Ching-Lung Fan^1,*, Yu-Jen Chung², Shan-Min Yen^1,3

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4627-4653, 2025, DOI:10.32604/cmc.2025.066856 - 30 July 2025

Abstract Efficient banana crop detection is crucial for precision agriculture; however, traditional remote sensing methods often lack the spatial resolution required for accurate identification. This study utilizes low-altitude Unmanned Aerial Vehicle (UAV) images and deep learning-based object detection models to enhance banana plant detection. A comparative analysis of Faster Region-Based Convolutional Neural Network (Faster R-CNN), You Only Look Once Version 3 (YOLOv3), Retina Network (RetinaNet), and Single Shot MultiBox Detector (SSD) was conducted to evaluate their effectiveness. Results show that RetinaNet achieved the highest detection accuracy, with a precision of 96.67%, a recall of 71.67%, and… More >

Fatma Bunyan Unel^1,*, Lutfiye Kusak¹, Murat Yakar¹, Abdullah Sahin², Hakan Dogan³, Fikret Demir⁴

Revue Internationale de Géomatique, Vol.34, pp. 433-460, 2025, DOI:10.32604/rig.2025.065502 - 29 July 2025

Abstract This study aimed to create a model for calculating the total reference crop evapotranspiration (ETo) in Mersin Province from May 2015 to 2020 and to generate maps using spatial analysis. Lemons from citrus play a significant role in Mersin’s agriculture, and because of lemons’ sensitivity to temperature, ETo is essential for them. It was observed that the ETo value () calculated using the Penman-Monteith (PM) method increased over the years. A model was developed using data from 36 Automated Weather Observing Systems (AWOS) in Mersin, Türkiye, which is located in a semi-arid climate zone. The… More >

Jingli Li, Xianting Fu, Yuheng Wen, Hailang Xu, Qian Liao, Deng-Guang Yu^*, Wenliang Song^*

Journal of Polymer Materials, Vol.42, No.2, pp. 435-448, 2025, DOI:10.32604/jpm.2025.064290 - 14 July 2025

Abstract Porous organic polymers are remarkably versatile materials with porous and carefully designed structures. They complement traditional preservation methods by overcoming their limitations and significantly extending the shelf life of preserved products. Notably, porous hollow nanospheres (PHNs), with their unique hollow structures capable of adsorbing and releasing organic molecules, have garnered considerable attention in food preservation. However, most PHNs are challenging to synthesize in one step, and PHNs are usually in powder form, which makes it challenging to apply them directly. In this study, we successfully synthesized PHNs in one step using the Friedel–Crafts reaction. The… More > Graphic Abstract

Min Ji^*, Shucheng Yang

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3075-3103, 2025, DOI:10.32604/cmc.2025.064166 - 03 July 2025

Abstract In agricultural production, crop images are commonly used for the classification and identification of various crops. However, several challenges arise, including low image clarity, elevated noise levels, low accuracy, and poor robustness of existing classification models. To address these issues, this research proposes an innovative crop image classification model named Lap-FEHRNet, which integrates a Laplacian Pyramid Super Resolution Network (LapSRN) with a feature enhancement high-resolution network based on attention mechanisms (FEHRNet). To mitigate noise interference, this research incorporates the LapSRN network, which utilizes a Laplacian pyramid structure to extract multi-level feature details from low-resolution images… More >

Shivani Sood¹, Harjeet Singh^2,*, Surbhi Bhatia Khan^3,4,5,*, Ahlam Almusharraf⁶

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2751-2787, 2025, DOI:10.32604/cmc.2025.060185 - 03 July 2025

Abstract Wheat fungal infections pose a danger to the grain quality and crop productivity. Thus, prompt and precise diagnosis is essential for efficient crop management. This study used the WFD2020 image dataset, which is available to everyone, to look into how deep learning models could be used to find powdery mildew, leaf rust, and yellow rust, which are three common fungal diseases in Punjab, India. We changed a few hyperparameters to test TensorFlow-based models, such as SSD and Faster R-CNN with ResNet50, ResNet101, and ResNet152 as backbones. Faster R-CNN with ResNet50 achieved a mean average precision More >

Fatou Balleh Jobe¹, Satyanarayana Narra^1,*, Vidhi Singh¹, Komi Agboka²

Energy Engineering, Vol.122, No.7, pp. 2539-2569, 2025, DOI:10.32604/ee.2025.061963 - 27 June 2025

Abstract This study evaluates the feasibility of incorporating alternative sustainable energy sources, specifically bioenergy and green hydrogen, into The Gambia’s energy mix to support the nation’s long-term energy development goals. The feedstocks analyzed include agricultural crop residues such as rice, cassava, groundnuts, maize, sorghum, oil palm fruit, seed cotton, and millet, as well as municipal solid waste (MSW). An assessment was conducted to calculate the theoretical potential generated from the organic components of both MSW and crop residues, utilizing data collected from 2017 to 2021 and projections extending to 2038. The results were employed to calculate… More >

Dao Yao^1,#, Junming Zhou^1,#, Yashuo Wang¹, Yuxin Li¹, Wenge Cheng², Xiaoyu Lu^2,*, Huijing Liu^1,*

Phyton-International Journal of Experimental Botany, Vol.94, No.6, pp. 1669-1694, 2025, DOI:10.32604/phyton.2025.064344 - 27 June 2025

Abstract Clustered regularly interspaced short palindromic repeat sequences (CRISPR) and their accompanying proteins (Cas), commonly presenting in bacteria and archaea, make up the CRISPR/Cas system. As one of the fundamental sources of nutrition for humans, edible crops play a crucial role in ensuring global food security. CRISPR/Cas9 gene editing has been applied to improve many crop traits, such as increasing nitrogen utilization efficiency, creating male sterile germplasm, and regulating tiller and spikelet formation. This paper provides a comprehensive overview of the use of CRISPR/Cas gene editing technology in crop genomes, covering the targeted genes, the types More >

Krisna Rani Sarker¹, Tahsina Sharmin Hoque^1,*, Nusrat Jahan Mim¹, Md. Anwarul Abedin¹, Md. Anamul Hoque¹, Ahmed Gaber², Mohammed M. Althaqafi³, Mohammad Anwar Hossain^4,*

Phyton-International Journal of Experimental Botany, Vol.94, No.6, pp. 1711-1733, 2025, DOI:10.32604/phyton.2025.062465 - 27 June 2025

Abstract Improving crop productivity and soil fertility through the balanced application of inorganic and organic nutrient sources is a sustainable approach in modern agriculture. Char land soils, widely distributed in riverine Bangladesh, are generally low in organic matter status and deficient in necessary nutrient elements for crop production. Addressing this challenge, the present study was conducted to investigate the effects of various organic nutrient sources with inorganic fertilizers on crop yields, nutrient uptake, and soil fertility in farm (L₁) and char land (L₂) of Brahmaputra River in Mymensingh, Bangladesh from 2022 (Y₁) to 2023 (Y₂). For each location,… More >

JIAJIA LV, XIAOYOU ZHONG, LIN WANG, WEIFEI FAN^*

Oncology Research, Vol.33, No.7, pp. 1581-1592, 2025, DOI:10.32604/or.2025.060063 - 26 June 2025

Abstract The tumor microenvironment (TME) is a complex and dynamic network comprised of tumor cells, surrounding cellular components, various signaling molecules, and the stroma. Myeloid-derived suppressor cells (MDSCs) are pivotal players in the immunosuppressive landscape of the TME, effectively hindering antitumor immune responses and facilitating tumor progression. Originating from pathologically activated myeloid precursors and relatively immature myeloid cells, MDSCs retain plasticity to further differentiate into other myeloid cells, such as macrophages or dendritic cells, which underpins their heterogeneity and adaptability in response to the TME. In this review, we delve into the plasticity of MDSCs in More >

Hussam Qushtom, Ahmad Hasasneh^*, Sari Masri

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1379-1395, 2025, DOI:10.32604/cmc.2025.061995 - 09 June 2025

Abstract This study presents an enhanced convolutional neural network (CNN) model integrated with Explainable Artificial Intelligence (XAI) techniques for accurate prediction and interpretation of wheat crop diseases. The aim is to streamline the detection process while offering transparent insights into the model’s decision-making to support effective disease management. To evaluate the model, a dataset was collected from wheat fields in Kotli, Azad Kashmir, Pakistan, and tested across multiple data splits. The proposed model demonstrates improved stability, faster convergence, and higher classification accuracy. The results show significant improvements in prediction accuracy and stability compared to prior works,… More >