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 >

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 >

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 >

Mukesh Kumar Verma^1,2,*, Manohar Yadav¹

Revue Internationale de Géomatique, Vol.34, pp. 855-879, 2025, DOI:10.32604/rig.2025.069914 - 17 November 2025

Abstract Precision Agriculture (PA) is revolutionizing modern farming by leveraging remote sensing (RS) technologies for continuous, non-destructive crop monitoring. This review comprehensively explores RS systems categorized by platform—terrestrial, airborne, and space-borne—and evaluates the role of multi-sensor fusion in addressing the spatial and temporal complexity of agricultural environments. Emphasis is placed on data from LiDAR, GNSS, cameras, and radar, alongside derived metrics such as plant height, projected leaf area, and biomass. The study also highlights the significance of data processing methods, particularly machine learning (ML) and deep learning (DL), in extracting actionable insights from large datasets. By More >

Siwakorn Banluesapy, Mahasak Ketcham^*, Montean Rattanasiriwongwut

Energy Engineering, Vol.122, No.10, pp. 4261-4296, 2025, DOI:10.32604/ee.2025.068422 - 30 September 2025

Abstract Traditional agricultural irrigation systems waste significant amounts of water and energy due to inefficient scheduling and the absence of real-time monitoring capabilities. This research developed a comprehensive IoT-based smart irrigation control system to optimize water and energy management in agricultural greenhouses while enhancing crop productivity. The system employs a sophisticated four-layer Internet of Things (IoT) architecture based on an ESP32 microcontroller, integrated with multiple environmental sensors, including soil moisture, temperature, humidity, and light intensity sensors, for comprehensive environmental monitoring. The system utilizes the Message Queuing Telemetry Transport (MQTT) communication protocol for reliable data transmission and… 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 >

Sameera V Mohd Sagheer¹, Orwel P V², P M Ameer³, Amal BaQais⁴, Shaeen Kalathil^5,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1329-1349, 2025, DOI:10.32604/cmc.2025.063961 - 09 June 2025

Abstract The precise identification of date palm tree diseases is essential for maintaining agricultural productivity and promoting sustainable farming methods. Conventional approaches rely on visual examination by experts to detect infected palm leaves, which is time intensive and susceptible to mistakes. This study proposes an automated leaf classification system that uses deep learning algorithms to identify and categorize diseases in date palm tree leaves with high precision and dependability. The system leverages pretrained convolutional neural network architectures (InceptionV3, DenseNet, and MobileNet) to extract and examine leaf characteristics for classification purposes. A publicly accessible dataset comprising multiple… More >

Wafaa H. Alwan^1,*, Sabah M. Alturfi²

Computer Systems Science and Engineering, Vol.49, pp. 419-434, 2025, DOI:10.32604/csse.2025.064195 - 30 April 2025

Abstract Plant diseases pose a significant challenge to global agricultural productivity, necessitating efficient and precise diagnostic systems for early intervention and mitigation. In this study, we propose a novel hybrid framework that integrates EfficientNet-B8, Vision Transformer (ViT), and Knowledge Graph Fusion (KGF) to enhance plant disease classification across 38 distinct disease categories. The proposed framework leverages deep learning and semantic enrichment to improve classification accuracy and interpretability. EfficientNet-B8, a convolutional neural network (CNN) with optimized depth and width scaling, captures fine-grained spatial details in high-resolution plant images, aiding in the detection of subtle disease symptoms. In… More >

Deepak Kumar¹, Vinay Kukreja¹, Ayush Dogra^1,*, Bhawna Goyal², Talal Taha Ali³

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2097-2121, 2023, DOI:10.32604/cmc.2023.044287 - 29 November 2023

Abstract Wheat rust diseases are one of the major types of fungal diseases that cause substantial yield quality losses of 15%–20% every year. The wheat rust diseases are identified either through experienced evaluators or computerassisted techniques. The experienced evaluators take time to identify the disease which is highly laborious and too costly. If wheat rust diseases are predicted at the development stages, then fungicides are sprayed earlier which helps to increase wheat yield quality. To solve the experienced evaluator issues, a combined region extraction and cross-entropy support vector machine (CE-SVM) model is proposed for wheat rust More >