Yingying Xing, Rong Wei, Xiukang Wang^*

Phyton-International Journal of Experimental Botany, Vol.95, No.4, 2026, DOI:10.32604/phyton.2026.078974 - 28 April 2026

Abstract Rhizosphere microorganisms, as crucial biological groups at the soil–plant interface, play a significant role in advancing sustainable agriculture. This review systematically synthesizes three decades of research to elucidate the mechanisms and applications of rhizosphere microbes—including nitrogen-fixing bacteria, phosphate-solubilizing microorganisms, and plant growth–promoting rhizobacteria (PGPR)—in enhancing soil health, improving crop stress tolerance, and optimizing ecosystem functioning. Key findings indicate that replacing 50% of synthetic nitrogen with organic fertilizer in maize–wheat rotation systems can reduce nitrous oxide emissions by up to 68% in loamy soils. Long-term no-till systems enhance carbon sequestration through microbial-driven soil organic matter accumulation.… More >

Zahid Khorshid Abbas^1,#, Anjana Singh^2,#, Mirza Sarwar Baig³, Sulaiman Ali Alharbi⁴, Yussri M. Mahrous⁵, Naif Abdulrhman Zabin Alnefiei¹, Moawia Mukhtar Hassan¹, M. Nasir Khan⁶, Zahid Hameed Siddiqui^1,7,*, Md Salik Noorani^2,*

Phyton-International Journal of Experimental Botany, Vol.95, No.4, 2026, DOI:10.32604/phyton.2026.076432 - 28 April 2026

Abstract Cucumber mosaic virus (CMV) is among the most widespread plant viruses, infecting over a thousand plant species, including Catharanthus roseus, a medicinal plant valued for producing the anticancer alkaloids vincristine and vinblastine. Despite its economic significance, genomic information on CMV infecting C. roseus in India has been lacking. In this study, we employed small RNA deep sequencing integrated with advanced bioinformatics to generate the first complete genome of CMV infecting C. roseus in India, followed by validation through RT-PCR and Sanger sequencing. The reconstructed tripartite CMV genome encodes replication, silencing suppressor, movement, and coat proteins, consistent with known More > Graphic Abstract

Matthew Starr¹, Lori Unruh-Snyder^1,*, Luke Gatiboni¹, Koralalage Jayaratne²

Phyton-International Journal of Experimental Botany, Vol.95, No.4, 2026, DOI:10.32604/phyton.2026.072898 - 28 April 2026

Abstract Biostimulants, categorized as microbial or non-microbial, including humic substances, seaweed extracts, chitosan, or protein hydrolysates (PHs), have gained significant attention in modern agriculture for their ability to enhance crop productivity, improve nutrient use efficiency, and increase resilience to abiotic and biotic stresses, while reducing dependence on conventional agrochemicals. This review synthesizes the historical development, classification, mechanisms of action, and agronomic benefits of biostimulants, with a particular emphasis on PHs, which are mixtures of amino acids, peptides, and polypeptides derived from plant or animal proteins through enzymatic, chemical, or thermal hydrolysis. The concept of biostimulants has… More >

Fengqi Hao^1,2,3, Yawen Hou^2,3, Conghui Gao^2,3, Jinqiang Bai^2,3, Gang Liu⁴, Hoiio Kong^1,*, Xiangjun Dong^1,2,3

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077252 - 09 April 2026

Abstract Field–road classification, a fine-grained form of agricultural machinery operation-mode identification, aims to use Global Navigation Satellite System (GNSS) trajectory data to assign each trajectory point a semantic label indicating whether the machine is performing field work or travelling on roads. Existing methods struggle with highly imbalanced class distributions, noisy measurements, and intricate spatiotemporal dependencies. This paper presents AgroGeoDB-Net, a unified framework that combines a residual BiLSTM backbone with two tightly coupled innovations: (i) a Density-Aware Local Interpolator (DALI), which balances the minority road class via density-aware interpolation while preserving road-segment structure; and (ii) a geometry-aware… More >

A. Sivasangari^1,*, V. J. K. Kishor Sonti¹, J. Cruz Antony¹, E. Murali¹, D. Deepa¹, A. Happonen²

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.074429 - 09 April 2026

Abstract In the past two decades, Precision Agriculture has received research attention since the development of robotics. Agricultural robotic equipment and drones, which can be operated by farmers, are appearing more frequently and being used to make the process of farming easier and more productive. This paper attempts to develop a modified Q-learning algorithm. A reinforcement learning algorithm called Q-learning has Q-values that are updated in order to find the best routes for the robotic devices to follow while avoiding any obstacles. Different types of terrain and other factors that influence the development of good routes… More >

Hajiba Benteima^1,2, Mohamed Ezzaitouni^2,*, Tarik Chileh-Chelh², Carlos Galindo³, José Luis Guil-Guerrero²

Phyton-International Journal of Experimental Botany, Vol.95, No.3, 2026, DOI:10.32604/phyton.2026.079255 - 31 March 2026

Abstract Wild edible plants have evolved in response to persistent and often severe environmental pressures, including salinity, drought, extreme temperatures, high light intensity and nutrient-poor soils. Despite the considerable physiological flexibility and adaptive capacity exhibited by these species, they remain underrepresented in contemporary plant stress research, which has traditionally focused on a limited number of model species and major crops. The present review proposes a conceptual framework that positions wild edible plants as physiological and ecological reference systems for studying naturally evolved plant stress tolerance, rather than as alternative genetic model species. The synthesis of current… More > Graphic Abstract

Changxu Zhao¹, Jianping Liu^1,*, Xiaofeng Wang¹, Wei Sun², Libo Liu³, Haiyu Ren¹, Pan Liu¹, Qiantong Wang¹

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.074409 - 12 March 2026

Abstract Foundation models are reshaping artificial intelligence, yet their deployment in specialised domains such as agricultural question answering (AQA) still faces challenges including data scarcity and barriers to domain-specific knowledge. To systematically review recent progress in this area, this paper adopts a task–paradigm perspective and examines applications across three major AQA task families. For text-based QA, we analyse the strengths and limitations of retrieval-based, generative, and hybrid approaches built on large language models, revealing a clear trend toward hybrid paradigms that balance precision and flexibility. For visual diagnosis, we discuss techniques such as cross-modal alignment and 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 >

Wen-Tsai Sung¹, Indra Griha Tofik Isa^2,3, Sung-Jung Hsiao^4,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-31, 2026, DOI:10.32604/cmc.2025.070922 - 09 December 2025

Abstract Mango is a plant with high economic value in the agricultural industry; thus, it is necessary to maximize the productivity performance of the mango plant, which can be done by implementing artificial intelligence. In this study, a lightweight object detection model will be developed that can detect mango plant conditions based on disease potential, so that it becomes an early detection warning system that has an impact on increasing agricultural productivity. The proposed lightweight model integrates YOLOv7-Tiny and the proposed modules, namely the C2S module. The C2S module consists of three sub-modules such as the… More >

Ali S. Alzaharani, Abid Iqbal^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-27, 2026, DOI:10.32604/cmc.2025.070410 - 10 November 2025

Abstract In this study, an automated multimodal system for detecting, classifying, and dating fruit was developed using a two-stage YOLOv11 pipeline. In the first stage, the YOLOv11 detection model locates individual date fruits in real time by drawing bounding boxes around them. These bounding boxes are subsequently passed to a YOLOv11 classification model, which analyzes cropped images and assigns class labels. An additional counting module automatically tallies the detected fruits, offering a near-instantaneous estimation of quantity. The experimental results suggest high precision and recall for detection, high classification accuracy (across 15 classes), and near-perfect counting in More >