2025 December - 40 articles
Accurate seed singulation is critical for uniform crop establishment and yield optimization in precision agriculture. This study presents the development and evaluation of a Hall sensor-based Seed Miss Prevention System (SMPS) integrated into a long-...
The coffee leaf miner (Leucoptera coffeella) is a major pest of coffee crops and can cause significant economic losses. Early monitoring is essential to support decision-making for its control. This study aimed to evaluate the potential of fuzzy logi...
Monitoring groundcover diversity in vineyards is a complex task, often limited by the time and expertise required for accurate botanical identification. Remote sensing technologies and AI-based tools are still underutilized in this context, particula...
This study investigates the performance of unit-element heat exchangers. Particularly, it focuses on the characteristics of the local air temperature profiles and heat transfer performance of serpentine copper pipe heat exchangers with different diam...
Plant diseases are currently a major threat to agricultural economies and food availability, having a negative environmental impact. Despite being a promising line of research, current approaches struggle with poor cross-site generalization, limited...
Unmanned Aerial Vehicles (UAVs) have become essential instruments for precision agriculture and forest monitoring, offering rapid, high-resolution data collection over wide areas. This review synthesizes global advances (2015–2024) in UAV-deriv...
Agriculture faces growing pressure to optimize water use, particularly in woody perennial crops where irrigation systems are installed once and seldom redesigned despite changes in canopy structure, soil conditions, or plant mortality. Such static la...
The article presents a method for determining the irrigation requirements of crops based on soil moisture. The proposed approach enables scheduling irrigation at the most appropriate time of day by combining current soil moisture measurements with fo...
This study explores the potential of hyperspectral imaging combined with machine learning techniques to provide accurate and non-invasive methods for analyzing soil nutrient content in precision agriculture. Data were collected from agricultural regi...
Livestock farming represents one of the primary sources of ammonia (NH3) and greenhouse gas (GHG) emissions, including methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2), having a significant environmental impact. Reducing emissions and rec...
Under the condition of high-speed maize seeding, the collision between the seeds and the restraint seeding guide device, as well as the excessively high seeding speed, will lead to a sharp increase in the coefficient of variation in the seed spacing...
Given the demand for more and better food by 2050, the use of technologies in agricultural activities is the most appropriate way to strengthen the sector; however, their adoption remains a milestone for small-scale agriculture. Currently, GenAI, est...
In response to the urgent need for full-process mechanization in Xinjiang’s cotton–cumin intercropping system, and to address the prominent bottlenecks of missing equipment for key harvesting steps and reliance on manual operations, we de...
High-quality soybean seeds possess genetic, physical, and physiological characteristics that directly influence crop yield. The use of hyperspectral sensors combined with machine learning (ML) can streamline and accelerate seed germination testing. T...
The olive fruit fly Bactrocera oleae (Rossi) (Diptera: Tephritidae) is the primary pest of olive crop globally, causing serious economic losses each year. Early and accurate detection of this pest is essential for implementing integrated management s...
This study aimed to develop a deep learning-based application for the automatic detection of nutritional deficiencies in coffee plants through the analysis of in-field leaf images. Images were collected from farms in the Shipasbamba district and clas...
Segmenting agricultural fields into management zones (MZ) is a core principle of precision agriculture (PA). However, the widespread adoption of PA remains limited, partly due to operational barriers in MZ segmentation. These barriers often involve t...
Cocoa is a key tropical crop with profound environmental, social, and economic implications throughout its value chain. Life Cycle Assessment (LCA) has been widely employed to assess these impacts; however, most applications remain fragmented and foc...
Global climate change has led to frequent extreme weather events such as high temperatures and droughts, severely threatening the heat and water balance during the growing season of summer maize. To adapt to these changes, adjusting planting dates to...
This paper proposes a fruit-harvesting robot system that improves harvesting efficiency by utilizing a Reachability Map (RM) and an Inverse Reachability Map (IRM). The proposed system accurately detects fruit locations using You Only Look Once versio...
Agricultural bio-inputs represent one of the primary alternatives for reducing the use of agrochemicals, as biological engineering offers promising solutions through the use of microorganisms for biological control of pests and diseases, and also red...
The global poultry industry faces growing challenges from skeletal disorders, with Kinky Back (KB) significantly impacting broiler welfare and production. KB causes spinal deformities that reduce mobility, feed access, and increase mortality. It ofte...
This study presents an optimized Real-Time Detection Transformer (RT-DETRv2) deep learning model for the automated assessment of Tartary buckwheat germination and evaluates the influence of soaking and ultrasonic pretreatments on the germination rati...
A reliable protocol for comprehensive rice yield data management was established to overcome the heterogeneity and inconsistency inherent in using diverse data sources, measurement conditions, and units. This methodology defines systematic routines f...
Accurate prediction of forage digestibility is essential for efficient livestock management and feed formulation. This study evaluated the performance of machine learning (ML) models to estimate the in vitro digestibility of leaf and stem components...
The efficient pre-incubation selection of duck eggs is essential to ensuring stable hatchability, but most existing optoelectronic and machine vision systems have been calibrated for chicken eggs and cannot be directly used for duck eggs because of t...
The present study serves as a proof-of-concept of our previous work, as the buckwheat (BW) starch film solutions are applied as edible coatings on strawberries and as film packaging materials for strawberry preservation. The BW starch film solution w...
The increase in pineapple production has led to a significant accumulation of agro-industrial waste, underscoring the need for sustainable strategies for its utilization. The valorization of pineapple crowns presents an opportunity to produce value-a...
The accurate prediction of plant height is crucial for optimizing soybean cultivar selection and improving yield estimations. In this study, we investigate the potential of machine learning (ML) algorithms to predict soybean plant height (PH) based o...
Digital agriculture is increasingly important for plant-level analysis, enabling detailed assessments of growth, nutrition and overall condition. Multispectral point clouds are promising due to the integration of geometric and radiometric information...
Precision agriculture, including sensors and artificial intelligence, is transforming agricultural monitoring. This study developed predictive models for fresh and dry forage mass, canopy height, forage density, dry matter (%DM), and crude protein (%...
When growing plants in artificial conditions, it is important to control the lighting parameters, both natural and artificial. This study explored the feasibility of creating a low-cost portable spectroradiometer for assessing the light environment i...
This study integrates Discrete Element Method (DEM) simulations, soil bin experiments, and multi-objective optimization to develop an energy-efficient manure injector shank. Eighteen geometries were first screened using DEM, reducing the set to six d...
Unmanned Aerial Vehicles (UAVs) equipped with low-cost RGB and near-infrared (NIR) cameras represent efficient and scalable technology for monitoring sugarcane crops. This study evaluated the potential of UAV imagery and three-dimensional crop modeli...
Accurate microclimate forecasting is essential for optimizing agricultural decision-making and resource management within Internet of Things (IoT)-enabled farming systems. This study proposes an Attention-Enhanced Dual-Branch Spatio-Temporal Deep Neu...
To address the inefficient use of pruned grape branches and the high cost of orchard management, an integrated machine for collecting and crushing grape branches was developed, tailored to the distinctive viticulture methods in Xinjiang, China, and t...
One of the defining challenges of this century is feeding a projected population of nearly ten billion people by 2050 under the pressures of intensifying water scarcity, accelerating climate change, and fragile food systems [...]
Soil compaction from repeated mechanized traffic in sugarcane cultivation reduces porosity, root growth, water infiltration and nutrient availability. Pre-consolidation stresses (σP) in sugarcane soils (70–210 kPa) are frequently exceeded...
Cashew apples, a byproduct of the cashew nut industry with an estimated global production of 38 million tonnes, are rich in several essential nutrients and are widely processed into juice, syrup, wine, pickles, and other value-added products. However...
The integration of optical sensors and deep learning technologies in smart agriculture represents a critical intersection between technological innovation and agricultural economic sustainability, yet comprehensive assessments of their economic impac...
