Sang-Hyun Lee^*, Qingtao Meng

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077207 - 09 April 2026

Abstract Accurate and timely detection of rice leaf diseases is critical for ensuring global food security and maximizing agricultural yields. However, existing deep learning methods often struggle to balance the high accuracy required for detecting multi-scale lesions in complex field environments with the computational efficiency necessary for edge device deployment. This paper proposes You Only Look Once for Lightweight Detection (YOLOv11-LD), a lightweight object detection model for multi-scale rice leaf disease detection in real paddy field environments. The model is built on YOLOv11n and integrates a Re-parameterized Vision Transformer (RepViT) backbone, a Bidirectional Feature Pyramid Network… More >

Ruqayah A. Ulwali, Nada K. Abbas^*

Chalcogenide Letters, Vol.23, No.3, 2026, DOI:10.32604/cl.2026.078934 - 03 April 2026

Abstract In this study, zinc sulfide nanoparticles (ZnS NPs) and zinc sulfide @ Silver sulfide core-shells (ZnS@Ag₂S NPs) were prepared using the green method with mint leaf extract as a reducing and coating agent, at varying concentrations of silver nitrate (AgNO₃) (0.005, 0.01, and 0.02 M). X-ray diffraction analysis (XRD) results showed the formation of a cubic phase of ZnS NPs and a monoclinic phase of Ag₂S with increasing silver nitrate concentration. The average crystalline size of ZnS NPs was calculated to be 2.01 nm and (2.78, 2.65, and 2.13 nm) after Ag₂S formation, while the shell (Ag₂S) was… More >

Achille Désiré Betené Omgba^1,2,*, Cheryle Manfouo Tchoupmene¹, Benoit Ndiwe^1,2,*, Antonios N. Papadopoulos³, Remy Legrand Ndoumou Belinga¹, Julien Clerc Obam¹, Christel Cedrig Laris Nsi Ongo¹, Ioanna A. Papadopoulou⁴, Armel Brice Mvogo^1,2, Fabien Betené Ebanda^1,2, Atangana Ateba^1,2, Antonio Pizzi⁵

Journal of Renewable Materials, Vol.14, No.3, 2026, DOI:10.32604/jrm.2026.02025-0201 - 25 March 2026

Abstract The high hydrophilicity of pineapple leaf fibres (PALF) limits their use in cement- and gypsum-based composites exposed to moisture. This study evaluates, for the first time, the combined effect of palm kernel oil and beeswax on the hygroscopic resistance and mechanical stability of PALF. The fibres were functionalised with three formulations (oil, wax, and a 1:2 oil/wax blend) applied at different mass ratios (CR = 0.5–2). Treatments increased the average bundle diameter by up to +46% (238 μm) and reduced density down to 1.06 g/cm³. Hygroscopically, water absorption decreased from 202.4% (raw fibres) to 76.3% (CR… More > Graphic Abstract

Qingwen Yang¹, Zhi Li², Jiawei Tang¹, Yanyi Liu¹, Tiezheng Guo¹, Yingyou Wen^1,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.074384 - 12 March 2026

Abstract Deploying Large Language Model (LLM)-based agents in the Industrial Internet of Things (IIoT) presents significant challenges, including high latency from cloud-based APIs, data privacy concerns, and the infeasibility of deploying monolithic models on resource-constrained edge devices. While smaller models (SLMs) are suitable for edge deployment, they often lack the reasoning power for complex, multi-step tasks. To address these issues, this paper introduces LEAF, a Lightweight Edge Agent Framework designed for efficiently executing complex tasks at the edge. LEAF employs a novel architecture where multiple expert SLMs—specialized for planning, execution, and interaction—work in concert, decomposing complex… More >

Meng Wang¹, Jinghan Cai¹, Wenzheng Liu¹, Xue Yang¹, Jingjing Zhang¹, Qiangmin Zhou¹, Fanzhen Wang¹, Hang Zhang^1,*, Tonghai Liu^2,*

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2025.075541 - 30 January 2026

Abstract Tomato is a major economic crop worldwide, and diseases on tomato leaves can significantly reduce both yield and quality. Traditional manual inspection is inefficient and highly subjective, making it difficult to meet the requirements of early disease identification in complex natural environments. To address this issue, this study proposes an improved YOLO11-based model, YOLO-SPDNet (Scale Sequence Fusion, Position-Channel Attention, and Dual Enhancement Network). The model integrates the SEAM (Self-Ensembling Attention Mechanism) semantic enhancement module, the MLCA (Mixed Local Channel Attention) lightweight attention mechanism, and the SPA (Scale-Position-Detail Awareness) module composed of SSFF (Scale Sequence Feature… More >

Wonkyu Yi, Jongseok Park^*

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2026.073450 - 30 January 2026

Abstract Understanding plant responses under low-pressure conditions is important for developing closed cultivation systems that simulate space environments. This study aimed to assess the effects of different pressure levels on growth, photosynthesis, and secondary metabolite accumulation in red leaf lettuce (Lactuca sativa L. var. ‘Super Caesar’s Red’). Plants were cultivated for three weeks in sealed chambers under 101 kPa (atmospheric pressure), 66 kPa (moderate low pressure), and 33 kPa (severe low pressure). Growth analysis showed that leaf length and leaf area decreased significantly with reduced pressure, while chlorophyll content and SPAD values increased gradually. Photosynthetic measurements indicated More >

Seerat Singla¹, Gunjan Shandilya¹, Ayman Altameem², Ruby Pant³, Ajay Kumar⁴, Ateeq Ur Rehman^5,*, Ahmad Almogren^6,*

Phyton-International Journal of Experimental Botany, Vol.94, No.12, pp. 4021-4057, 2025, DOI:10.32604/phyton.2025.073354 - 29 December 2025

Abstract Turmeric Leaf diseases pose a major threat to turmeric cultivation, causing significant yield loss and economic impact. Early and accurate identification of these diseases is essential for effective crop management and timely intervention. This study proposes DenseSwinGNNNet, a hybrid deep learning framework that integrates DenseNet-121, the Swin Transformer, and a Graph Neural Network (GNN) to enhance the classification of turmeric leaf conditions. DenseNet121 extracts discriminative low-level features, the Swin Transformer captures long-range contextual relationships through hierarchical self-attention, and the GNN models inter-feature dependencies to refine the final representation. A total of 4361 images from the… More >

Jie Jin¹, Tianci Wang¹, Yaning Wang¹, Jingqi Yao², Jinxiu Song^1,*

Phyton-International Journal of Experimental Botany, Vol.94, No.11, pp. 3611-3632, 2025, DOI:10.32604/phyton.2025.070680 - 01 December 2025

Abstract In plant factory with artificial lighting, precise regulation of environmental and nutritional factors is essential to optimize both growth and quality of leafy vegetables. This study systematically evaluated the combined effects of light intensity (150, 200, 250 μmol/(m²·s)) and calcium supply in the nutrient solution (0.5, 1.0, 1.5 mmol/L) on lettuce morphology, photosynthesis, quality indices, and tipburn incidence. Elevating light from 150 to 200 μmol/(m²·s) significantly enhanced leaf number, area, photosynthetic rate, biomass, and foliar calcium. These gains plateaued at 250 μmol/(m²·s), where tipburn incidence surged to 76.5%. Photosynthetic pigments progressively rose with light intensity. Calcium supply… More >

Heng Zhang^1,#, Meng Wang^1,#, Dong Zeng^1,2, Yunbo Xu¹, Dongyuan Guo¹, Xuanchen Liu^1,3, Zhanqi Ren⁴, Jinzi Zhang¹, Yuhang Liu¹, Qiuyu Wang¹, Shuo Yu¹, Guanzheng Qu^1,*

Phyton-International Journal of Experimental Botany, Vol.94, No.10, pp. 3205-3216, 2025, DOI:10.32604/phyton.2025.069994 - 29 October 2025

Abstract Hybridization remains an important method for breeding new poplar varieties. It results in significant variation in leaf phenotype among parents and offspring, and among offspring themselves. This study aimed to investigate whether leaf shape variations were similar in offspring produced from reciprocal crosses. Specifically, two hybrid combinations were produced: the direct cross with Populus ussuriensis as the maternal parent and P. simonii × P. nigra as the paternal parent (HY53), and the reciprocal cross with P. simonii × P. nigra as the maternal parent and P. ussuriensis as the paternal parent (HY268). Using 3-month-old rooted cuttings from 40 clones (36 F₁ hybrids… More >

Nameer Baht^1,*, Enrique Domínguez^1,2,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4441-4455, 2025, DOI:10.32604/cmc.2025.069528 - 23 October 2025

Abstract Coffee is an important agricultural commodity, and its production is threatened by various diseases. It is also a source of concern for coffee-exporting countries, which is causing them to rethink their strategies for the future. Maintaining crop production requires early diagnosis. Notably, Coffee Leaf Miner (CLM) Machine learning (ML) offers promising tools for automated disease detection. Early detection of CLM is crucial for minimising yield losses. However, this study explores the effectiveness of using Convolutional Neural Networks (CNNs) with transfer learning algorithms ResNet50, DenseNet121, MobileNet, Inception, and hybrid VGG19 for classifying coffee leaf images as… More >