars resistant to wheat stem rust. In this study, gene postulation, pedigree analysis, and molecular detection were used to determine the presence of stem rust resistance genes in 65 commercial wheat cultivars from Hebei Province. In addition, two predominant races 21C3CTHTM and 34MRGQM were used to evaluate the resistance of these cultivars at the adult-plant stage in 2021–2022. The results revealed that 6 <jats:italic>Sr</jats:italic> genes (namely, <jats:italic>Sr5</jats:italic>, <jats:italic>Sr17</jats:italic>, <jats:italic>Sr24</jats:italic>, <jats:italic>Sr31</jats:italic>, <jats:italic>Sr32</jats:italic>, <jats:italic>Sr38</jats:italic>, and <jats:italic>SrTmp</jats:italic>), either singly or in combination, were identified in 46 wheat cultivars. Overall, 37 wheat cultivars contained <jats:italic>Sr31</jats:italic>. <jats:italic>Sr5</jats:italic> and <jats:italic>Sr17</jats:italic> were present in 3 and 3 cultivars, respectively. Gao 5218 strong gluten, Jie 13-Ji 7369, and Kenong 1006 contained <jats:italic>Sr24</jats:italic>, <jats:italic>Sr32</jats:italic>, and <jats:italic>Sr38</jats:italic>, respectively. No wheat cultivar contained <jats:italic>Sr25</jats:italic> and <jats:italic>Sr26.</jats:italic> In total, 50 (76.9%) wheat cultivars were resistant to all tested races of <jats:italic>P</jats:italic>. <jats:italic>graminis</jats:italic> f. sp. <jats:italic>tritici</jats:italic> in field test in 2021–2022. This study is important for breeding wheat cultivars with resistance to stem rust.</jats:p>

<jats:p>The use of ML in agronomy has been increasing exponentially since the start of the century, including data-driven predictions of crop yields from farm-level information on soil, climate and management. However, little is known about the effect of data partitioning schemes on the actual performance of the models, in special when they are built for yield forecast. In this study, we explore the effect of the choice of predictive algorithm, amount of data, and data partitioning strategies on predictive performance, using synthetic datasets from biophysical crop models. We simulated sunflower and wheat data using <jats:italic>OilcropSun</jats:italic> and <jats:italic>Ceres-Wheat</jats:italic> from <jats:italic>DSSAT</jats:italic> for the period 2001-2020 in 5 areas of Spain. Simulations were performed in farms differing in soil depth and management. The data set of farm simulated yields was analyzed with different algorithms (regularized linear models, random forest, artificial neural networks) as a function of seasonal weather, management, and soil. The analysis was performed with Keras for neural networks and R packages for all other algorithms. Data partitioning for training and testing was performed with ordered data (i.e., older data for training, newest data for testing) in order to compare the different algorithms in their ability to predict yields in the future by extrapolating from past data. The Random Forest algorithm had a better performance (Root Mean Square Error 35-38%) than artificial neural networks (37-141%) and regularized linear models (64-65%) and was easier to execute. However, even the best models showed a limited advantage over the predictions of a sensible baseline (average yield of the farm in the training set) which showed RMSE of 42%. Errors in seasonal weather forecasting were not taken into account, so real-world performance is expected to be even closer to the baseline. Application of AI algorithms for yield prediction should always include a comparison with the best guess to evaluate if the additional cost of data required for the model compensates for the increase in predictive power. Random partitioning of data for training and validation should be avoided in models for yield forecasting. Crop models validated for the region and cultivars of interest may be used before actual data collection to establish the potential advantage as illustrated in this study.</jats:p>

<jats:p>Potato virus Y (PVY) mainly infects Solanaceous crops, resulting in considerable losses in the yield and quality. Iron (Fe) is involved in various biological processes in plants, but its roles in resistance to PVY infection has not been reported. In this study, foliar application of Fe could effectively inhibit early infection of PVY, and a full-length transcriptome and Illumina RNA sequencing was performed to investigate its modes of action in PVY-infected <jats:italic>Nicotiana tabacum</jats:italic>. The results showed that 18,074 alternative splicing variants, 3,654 fusion transcripts, 3,086 long non-coding RNAs and 14,403 differentially expressed genes (DEGs) were identified. Specifically, Fe application down-regulated the expression levels of the DEGs related to phospholipid hydrolysis, phospholipid signal, cell wall biosynthesis, transcription factors (TFs) and photosystem I composition, while those involved with photosynthetic electron transport chain (PETC) were up-regulated at 1 day post inoculation (dpi). At 3 dpi, these DEGs related to photosystem II composition, PETC, molecular chaperones, protein degradation and some TFs were up-regulated, while those associated with light-harvesting, phospholipid hydrolysis, cell wall biosynthesis were down-regulated. At 9 dpi, Fe application had little effects on resistance to PVY infection and transcript profiles. Functional analysis of these potentially critical DEGs was thereafter performed using virus-induced gene silencing approaches and the results showed that <jats:italic>NbCat-6A</jats:italic> positively regulates PVY infection, while the reduced expressions of <jats:italic>NbWRKY26</jats:italic>, <jats:italic>NbnsLTP</jats:italic>, <jats:italic>NbFAD3</jats:italic> and <jats:italic>NbHSP90</jats:italic> significantly promote PVY infection in <jats:italic>N. benthamiana</jats:italic>. Our results elucidated the regulatory network of Fe-mediated resistance to PVY infection in plants, and the functional candidate genes also provide important theoretical bases to further improve host resistance against PVY infection.</jats:p>