2026 January - 39 articles
Projects integrating Science, Technology, Engineering, and Mathematics (STEM) are essential to interdisciplinary research. This study presents a STEM (Science, Technology, Engineering, and Mathematics) methodology with the primary objective of design...
Large language models (LLMs) offer new opportunities for agricultural education and decision support, yet their adoption is limited by domain-specific terminology, ambiguous retrieval, and factual inconsistencies. This work presents AgroLLM, a domain...
Facing water scarcity and environmental contamination, a sustainable approach combining bioeconomy and circular economy principles has emerged: the use of onshore oil and gas produced water (PW) to irrigate Nopalea cochenillifera. This study evaluate...
This study compared three deep learning architectures—one-dimensional convolutional neural network (1D-CNN), self-supervised learning (SSL), and Vision Transformer (ViT)—to evaluate their ability to predict carotenoid content from visible...
The temperate Himalayan states of Jammu and Kashmir, Himachal Pradesh, Uttarakhand, Ladakh, Sikkim, and Arunachal Pradesh in India face unique agro-ecological challenges across agriculture and allied sectors, including pest and disease pressures, ine...
Rice–crab co-culture systems (RC) represent promising sustainable intensification approaches, yet their nitrogen (N) cycling and optimal fertilization strategies remain poorly characterized. In this study, we compared RC with rice monoculture s...
Non-destructive estimation of high-temperature stress effects on tobacco plants is crucial for both scientific research and practical applications. Normalized difference vegetation index (NDVI), chlorophyll index, and spectra in the range of 900&ndas...
Greenhouse pesticide application often suffers from low droplet deposition uniformity and health risks to operators. A self-propelled overhead rail air-assisted sprayer has been designed. The mathematical model based on droplet movement and the DPM a...
Sugarcane diseases cause estimated global annual losses of over $5 billion. While deep learning shows promise for disease detection, current approaches lack transparency and confidence estimates, limiting their adoption by agricultural stakeholders....
Weed management has become a critical agricultural practice, as weeds compete with crops for nutrients, host pests and diseases, and cause major economic losses. The invasive weed Ambrosia artemisiifolia (common ragweed) is particularly problematic i...
Early and accurate date palm disease detection is the key to successful smart farming ecosystem sustainability. In this paper, we introduce DoST-DPD, a new Dual-Stream Transformer architecture for multimodal disease diagnosis utilizing RGB, thermal a...
This study aimed to develop a methodology to evaluate, through RGB image processing, the wheat cultivar TRIO Calibre under three irrigation levels (100, 50, and 25%), with or without the application of Bacillus aryabhattai, in Brazilian Cerrado soil....
LoRa-based IoT systems are increasingly used in smart farming, greenhouse monitoring, and large-scale agricultural sensing, where long-range, energy-efficient communication is essential. However, estimating link quality metrics such as PRR, RSSI, and...
To address challenges such as small target size, high density, severe occlusion, complex background interference, and edge device computational constraints, a lightweight model, GAFNet, is proposed based on YOLO11n, optimized for rice pest detection...
Waste valorisation has become a key strategy for applying circular economy principles in the agro-industrial field. This study investigated the territorial implementation of the waste composting on a territorial scale. The wastes considered were the...
Precision agriculture has increasingly adopted controlled agricultural microclimates, particularly smart greenhouses, as a strategy to enhance crop yields while maintaining environmental conditions within suitable ranges for each crop. Among the vari...
This study presents a field-oriented AI web system for durian orchard management that recognizes leaf health from on-orchard images under variable illumination. Two complementary pipelines are employed: (1) a rule-based module operating in HSV and CI...
Early and non-destructive detection of plant-parasitic nematodes is critical for implementing site-specific management in coffee production systems. This study evaluated the potential of unmanned aerial vehicle (UAV) multispectral and thermal imaging...
For high-value-added products such as matcha, scientific confirmation of the origin is essential for quality assurance and fraud prevention. In this study, three nondestructive analytical techniques, specifically fluorescence (FF), near-infrared (NIR...
Plasma-assisted nitrogen fixation is emerging as a promising alternative to the dominant industrial method of the Haber–Bosch (H–B) process, which is energy-intensive and environmentally detrimental. Nonthermal plasma technology represent...
The low level of mechanization in the production process of cumin seeds is one of the primary factors limiting their yield and economic efficiency. To enhance the mechanization of cumin seed production, this study focused on cumin seeds as the resear...
Semantic segmentation of UAV–acquired RGB orthomosaics is a key component for quantifying vegetation cover and monitoring phenology in precision agriculture. This study evaluates a representative set of CNN–based architectures (U–Ne...
Kazakhstan’s growing potato industry underscores the need to develop and apply digital solutions that boost grading efficiency. A comparison between two traditional deep neural network architectures used to classify color images of potatoes fro...
As precision agriculture places higher demands on real-time field weed detection and recognition accuracy, this paper proposes a multi-scale information-enhanced weed detection algorithm, MIE-YOLO (Multi-scale Information Enhanced), for precision agr...
Metal foreign objects left in fields pose a significant challenge during silage harvester operation, leading to reduced mechanical efficiency, compromised feed quality, and risks to livestock safety. However, due to the complex and demanding working...
Prompt identification of clinical signs and early treatment of hoof problems are essential to effectively manage and reduce lameness in dairy farms. This study aimed to evaluate the influence of days in milk (DIM), parity, and milk yield (MY) on the...
Efficient and reliable estimation of rice phenological stages is crucial for improving yield prediction, optimizing irrigation, and guiding fertilization management. Spectral indices (SIs) derived from remote sensing have demonstrated strong potentia...
Small-scale farms produce more than one-third of the world’s food supply, making them a crucial contributor to global food security. In this study, an artificial intelligence of things (AIoT) framework is introduced for smart small-scale farm m...
Landfill leachate reverse osmosis (RO) treatment generates a highly concentrated stream rich in recalcitrant organic matter, particularly humic substances (HS), which present potential for recovery and reuse as a biofertilizer. This study evaluated H...
Low-disturbance liquid manure injection is increasingly important for sustainable soil management because it reduces residue burial, minimizes surface disruption, and lowers energy demand during application. However, the performance of low-disturbanc...
Spatial monitoring of olive systems in arid regions is essential for understanding agricultural expansion, water pressure, and productive sustainability. This study aimed to map coverage and estimate olive plantation density (Olea europaea L.) in the...
Artificial intelligence (AI) shows great promise for transforming the agriculture sector and can enable the development of many modern farming practices over conventional methods. Nowadays, AI agents and agentic AI have attained popularity due to the...
To address the issue of low depth estimation accuracy in complex goji berry orchards, this paper proposes a method for identifying and locating goji berries that combines the YOLO-VitBiS object detection network with stereo vision technology. Based o...
The compression deformation of seed cotton has been identified as a key factor affecting the working reliability of the baling device and the quality of bale molding. However, due to the complex working conditions of seed cotton in the continuous com...
This article examines the pressing issue of verifying contact models in the discrete element method (DEM) for modeling soil tillage processes. Due to the lack of a generally accepted methodology for selecting contact models for various soil types, a...
Accurate and timely crop yield prediction is crucial for global food security and modern agricultural management. Traditional methods often lack the scalability and granularity required for precision farming. This paper introduces FARM (Fine-tuning A...
This study presents a hybrid recurrent neural network (RNN) approach for greenhouse microclimate prediction, combining a mechanistic model with an Elman network. The research addresses the gap in systematic comparisons between hybrid RNN and feedforw...
The use of agrochemicals remains indispensable for ensuring fruit production; however, their excessive or inefficient application poses significant environmental and health concerns. Rapid detection of spray deposition is crucial for assessing spraye...
Digital twins (DTs) are increasingly used in controlled-environment agriculture to model microclimates and drive energy-efficient control. However, long-term drift and seasonal variability require continuous recalibration and controller retuning. We...
