Abstract Color is a visible pollutant and the presence of even minute amounts of coloring substance makes it undesirable due to its appearance. The removal of color from dye-bearing effluents is a major problem due to the difficulty in treating such wastewaters by conventional treatment methods. The most commonly used methods for color removal are biological oxidation and chemical precipitation. However, these processes are effective and economic only in the case where the solute concentrations are relatively high. Most industries use dyes and pigments to color their products. The presence of dyes in effluents is a major concern due to its adverse effect on various forms of life. The discharge of dyes in the environment is a matter of concern for both toxicological and esthetical reasons. It is evident from a literature survey of about 283 recently published papers that low-cost adsorbents have demonstrated outstanding removal capabilities for dye removal and the optimal equilibrium time of various dyes with different charcoal adsorbents from agricultural residues is between 4 and 5 h. Maximum adsorptions of acidic dyes were obtained from the solutions with pH 8–10. The challenges and future prospects are discussed to provide a better framework for a safer and cleaner environment.
Abstract Adsorption processes are being widely used by various researchers for the removal of heavy metals from waste streams and activated carbon has been frequently used as an adsorbent. Despite its extensive use in water and wastewater treatment industries, activated carbon remains an expensive material. In recent years, the need for safe and economical methods for the elimination of heavy metals from contaminated waters has necessitated research interest toward the production of low cost alternatives to commercially available activated carbon. Therefore, there is an urgent need that all possible sources of agro-based inexpensive adsorbents should be explored and their feasibility for the removal of heavy metals should be studied in detail. The objective of this research is to study the utilization possibilities of less expensive adsorbents for the elimination of heavy metals from wastewater. Agricultural and industrial waste by-products such as rice husk and fly ash have be used for the elimination of heavy metals from wastewater for the treatment of the EL-AHLIA Company wastewater for electroplating industries as an actual case study. Results showed that low cost adsorbents can be fruitfully used for the removal of heavy metals with a concentration range of 20–60 mg/l also, using real wastewater showed that rice husk was effective in the simultaneous removal of Fe, Pb and Ni, where fly ash was effective in the removal of Cd and Cu.
The application of plastic film mulching combined with drip irrigation can significantly alter the soil and water conditions for crop development. However, existing stomatal conductance models fail to adequately incorporate the effects of this practice on the physiological development of crops. This study employs three stomatal conductance models: Ball-Woodrow-Berry (BWB) model, Ball-Berry-Leuning (BBL) model, and Unified Stomatal Optimization (USO) model. This study introduces two model correction factors: the water response function (f(θ)) and the leaf-air temperature difference (∆T). These factors are utilized to simulate soybean stomatal conductance under various conditions, including plastic film mulching with drip irrigation, plastic film mulching without irrigation, drip irrigation without mulching, and control. The findings demonstrate that the USO model achieves superior performance, followed by the BBL and BWB models. Furthermore, the f(θ) correction factor outperforms the ∆T correction factor in enhancing model performance. The determination coefficients of the corrected BWB, BBL, and USO models increased by 15.2 %-102.2 %, 16.7 %-75.2 %, and 11.6 %-61.0 %, respectively. Meanwhile, the relative errors decreased by 7.5 %-43.2 %, 9.4 %-36.7 %, and 8.3 %-36.6 %, respectively. Additionally, the root mean square errors decreased by 8.2 %-27 %, 6.7 %-32.8 %, and 12.3 %-33.3 %. The corrected model exhibits strong reliability and universality across various soil water relative content and ∆T conditions, as evidenced by comparisons with the 95 % confidence intervals of observational data. The results of this study establish a theoretical foundation for the rational selection of stomatal conductance models in the northeast black soil region, thereby enhancing the simulation accuracy of water and carbon cycle processes under complex hydrothermal conditions.
Biochar has been widely applied as an efficiency soil additive to modify the quality of cultivated field. However, the effects of long-term biochar addition on spatial and temporal dynamics of soil compaction, and the changes in soil moisture condition and plant root growth remain unclear. Hence, an eight-year (2015/16–2023/24) consecutive field experiment on wheat was conducted in the subtropical humid region of east China, using three treatments: no N fertilizer (PK), chemical fertilizer (NPK), NPK plus biochar (5 t ha−1 yr−1, NPKB). Relative to NPK, across nine growing seasons of wheat, NPKB decreased the soil bulk density by 0.019 and 0.013 units (g cm−3 yr−1), and decreased the soil penetration resistance by 0.028 and 0.015 units (MPa yr−1) in 0–10 cm and 10–20 cm depths, respectively. Biochar addition improved soil water content from seeding to flowering, increased wheat root distribution during the whole growth period, and enhanced soil N supply capacity by promoting N adsorption, which gave rise to greater biomass and N accumulation and more biomass allocation in grain. As a result, NPKB increased wheat yield by 14.8 %, N recovery efficiency by 55.1 %, and crop water productivity by 14.9 %, relative to NPK, on average across four growing seasons of wheat. Therefore, long-term biochar addition has potential to substantially increase grain yield of post-rice wheat, water productivity, and N recovery efficiency. Hence, for the sustainable intensification cropping in the long-run, successive biochar addition could be a finable management for wheat production on the rainfed Yangtze River Region of China.
Accurate, near real-time soybean phenology information is critical for crop management and breeding. Previous approaches relying on satellite remote sensing time-series data suffer from temporal delays, limiting their usefulness for in-season decision-making. To overcome this limitation, this study reframes phenology identification as a near real-time classification task using single-timepoint Unmanned Aerial Vehicle (UAV) imagery collected from 420 soybean germplasm resources across three experimental sites, and proposes an innovative multi-modal dynamic Gating Fusion Model that integrates two optimized pathways. one based on machine learning (ML) and the other on deep learning (DP). In the ML branch, systematic benchmarking of tabular-feature models identified the Soft Voting ensemble as the best classifier. In the DL branch, an enhanced BC-ConvNeXt model equipped with BiFPN and CBAM modules was developed to strengthen visual feature extraction. Building on these two optimal classifiers, the dynamic gating fusion model achieved the highest F1-score of 94.3% across seven key growth stages (V1, V2, R1, R2, R6, R7, R8). This result represents a significant improvement of 1.5% and 10.6% over the best performing ML and DL models, respectively. The superior performance arises from the intelligent arbitration of complementary strengths, with gating-weight analysis revealing a strategy that prioritizes ML predictions while leveraging DL for error correction. This work establishes a complete framework for near real-time crop phenology detection and demonstrates the strong potential of intelligent multi-modal fusion in high-throughput phenotyping.
<p dir="ltr">The aim of the SAM is to enhance the quality of data that could be used for different models such as CGE, linkages, multipliers and price models</p>
Madhav Rijal, Rashik Shrestha, Trevor Smith
et al.
This study presents a methodology to safely manipulate branches to aid various agricultural tasks. Humans in a real agricultural environment often manipulate branches to perform agricultural tasks effectively, but current agricultural robots lack this capability. This proposed strategy to manipulate branches can aid in different precision agriculture tasks, such as fruit picking in dense foliage, pollinating flowers under occlusion, and moving overhanging vines and branches for navigation. The proposed method modifies RRT* to plan a path that satisfies the branch geometric constraints and obeys branch deformable characteristics. Re-planning is done to obtain a path that helps the robot exert force within a desired range so that branches are not damaged during manipulation. Experimentally, this method achieved a success rate of 78% across 50 trials, successfully moving a branch from different starting points to a target region.
Agricultural robots are emerging as powerful assistants across a wide range of agricultural tasks, nevertheless, still heavily rely on manual operation or fixed rail systems for movement. The AgriVLN method and the A2A benchmark pioneeringly extend Vision-and-Language Navigation (VLN) to the agricultural domain, enabling robots to navigate to the target positions following the natural language instructions. In practical agricultural scenarios, navigation instructions often repeatedly occur, yet AgriVLN treat each instruction as an independent episode, overlooking the potential of past experiences to provide spatial context for subsequent ones. To bridge this gap, we propose the method of Spatial Understanding Memory for Agricultural Vision-and-Language Navigation (SUM-AgriVLN), in which the SUM module employs spatial understanding and save spatial memory through 3D reconstruction and representation. When evaluated on the A2A benchmark, our SUM-AgriVLN effectively improves Success Rate from 0.47 to 0.54 with slight sacrifice on Navigation Error from 2.91m to 2.93m, demonstrating the state-of-the-art performance in the agricultural domain. Code: https://github.com/AlexTraveling/SUM-AgriVLN.
Agricultural robots are serving as powerful assistants across a wide range of agricultural tasks, nevertheless, still heavily relying on manual operations or railway systems for movement. The AgriVLN method and the A2A benchmark pioneeringly extended Vision-and-Language Navigation (VLN) to the agricultural domain, enabling a robot to navigate to a target position following a natural language instruction. Unlike human binocular vision, most agricultural robots are only given a single camera for monocular vision, which results in limited spatial perception. To bridge this gap, we present the method of Agricultural Vision-and-Language Navigation with Monocular Depth Estimation (MDE-AgriVLN), in which we propose the MDE module generating depth features from RGB images, to assist the decision-maker on multimodal reasoning. When evaluated on the A2A benchmark, our MDE-AgriVLN method successfully increases Success Rate from 0.23 to 0.32 and decreases Navigation Error from 4.43m to 4.08m, demonstrating the state-of-the-art performance in the agricultural VLN domain. Code: https://github.com/AlexTraveling/MDE-AgriVLN.
The addition of phosphorus, in the form of mineral fertilizer, becomes necessary in most agricultural soils in order to achieve consistent high yield levels of intensive farming and maintain soil fertility. Recent consolidation of phosphate fertilizer industry has transformed fragmented trade into a single integrated global network, where a small group of large-scale companies dominates the international market for phosphate commodity fertilizers. To assess the impact of new trade structure on future region-level phosphorus supply, we simulate behavior of markets for ammonium phosphates in the FAO scenarios of global intensive farming evolution. Details of market microstructure are represented here by a many-to-many matching market. Current spatial distribution of global demand in ammonium phosphates is projected to strengthen by 2030. Bootstrap simulations produce similar network structures for both scenarios showing reduction in the density of the distributed market. In response to the non-uniform demand growth across regions, market concentration is expected to increase for small-scale markets, and to remain predominantly stable for large-scale markets; on the supply side, simulated equilibria point out large-scale multi-market suppliers concentrating on fewer markets than before. A high rate of import substitution by local suppliers in some markets indicate the need of additional region-level capital investment.
Smart agriculture applications, integrating technologies like the Internet of Things and machine learning/artificial intelligence (ML/AI) into agriculture, hold promise to address modern challenges of rising food demand, environmental pollution, and water scarcity. Alongside the concept of the phytobiome, which defines the area including the plant, its environment, and associated organisms, and the recent emergence of molecular communication (MC), there exists an important opportunity to advance agricultural science and practice using communication theory. In this article, we motivate to use the communication engineering perspective for developing a holistic understanding of the phytobiome communication and bridge the gap between the phytobiome communication and smart agriculture. Firstly, an overview of phytobiome communication via molecular and electrophysiological signals is presented and a multi-scale framework modeling the phytobiome as a communication network is conceptualized. Then, how this framework is used to model electrophysiological signals is demonstrated with plant experiments. Furthermore, possible smart agriculture applications, such as smart irrigation and targeted delivery of agrochemicals, through engineering the phytobiome communication are proposed. These applications merge ML/AI methods with the Internet of Bio-Nano-Things enabled by MC and pave the way towards more efficient, sustainable, and eco-friendly agricultural production. Finally, the implementation challenges, open research issues, and industrial outlook for these applications are discussed.
Quetzalcoatl Hernandez-Escobedo, David Muñoz-Rodríguez, Alejandro Vargas-Casillas
et al.
Over the last three years, research on the application of renewable energies in the agricultural sector has grown significantly. In this study, we conducted a bibliometric analysis of global scientific production from 2019 to 2021 to identify trends, leading contributors, and emerging research areas. Based on 1378 documents retrieved from Scopus, we observed a clear upward trend in publications, with a peak in 2021. India, China, the United States, Italy, and the United Kingdom were the most productive countries, while key institutions from China, Iran, and the Netherlands led the research output. Our results reveal five major thematic clusters: renewable energy technologies in agriculture, bioenergy, sustainable agriculture, biomass energy, and the environmental impact of agricultural activities. Notable advances include agrovoltaic systems, the use of agricultural and livestock waste for biogas production, and the development of agricultural robots powered by renewable energy sources. Additionally, there is increasing interest in examining the links among agriculture, renewable energy use, and greenhouse gas emissions, aligned with global sustainability goals. This analysis highlights the evolution of the field, international collaboration patterns, and the most influential research lines, offering valuable insights to guide future scientific developments in integrating renewable energies within the agricultural sector.
Efficient harvesting of herbaceous mulberry is essential for reducing labor costs and ensuring high-quality stubble for rapid regrowth in sericulture production. However, existing mechanized harvesters rarely enable in situ measurement of cutting and conveying power under field conditions, and the influence of operational parameters on both energy consumption and stubble quality remains insufficiently quantified. In this study, a crawler-type prototype harvester equipped with three independently driven AC servo motors and real-time torque sensors was developed to monitor cutting, conveying, and baling processes. A Central Composite Design (CCD) combined with response surface methodology was employed to investigate the effects of forward speed, conveying speed, and average cutting speed on average cutting power per branch, average conveying power per branch, and stubble quality score. Field trials were conducted in Rizhao, Shandong Province, China, using the mulberry cultivar ‘Guishangyou 12’. The regression models exhibited high goodness of fit (R² = 0.9546∼0.9946) and non-significant lack of fit (p > 0.05). Results indicated that cutting power consumption was on average 3.7 times higher than conveying power, with cutting speed exerting the most significant influence on energy use (p < 0.01) and stubble quality (p < 0.01). The optimal parameter combination—forward speed of 0.55 m·s⁻¹, conveying speed of 0.96 m·s⁻¹, and cutting speed of 0.95 m·s⁻¹—reduced cutting power to 26.91 J·branch⁻¹, minimized conveying power to 6.64 J·branch⁻¹, and achieved a stubble quality score of 9.43. Validation experiments confirmed that deviations from predicted values were <5%. These findings provide a quantitative basis for operational optimization and energy efficiency improvement in herbaceous mulberry harvesting machinery.
The accurate tracking of individual calves is essential for health monitoring. However, existing multi tracking frameworks often encounter frequent ID abnormal switching issues during occlusion. To address these challenges, we propose a novel multi-object tracking framework named YSD-BPTrack for calves in occluded environments on cattle farms in this paper. This framework mainly consists of two stages: detection and tracking. Concerning the detection phase, the DCNv4 is integrated into the YOLOv8s model to capture spatial deformation features caused by occlusion, thereby enhancing detection performance under occlusion. Additionally, the Star operation of StarNet is also applied to the model to achieve excellent detection performance with lower computational costs. Concerning the tracking stage, we first propose an innovative rematching algorithm (Rematching module) and a new trajectory removal strategy (Trajectory removal module). The Rematching module performs rematching with detection boxes utilizing extended trajectory prediction boxes in cases of occlusion, resulting in a reduced probability of ID switch errors. Moreover, the Trajectory Removal module dynamically adjusts the removal time for lost matching trajectories, which decreases the likelihood of trajectories being mistakenly removed. Specifically, our proposed novel framework achieves a HOTA (Higher Order Tracking Accuracy) of 91.6%, surpassing other frameworks in both track accuracy and efficiency. Experimental results also validate the superiority of the YSD-BPTrack, with HOTA increasing by 17.6%, MOTA (Multiple Object Tracking Accuracy) by 13.9%, MOTP (Multiple Object Tracking Precision) by 1.8%, IDF1 (Identification F1 Score) by 15.4%, and reducing parameters by 49.1%, IDSw (Identification Switches) by 88.9%, and computational overhead by 39.2% compared to the other frameworks. Overall, the proposed multi-object tracking framework has great potential to revolutionize the tracking of calves.
The effectiveness of farmer loan policies is crucial for the high-quality development of agriculture and the orderly advancement of the rural revitalization strategy. Exploring the impact of farmers' borrowing behavior on agricultural production technical efficiency holds significant practical value. This paper utilizes data from the 2020 China Family Panel Studies (CFPS) and applies Stochastic Frontier Analysis (SFA) along with the Tobit model for empirical analysis. The study finds that farmers' borrowing behavior positively influences agricultural production technical efficiency, with this effect being especially pronounced among low-income farmers. Additionally, the paper further examines household characteristics, such as household head age, gender, educational level, and the proportion of women in the family, in relation to agricultural production technical efficiency. The findings provide policy recommendations for optimizing rural financial service systems and enhancing agricultural production technical efficiency.
This survey investigates the transformative potential of various YOLO variants, from YOLOv1 to the state-of-the-art YOLOv10, in the context of agricultural advancements. The primary objective is to elucidate how these cutting-edge object detection models can re-energise and optimize diverse aspects of agriculture, ranging from crop monitoring to livestock management. It aims to achieve key objectives, including the identification of contemporary challenges in agriculture, a detailed assessment of YOLO's incremental advancements, and an exploration of its specific applications in agriculture. This is one of the first surveys to include the latest YOLOv10, offering a fresh perspective on its implications for precision farming and sustainable agricultural practices in the era of Artificial Intelligence and automation. Further, the survey undertakes a critical analysis of YOLO's performance, synthesizes existing research, and projects future trends. By scrutinizing the unique capabilities packed in YOLO variants and their real-world applications, this survey provides valuable insights into the evolving relationship between YOLO variants and agriculture. The findings contribute towards a nuanced understanding of the potential for precision farming and sustainable agricultural practices, marking a significant step forward in the integration of advanced object detection technologies within the agricultural sector.
Lameya Aldhaheri, Noor Alshehhi, Irfana Ilyas Jameela Manzil
et al.
The emerging field of smart agriculture leverages the Internet of Things (IoT) to revolutionize farming practices. This paper investigates the transformative potential of Long Range (LoRa) technology as a key enabler of long-range wireless communication for agricultural IoT systems. By reviewing existing literature, we identify a gap in research specifically focused on LoRa's prospects and challenges from a communication perspective in smart agriculture. We delve into the details of LoRa-based agricultural networks, covering network architecture design, Physical Layer (PHY) considerations tailored to the agricultural environment, and channel modeling techniques that account for soil characteristics. The paper further explores relaying and routing mechanisms that address the challenges of extending network coverage and optimizing data transmission in vast agricultural landscapes. Transitioning to practical aspects, we discuss sensor deployment strategies and energy management techniques, offering insights for real-world deployments. A comparative analysis of LoRa with other wireless communication technologies employed in agricultural IoT applications highlights its strengths and weaknesses in this context. Furthermore, the paper outlines several future research directions to leverage the potential of LoRa-based agriculture 4.0. These include advancements in channel modeling for diverse farming environments, novel relay routing algorithms, integrating emerging sensor technologies like hyper-spectral imaging and drone-based sensing, on-device Artificial Intelligence (AI) models, and sustainable solutions. This survey can guide researchers, technologists, and practitioners to understand, implement, and propel smart agriculture initiatives using LoRa technology.