Alexis Salazar-Navarro¹, Victor Ruiz-Valdiviezo², Jose Joya-Dávila³, Daniel Gonzalez-Mendoza^1,*

Phyton-International Journal of Experimental Botany, Vol.93, No.5, pp. 923-949, 2024, DOI:10.32604/phyton.2024.049612

Abstract Coffee Leaf Rust (CLR) is caused by Hemileia vastatrix in Coffea spp. It is one of the most dangerous phytopathogens for coffee plantations in terms of coffee productivity and coffee cup quality. In this review, we resume the problem of CLR in Mexico and the pathogenesis of H. vastatrix. The review abord plant-pathogen interactions which lead a compatible or incompatible interactions and result in CLR disease or resistance, respectively. The review abord Coffea spp. defense response pathways involved in H. vastatrix pathogenicity. Additionally, current measures to control H. vastatrix proliferation and germination were aborded focused on phytosanitary actions, and biological More >

Jingcheng Zhang¹, Xingjian Zhou¹, Dong Shen¹, Qimeng Yu¹, Lin Yuan^2,*, Yingying Dong³

Phyton-International Journal of Experimental Botany, Vol.93, No.4, pp. 745-762, 2024, DOI:10.32604/phyton.2024.049734

Abstract As an important rice disease, rice bacterial leaf blight (RBLB, caused by the bacterium Xanthomonas oryzae pv. oryzae), has become widespread in east China in recent years. Significant losses in rice yield occurred as a result of the disease’s epidemic, making it imperative to monitor RBLB at a large scale. With the development of remote sensing technology, the broad-band sensors equipped with red-edge channels over multiple spatial resolutions offer numerous available data for large-scale monitoring of rice diseases. However, RBLB is characterized by rapid dispersal under suitable conditions, making it difficult to track the disease at… More >

Jiaqi Lang¹, Mingyan Ye¹, Ya Luo¹, Yueheng Wang¹, Zhifen Shi^1,2, Xiaotian Kong^1,3, Xuan Li¹, Nong Zhou^1,*

Phyton-International Journal of Experimental Botany, Vol.93, No.4, pp. 789-806, 2024, DOI:10.32604/phyton.2024.049088

Abstract This study aimed to examine the effects of inoculating Fritillaria taipaiensis P.Y.Li leaves with different strains of potassium-solubilizing bacteria (KSB), or combinations thereof, focusing on aspects of photosynthesis and physiological and biochemical characteristics. At present, some studies have only studied the rhizosphere microbial community characteristics of F. taipaiensis and have not discussed the effects of different microbial species on the growth promotion of F. taipaiensis. This paper will start from the perspective of potassium-solubilizing bacteria to conduct an in-depth study. Seed cultivation commenced at the base with three different KSBs in early October 2022. The growth of F. taipaiensis… More >

Jiana Chen^1,2, Fangbo Cao^1,2, Min Huang^1,2,*

Phyton-International Journal of Experimental Botany, Vol.93, No.4, pp. 807-817, 2024, DOI:10.32604/phyton.2024.048299

Abstract Transplanting rice varieties grown in different seasons can lead to different yields due to different dry matter production. Early-season rice varieties transplanted in the late season can obtain high yields with short-growth duration and higher yields driven by higher dry matter production. To make clear the variations in dry matter production across seasons, four early-season rice varieties were chosen for late-season transplantation. The grain yield, dry matter accumulation, leaf photosynthetic, and leaf stomatal properties were studied. It was observed that the average yields of these four varieties in the late season were 33% greater, despite… More >

Wissal Saadellaoui¹, Samiha Kahlaoui¹, Kheiria Hcini¹, Abir Haddada¹, Noomene Sleimi^2,*, Roberta Ascrizzi³, Guido Flamini³, Fethia Harzallah-Skhiri⁴, Sondes Stambouli-Essassi¹

Phyton-International Journal of Experimental Botany, Vol.93, No.4, pp. 725-744, 2024, DOI:10.32604/phyton.2024.048110

Abstract In this work, we investigated aroma volatiles emanated by dry roots, stems, leaves, flowers, and fruits of Cardaria draba (L.) Desv. growing wild in Tunisia and its aerial part essential oils (EOs) composition. A total of 37 volatile organic compounds (96.7%–98.9%) were identified; 4 esters, 4 alcohols, 7 hydrocarbons, 12 aldehydes, 5 ketones, 1 lactone, 1 organosulfur compound, 2 organonitrogen compounds, and 1 acid. The hydrocarbons form the main group, representing 49.5%–84.6% of the total detected volatiles. The main constituent was 2,2,4,6,6-pentamethylheptane (44.5%–76.2%) reaching the highest relative percentages. Forty-two compounds were determined in the two fractions… More >

Yong Zhao^1,#, Yaxuan Jiang^3,#, Li Wen¹, Rui Luo², Guorui Li², Jianjun Di², Mingda Yin², Zhiyan Wang², Fenglan Huang^2,4,5,6,7,*, Fanjuan Meng^3,*

Phyton-International Journal of Experimental Botany, Vol.93, No.4, pp. 713-723, 2024, DOI:10.32604/phyton.2024.047657

Abstract The yield of castor is influenced by the type of inflorescence and the proportion of female flowers. However, there are few studies on the genetic mechanism involved in the development and differentiation of castor inflorescences. In this study, we performed transcriptomic analyses of three different phenotypes of inflorescences at the five-leaf stage. In comparison to the MI (complete pistil without willow leaves), 290 and 89 differentially expressed genes (DEGs) were found in the SFI (complete pistil with willow leaves) and the BI (monoecious inflorescence), respectively. Among the DEGs, 104 and 88 were upregulated in the… More >

Ankita Gangwar¹, Vijaypal Singh Dhaka¹, Geeta Rani^2,*, Shrey Khandelwal¹, Ester Zumpano^3,4, Eugenio Vocaturo^3,4

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 117-142, 2024, DOI:10.32604/cmc.2024.048119

Abstract A consumption of 46.9 million tons of processed tomatoes was reported in 2022 which is merely 20% of the total consumption. An increase of 3.3% in consumption is predicted from 2024 to 2032. Tomatoes are also rich in iron, potassium, antioxidant lycopene, vitamins A, C and K which are important for preventing cancer, and maintaining blood pressure and glucose levels. Thus, tomatoes are globally important due to their widespread usage and nutritional value. To face the high demand for tomatoes, it is mandatory to investigate the causes of crop loss and minimize them. Diseases are… More >

B. BRAILSON MANSINGH¹, K. L. NARASIMHAMU², K. C. VARAPRASAD³, J. S. BINOJ^4,*, A. RADHAKRISHNAN⁵, ALAMRY ALI⁶

Journal of Polymer Materials, Vol.40, No.1-2, pp. 71-82, 2023, DOI:10.32381/JPM.2023.40.1-2.6

Abstract Several agro-wastes are rich in natural fibers and finds scope to be used as reinforcement in composite industry. These natural fibers have some advantages over man-made fibers, including low cost, light weight, renewable nature, high specific strength and modulus, and availability in various forms worldwide. In this paper, the effect of surface modification of leaf sheath coconut fiber (LSF) (an agro-waste) reinforced in phenol formaldehyde matrix composites with silicon carbide (SiC) nano particles as filler material were investigated for its mechanical characteristics. The investigation portrays that coconut LSF (CLSF) modified with potassium permanganate reinforced polymer More >

Mahmood A. Mahmood^1,2,*, Khalaf Alsalem¹

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3431-3448, 2024, DOI:10.32604/cmc.2024.047604

Abstract Olive trees are susceptible to a variety of diseases that can cause significant crop damage and economic losses. Early detection of these diseases is essential for effective management. We propose a novel transformed wavelet, feature-fused, pre-trained deep learning model for detecting olive leaf diseases. The proposed model combines wavelet transforms with pre-trained deep-learning models to extract discriminative features from olive leaf images. The model has four main phases: preprocessing using data augmentation, three-level wavelet transformation, learning using pre-trained deep learning models, and a fused deep learning model. In the preprocessing phase, the image dataset is… More >

Chirag Chandrashekar¹, K. P. Vijayakumar^1,*, K. Pradeep¹, A. Balasundaram^1,2

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2511-2533, 2024, DOI:10.32604/cmc.2024.047697

Abstract The most widely farmed fruit in the world is mango. Both the production and quality of the mangoes are hampered by many diseases. These diseases need to be effectively controlled and mitigated. Therefore, a quick and accurate diagnosis of the disorders is essential. Deep convolutional neural networks, renowned for their independence in feature extraction, have established their value in numerous detection and classification tasks. However, it requires large training datasets and several parameters that need careful adjustment. The proposed Modified Dense Convolutional Network (MDCN) provides a successful classification scheme for plant diseases affecting mango leaves. More >