Geoffry Mutiso^*, John Ndia

Journal on Artificial Intelligence, Vol.7, pp. 275-288, 2025, DOI:10.32604/jai.2025.069768 - 05 September 2025

Abstract Tomato leaf diseases significantly reduce crop yield; therefore, early and accurate disease detection is required. Traditional detection methods are laborious and error-prone, particularly in large-scale farms, whereas existing hybrid deep learning models often face computational inefficiencies and poor generalization over diverse environmental and disease conditions. This study presents a unified U-Net-Vision Mamba Model with Hierarchical Bottleneck Attention Mechanism (U-net-Vim-HBAM), which integrates U-Net’s high-resolution segmentation, Vision Mamba’s efficient contextual processing, and a Hierarchical Bottleneck Attention Mechanism to address the challenges of disease detection accuracy, computational complexity, and efficiency in existing models. The model was trained on More >

Lina Song¹, Caijie Yi¹, Shiwei Zhao¹, Yuxin Peng¹, Zijing Li¹, Yuqiang Zhang ², Hua Zhang¹, Helan Qin¹, Huali Zhang^1,*

Phyton-International Journal of Experimental Botany, Vol.94, No.8, pp. 2593-2613, 2025, DOI:10.32604/phyton.2025.068133 - 29 August 2025

Abstract Naturally occurring yellow leaf mutants are an important resource for studying pigment content and biosynthesis, as well as related gene expression. In our ongoing cultivation of Rehmannia chingii H. L. Li, we found an off-type yellow plant. The yellowing started with the new leaves and gradually spread downward until the entire plant exhibited a stable shade of yellow. We studied the differences in the chlorophyll and carotenoid content, carotenoid profile, and transcriptome of this yellow-leaf mutant (P2). Compared to the wild-type R. chingii plant (P1), P2 leaves had significantly lower chlorophyll and carotenoid content. LC-MS/MS analysis revealed More >

Nilüfer Kocak Sahin^*

Phyton-International Journal of Experimental Botany, Vol.94, No.8, pp. 2399-2421, 2025, DOI:10.32604/phyton.2025.067880 - 29 August 2025

Abstract Peanut (Arachis hypogaea) is of international importance as a source of oil and protein. Soil salinity is one of the most significant abiotic stress factors affecting the yield and quality of peanuts. This study evaluated the potential of a seaweed-based biostimulant to enhance emergence and seedling growth of four peanut cultivars (‘Ayse Hanım’, ‘Halis Bey’, ‘NC-7’, and ‘Albenek’) under increasing salinity levels. The experiment was conducted under greenhouse conditions using a randomized complete block design with four replicates. Seeds were sown in trays and treated with two doses of seaweed extract (0 and 5 g L⁻¹) applied… More >

Kaihao Shi^1,2, Lin Yuan^1,2,*, Qimeng Yu³, Zhongting Shen², Yingtan Yu², Chenwei Nie¹, Xingjian Zhou³, Jingcheng Zhang³

Phyton-International Journal of Experimental Botany, Vol.94, No.7, pp. 2033-2054, 2025, DOI:10.32604/phyton.2025.066286 - 31 July 2025

Abstract Plant diseases are a major threat that can severely impact the production of agriculture and forestry. This can lead to the disruption of ecosystem functions and health. With its ability to capture continuous narrow-band spectra, hyperspectral technology has become a crucial tool to monitor crop diseases using remote sensing. However, existing continuous wavelet analysis (CWA) methods suffer from feature redundancy issues, while the continuous wavelet projection algorithm (CWPA), an optimization approach for feature selection, has not been fully validated to monitor plant diseases. This study utilized rice bacterial leaf blight (BLB) as an example by… More >

Antonela Markulj Kulundžić^1,*, Daniela Horvat², Marija Kovačević Babić², Anto Mijić¹, Aleksandra Sudarić¹, Maja Matoša Kočar¹, Tomislav Duvnjak¹, Ivica Liović¹, Ivana Varga³, Marija Viljevac Vuletić^2,*

Phyton-International Journal of Experimental Botany, Vol.94, No.7, pp. 2075-2095, 2025, DOI:10.32604/phyton.2025.065961 - 31 July 2025

Abstract Sunflower leaf photosynthesis strongly depends on the leaf position in the plant stem conditioning, which directly affects other physiological processes. Therefore, a study of the leaf’s physiological status regarding the leaf position in the stem was performed on sunflowers in the flowering stage. Eight differently positioned leaves were investigated, starting with the youngest leaf on the top of the stem to the leaves of the stem bottom, assigned as the oldest senescent leaves. According to chlorophyll fluorescence (ChlF) parameters connected to photosystem II (PSII) processes, significant changes in PSII functioning occurred only in the senescent… More >

Yuchao Xia, Jing Qiu^*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2531-2547, 2025, DOI:10.32604/cmc.2025.064635 - 03 July 2025

Abstract In recent years, fungal diseases affecting grape crops have attracted significant attention. Currently, the assessment of black rot severity mainly depends on the ratio of lesion area to leaf surface area. However, effectively and accurately segmenting leaf lesions presents considerable challenges. Existing grape leaf lesion segmentation models have several limitations, such as a large number of parameters, long training durations, and limited precision in extracting small lesions and boundary details. To address these issues, we propose an enhanced DeepLabv3+ model incorporating Strip Pooling, Content-Guided Fusion, and Convolutional Block Attention Module (SFC_DeepLabv3+), an enhanced lesion segmentation method based… More >

Shamim Ara Bagum¹, Mahbub Ul Islam², M Shalim Uddin^2,*, Sripati Sikder³, Ahmed Gaber⁴, Akbar Hossain^5,*

Phyton-International Journal of Experimental Botany, Vol.94, No.6, pp. 1897-1919, 2025, DOI:10.32604/phyton.2025.065607 - 27 June 2025

Abstract The yield of maize (Zea mays L.) is highly influenced by nitrogen fertilization. This study investigated the impact of nitrogen fertilization on morphophysiological traits in maize (Zea mays L.) and developed algorithms to relate manual phenotyping and digital phenotyping of maize with leaf nitrogen and digital field image traits. The experiment included three hybrid maize varieties, V1 (Hybrid 981), V2 (BARI Hybrid maize-9), and V3 (Hybrid P3396), which were evaluated across three nitrogen levels (N1 = 100 kg N ha⁻¹, N2 = 200 kg N ha⁻¹, N3 = 300 kg N ha⁻¹) in a split-plot design with three replications.… More >

Nouha Haoudi^1,2, Lahcen Hssaini¹, Jamila Bahhou², Abderrahim Bentaibi¹, Hicham Aboumadane^1,2, Rachid Razouk^1,*

Phyton-International Journal of Experimental Botany, Vol.94, No.6, pp. 1857-1877, 2025, DOI:10.32604/phyton.2025.065116 - 27 June 2025

Abstract This study was carried out to assess plasticity to drought of 30 adult fig cultivars, based on a screening of leaf structural and functional traits under sustained deficit irrigation, corresponding to 60% of crop evapotranspiration. All trees, three per cultivar, are planted in an ex-situ collection in Sais plain, northern Morocco. The measurements concerned leaf area, blade thickness, trichomes density, trichome hair length, stomatal density, stomatal dimensions, stomatal area index, chlorophyll concentration index, relative water content, stomatal conductance, leaf temperature, water loss in detached leaves, cuticular wax content, proline content, total phenolic compounds, and total soluble… More >

Rahim Khan¹, Ihsan Rabbi¹, Umar Farooq¹, Jawad Khan^2,*, Fahad Alturise^3,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 827-842, 2025, DOI:10.32604/cmc.2025.063303 - 09 June 2025

Abstract Leaf disease identification is one of the most promising applications of convolutional neural networks (CNNs). This method represents a significant step towards revolutionizing agriculture by enabling the quick and accurate assessment of plant health. In this study, a CNN model was specifically designed and tested to detect and categorize diseases on fig tree leaves. The researchers utilized a dataset of 3422 images, divided into four classes: healthy, fig rust, fig mosaic, and anthracnose. These diseases can significantly reduce the yield and quality of fig tree fruit. The objective of this research is to develop a… More >

Tajinder Kumar¹, Sarbjit Kaur², Purushottam Sharma^3,*, Ankita Chhikara⁴, Xiaochun Cheng^5,*, Sachin Lalar⁶, Vikram Verma⁷

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5219-5234, 2025, DOI:10.32604/cmc.2025.062010 - 19 May 2025

Abstract During its growth stage, the plant is exposed to various diseases. Detection and early detection of crop diseases is a major challenge in the horticulture industry. Crop infections can harm total crop yield and reduce farmers’ income if not identified early. Today’s approved method involves a professional plant pathologist to diagnose the disease by visual inspection of the afflicted plant leaves. This is an excellent use case for Community Assessment and Treatment Services (CATS) due to the lengthy manual disease diagnosis process and the accuracy of identification is directly proportional to the skills of pathologists.… More >