Submit to Agronomy Review for Agronomy

Journal Menu

Journal Browser

► Journal Browser

Agronomy, Volume 14, Issue 9 (September 2024) – 6 articles

Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
You may sign up for e-mail alerts to receive table of contents of newly released issues.
PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.

Order results

Result details

Section

Show export options Show export options

Select all

Export citation of selected articles as:

14 pages, 2167 KiB

Open AccessReview

Type III Secretion Effectors of Xanthomonas oryzae pv. oryzicola: The Arsenal to Attack Equivalent Rice Defense for Invasion

by Nawei Tan, Yechao Huang, Weiguo Miao, Qingxia Zhang and Tao Wu

Agronomy 2024, 14(9), 1881; https://doi.org/10.3390/agronomy14091881 (registering DOI) - 23 Aug 2024

Abstract

Rice–Xanthomonas oryzae pv. oryzicola (Xoc) is one of the commonly used rice models of host–pathogen interactions. Xoc causes bacterial leaf streak (BLS) and has quarantine status. As a Gram-negative pathogen, Xoc usually employs type III secretion effectors (T3SEs), including transcription activator-like effectors (TALEs) and non-TALEs, to interfere with the innate immunity of rice. However, few major resistance genes corresponding to Xoc are found in rice cultivations; only Rxo1-AvrRxo1 and Xo1-TALEs interactions have been discovered in rice–Xoc. In this review, we focus on the role of the T3S system (T3SS) in Xoc virulence and consider the reported non-TALEs, including AvrRxo1, AvrBs2, XopN, XopC2, XopAP, and XopAK, as well as TALEs including Tal2g/Tal5d, Tal2h, Tal2a, Tal7, Tal10a, TalI, Tal2b, and Tal2c. Interestingly, AvrRxo1, XopC2, and XopAP disturb stomatal opening to promote infection through targeting diverse signaling pathways in rice. Otherwise, Tal2b and Tal2c, respectively, activate two rice salicylic acid (SA) hydroxylation genes to redundantly suppress the SA-mediated basal defense, and TalI, which has unknown targets, suppresses the SA signaling pathway in rice. In addition, other Xoc virulence factors are discussed. In conclusion, several T3SEs from Xoc interfere with similar defense pathways in rice to achieve invasion, providing an outlook for the control of this disease through manipulating the conserved pathways. Full article

(This article belongs to the Special Issue New Insights into Pest and Disease Control in Rice)

► Show Figures

Figure 1

13 pages, 6554 KiB

Open AccessArticle

The Influence of the Hybrid Compound Nd(NO₃)₃@Zn-MOF on the Growth of Vanilla (Vanilla planifolia Jacks. ex Andrews) Cultured In Vitro: A Preliminary Study

by Carlos Alberto Cruz-Cruz, Xóchitl De Jesús García-Zárate, José Luis Spinoso-Castillo, Rodolfo Peña-Rodríguez, Raúl Colorado-Peralta, Ricardo Sánchez-Páez and Jericó Jabín Bello-Bello

Agronomy 2024, 14(9), 1880; https://doi.org/10.3390/agronomy14091880 (registering DOI) - 23 Aug 2024

Abstract

Hybrid compounds have a significant impact on agriculture as slow macro- and micronutrient administration systems. This study aimed to evaluate the synthesis and effect of the hybrid compound Nd(NO₃)₃@Zn-MOF in different concentrations on the in vitro growth of vanilla (Vanilla planifolia Jacks. ex Andrews). A total of 13 vanilla plantlets per treatment were cultivated in test tubes with semi-solid Murashige and Skoog (MS) medium and without growth regulators and treated with 0, 5, 10, 15, and 30 mg L⁻¹ of Nd(NO₃)₃@Zn-MOF. After 60 days of culture, we evaluated different morphological and biochemical parameters, such as shoot length, root length, the number of roots, the number of leaves, total chlorophyll and carotenoid content, antioxidant capacity, and phenolic compound content. Our results showed that the Nd(NO₃)₃@Zn-MOF at 10 mg L⁻¹ concentration increased plantlet length. Furthermore, we observed an increase in root length and number with the 5 and 10 mg L⁻¹ concentrations, and a decrease in these same parameters with the 15 and 30 mg L⁻¹ Nd(NO₃)₃@Zn-MOF concentrations. There were no significant differences regarding the number of leaves or total chlorophyll content. As for the antioxidant capacity, we observed an increase with 5, 10, and 15 mg L⁻¹ of Nd(NO₃)₃@Zn-MOF and a decrease with the highest concentration. Finally, the phenolic and carotenoid content decreased with the 15 and 30 mg L⁻¹ Nd(NO₃)₃@Zn-MOF concentrations compared to the control. In conclusion, the hybrid compound Nd(NO₃)₃@Zn-MOF showed beneficial effects on the growth, physiology, and biochemistry of V. planifolia in vitro when plants were treated at low concentrations. Additionally, the high concentrations used in this study did not induce toxicity. Our findings suggest that Nd(NO₃)₃@Zn-MOF could be used as a biostimulant in vanilla during its in vitro culture. However, due to the hormetic effect and the possible different reactions of different genotypes, this requires further detailed research. Full article

(This article belongs to the Special Issue Modern In Vitro Technologies for Developing Horticulture)

► Show Figures

Figure 1

19 pages, 2581 KiB

Open AccessArticle

Deep Learning-Based Methods for Multi-Class Rice Disease Detection Using Plant Images

by Yuhai Li, Xiaoyan Chen, Lina Yin and Yue Hu

Agronomy 2024, 14(9), 1879; https://doi.org/10.3390/agronomy14091879 (registering DOI) - 23 Aug 2024

Abstract

Rapid and accurate diagnosis of rice diseases can prevent large-scale outbreaks and reduce pesticide overuse, thereby ensuring rice yield and quality. Existing research typically focuses on a limited number of rice diseases, which makes these studies less applicable to the diverse range of diseases currently affecting rice. Consequently, these studies fail to meet the detection needs of agricultural workers. Additionally, the lack of discussion regarding advanced detection algorithms in current research makes it difficult to determine the optimal application solution. To address these limitations, this study constructs a multi-class rice disease dataset comprising eleven rice diseases and one healthy leaf class. The resulting model is more widely applicable to a variety of diseases. Additionally, we evaluated advanced detection networks and found that DenseNet emerged as the best-performing model with an accuracy of 95.7%, precision of 95.3%, recall of 94.8%, F1 score of 95.0%, and a parameter count of only 6.97 M. Considering the current interest in transfer learning, this study introduced pre-trained weights from the large-scale, multi-class ImageNet dataset into the experiments. Among the tested models, RegNet achieved the best comprehensive performance, with an accuracy of 96.8%, precision of 96.2%, recall of 95.9%, F1 score of 96.0%, and a parameter count of only 3.91 M. Based on the transfer learning-based RegNet model, we developed a rice disease identification app that provides a simple and efficient diagnosis of rice diseases. Full article

(This article belongs to the Special Issue Intelligent Information System for Agriculture Based on Vision Technology)

► Show Figures

Figure 1

18 pages, 7096 KiB

Open AccessArticle

Prune-FSL: Pruning-Based Lightweight Few-Shot Learning for Plant Disease Identification

by Wenbo Yan, Quan Feng, Sen Yang, Jianhua Zhang and Wanxia Yang

Agronomy 2024, 14(9), 1878; https://doi.org/10.3390/agronomy14091878 (registering DOI) - 23 Aug 2024

Abstract

The high performance of deep learning networks relies on large datasets and powerful computational resources. However, collecting enough diseased training samples is a daunting challenge. In addition, existing few-shot learning models tend to suffer from large size, which makes their deployment on edge devices difficult. To address these issues, this study proposes a pruning-based lightweight few-shot learning (Prune-FSL) approach, which aims to utilize a very small number of labeled samples to identify unknown classes of crop diseases and achieve lightweighting of the model. First, the disease few-shot learning model was built through a metric-based meta-learning framework to address the problem of sample scarcity. Second, a slimming pruning method was used to trim the network channels by the γ coefficients of the BN layer to achieve efficient network compression. Finally, a meta-learning pruning strategy was designed to enhance the generalization ability of the model. The experimental results show that with 80% parameter reduction, the Prune-FSL method reduces the Macs computation from 3.52 G to 0.14 G, and the model achieved an accuracy of 77.97% and 90.70% in 5-way 1-shot and 5-way 5-shot, respectively. The performance of the pruned model was also compared with other representative lightweight models, yielding a result that outperforms those of five mainstream lightweight networks, such as Shufflenet. It also achieves 18-year model performance with one-fifth the number of parameters. In addition, this study demonstrated that pruning after sparse pre-training was superior to the strategy of pruning after meta-learning, and this advantage becomes more significant as the network parameters are reduced. In addition, the experiments also showed that the performance of the model decreases as the number of ways increases and increases as the number of shots increases. Overall, this study presents a few-shot learning method for crop disease recognition for edge devices. The method not only has a lower number of parameters and higher performance but also outperforms existing related studies. It provides a feasible technical route for future small-sample disease recognition under edge device conditions. Full article

(This article belongs to the Special Issue Computer Vision and Deep Learning Technology in Agriculture: 2nd Edition)

► Show Figures

Figure 1

15 pages, 750 KiB

Open AccessArticle

Assessing Salinity Tolerance in Pinto Bean Varieties: Implications for Sustainable Agriculture

by Winie S. Paul, Antisar Afkairin, Allan A. Andales, Yaling Qian and Jessica G. Davis

Agronomy 2024, 14(9), 1877; https://doi.org/10.3390/agronomy14091877 (registering DOI) - 23 Aug 2024

Abstract

Salinity is an abiotic stress restricting agricultural crop production globally, in which salts inhibit plants’ ability to absorb water and nutrients. Pinto beans (Phaseolus vulgaris L.) are very important in human nutrition and are sensitive to salinity. The objective of this study was to assess the salinity tolerance of six pinto bean varieties by evaluating the effect of different salt types on germination and growth. In the germination experiment, varieties were arranged in a randomized complete block design with five replications and three saline solutions (NaCl, CaCl₂, MgSO₄·7H₂O) at 0, 0.05 M, 0.1 M, and 0.15 M concentrations each. For the greenhouse experiment, saline solutions with the same EC (5 dS m⁻¹), control (distilled water), and six pinto bean varieties were organized in a Complete Random Design with 10 replicates. The results demonstrated that germination percentage, speed of germination, and hypocotyl length decreased as salt concentrations increased. Othello’s vegetative and reproductive parameters were significantly higher compared to the other varieties under saline conditions; its early maturity may have enabled it to perform better under salt stress. In addition to soil and water management, selecting salt-tolerant crops and varieties is essential to maintaining agricultural sustainability in regions undergoing salinization. Full article

(This article belongs to the Section Soil and Plant Nutrition)

► Show Figures

Figure 1

14 pages, 922 KiB

Open AccessArticle

Deficit Irrigation and High Planting Density Improve Nitrogen Uptake and Use Efficiency of Cotton in Drip Irrigation

by Fengquan Wu, Qiuxiang Tang, Jianping Cui, Liwen Tian, Rensong Guo, Liang Wang and Tao Lin

Agronomy 2024, 14(9), 1876; https://doi.org/10.3390/agronomy14091876 (registering DOI) - 23 Aug 2024

Viewed by 10

Abstract

The optimization of plant density plays a crucial role in cotton production, and deficit irrigation, as a water-saving measure, has been widely adopted in arid regions. However, regulatory mechanisms governing nitrogen absorption, transportation, and nitrogen use efficiency (NUE) in cotton under deficit irrigation and high plant density remain unclear. To clarify the mechanisms of N uptake and NUE of cotton, the main plots were subjected to three irrigation amounts based on field capacity (Fc): (315 [W1, 0.5 Fc], 405 [W2, 0.75 Fc, farmers’ irrigation practice], and 495 mm [W3, 1.0 Fc]). Subplots were planted and applied at three densities: (13.5 [M1], 18.0 [M2, farmers’ planting practice], and 22.5 [M3] plants m⁻²). The results revealed that under low-irrigation conditions, the cotton yield was 5.1% lower than that under the farmer’s irrigation practice. In all plant densities and years, the nitrogen uptake of cotton increased significantly with the increase in irrigation. However, excessive irrigation resulted in nitrogen accumulation and migration, mainly concentrated in the vegetative organs of cotton, which reduced the NUE by 9.2% compared with that under farmers’ irrigation practice. Concerning the interaction between irrigation and plant density, under low irrigation, the nitrogen uptake of high-density planting was higher, and the yield of seed cotton was only 2.9% lower than that of the control (the interaction effect of farmers’ irrigation × plant density), but the NUE was increased by 10.9%. Notably, with the increase in irrigation amount, the soil nitrate nitrogen at the 0–40 cm soil layer decreased, and high irrigation amounts would lead to the transfer of soil nitrate nitrogen to deep soil. With the increase in plant density, the rate of nitrogen uptake and the amount of nitrogen uptake increased, which significantly reduced the soil nitrate nitrogen content. In conclusion, deficit irrigation and high plant density can improve cotton yield and NUE. We anticipate that these findings will facilitate optimized agricultural management in areas with limited water. Full article

(This article belongs to the Topic Advances in Industrial Crops Physioecology and Sustainable Cultivation)

► Show Figures