Agricultural Production Management Research Articles

Integrating irrigation management and soil remediation practices for sustainable agricultural production: advances, challenges, and future directions

Integrating irrigation management and soil remediation practices for sustainable agricultural production: advances, challenges, and future directions

Experimental evidence of negative agricultural impacts and effectiveness of mitigation strategies of invasive green iguanas (Iguana iguana) in Puerto Rico

Losses in crop yield due to invasive insects, weeds, pathogens, and herbivores cost trillions of dollars per year globally. To prevent further spread of invasive agricultural pest species, continuous monitoring and prevention are crucial. Once introduced, however, assessing the impact of an invasive pest on agricultural production and testing management strategies are essential. The green iguana (Iguana iguana), a globally widespread invasive herbivore, is considered a possible agricultural pest although no quantitative data on its impact are available. In this study, we evaluated the impact of the invasive green iguana on cucumber (Cucumis sativus, var. Dasher II) and lettuce (Lactuca sativa, var. Black-seeded Simpson) yield by testing the efficacy of two management strategies – Neem-based pesticide and mesh fencing – compared to open field cultivation in Puerto Rico. Mesh fencing led to 20% more growth and doubled cucumber yield compared to open field cultivation, while spraying Neem led to an 18% increase in plant growth but no effect on cucumber yield. We found no difference in lettuce growth or yield among treatment and control plots. This study supports categorizing the green iguana as an invasive agricultural pest species and demonstrates the reptile’s potential to reduce crop yield. It also shows that Neem application at the manufacturer’s suggested concentration is not an effective mitigation technique for reducing crop loss due to green iguana herbivory. Government agencies in regions where the green iguana has the potential to be introduced should consider the species a threat to food production when developing monitoring programs and drafting regulations.

The Spatiotemporal Dynamics of Temperature Variability Across Mts. Qinling: A Comparative Study from 1971 to 2022

Analyzing the spatiotemporal patterns of atmospheric temperature in sensitive areas is critically important for understanding the broader implications of global climate change, which remains a prominent topic in geosciences. It also plays a crucial role in advancing sustainable development. This study utilized daily minimum, maximum, and mean temperature data from twelve meteorological stations across the South and North Mts. Qinling (Qinling Mountains). Employing trend analysis, the Mann–Kendall mutation test, and Morlet wavelet analysis, we explored the predominant temperature trends and characteristics from 1971 to 2022. Our findings revealed consistent inter-annual warming trends in both regions, with more rapid temperature increases in the North compared to the South. Notably, significant shifts occurred in 2003 for both mean and minimum temperatures in the North, while the maximum and minimum temperature values were recorded in the 2010s and 1980s, respectively. Both regions exhibited a primary temperature fluctuation cycle of 28 years. Seasonally, the strongest warming effects appeared in spring, with the weakest in autumn, and moderate effects in winter and summer, indicating that spring contributes most significantly to regional warming. Monthly analysis showed positive temperature trends across all months, with higher rates in the North. The weakening temperature boundary effect of the Mts. Qinling suggested a weakening North–South division, particularly highlighted by the northward shift of the 1 °C isotherm curve for the coldest month, moving away from the previously observed 0 °C isotherm. This northward shift highlights the differential warming rates between the northern and southern regions. Overall, the analysis confirms a robust warming trend, with notable fluctuations in January’s temperatures since 1998, suggesting the Mts. Qinling’s emerging role as a climatic divider in the Chinese Mainland. This introduces new challenges for regional ecosystems, agricultural production, and water resource management, highlighting the pressing need to advance regional sustainable development in the face of climate change.

Study on Spatiotemporal Characteristics and Influencing Factors of High-Resolution Single-Season Rice

Single-season rice describes the area under rice cultivation from May–October of the year. Many scholars have used lower-resolution data to study single-season rice in different regions, but using high-precision and high-resolution single-season rice data can reveal new phenomena. This paper uses a long-time-series, high-precision, and high-resolution single-season rice cultivation dataset to conduct an in-depth analysis of the spatial–temporal variability characteristics of single-season rice in Jiangsu Province, China, from 2017 to 2021. It explores the correlation between meteorological factors and greenhouse gasses for single-season rice. It analyzes the driving role of social factors on single-season rice. The results showed that single-season rice was mainly grown in the central and northeastern regions of the study area. The single-season rice cultivation was significantly reduced in 2020 due to the impact of COVID-19. Single-season rice strongly correlates with meteorological factors in time but shows a weak spatial correlation. This is because human factors largely dominate the area under single-season rice cultivation. Methane emissions in the study area are mainly influenced by anthropogenic activities rather than single-season rice. Social factors are essential in controlling single-season rice cultivation in the study area. This study was conducted in Jiangsu Province, China. Still, the methodology and results have important implications for agricultural production and environmental management studies in other regions, and some findings have general applicability.

Construction of a blockchain based cold chain logistics information platform for Gannan navel oranges to enhance transparency and efficiency

As a high-value agricultural product, the cold chain logistics management of Gannan navel oranges has global significance. Especially in Africa and Europe, food safety and supply chain quality are critical issues. However, the existing Gannan navel orange logistics information management system relies on traditional databases and information systems, with insufficient data sharing and transparency, affecting the overall coordination of the supply chain and customer satisfaction. In addition, the traditional system is vulnerable to hacker attacks and malicious tampering of data by insiders, resulting in serious economic losses and reputation damage. So the blockchain platform constructed in this study improves the transparency and traceability of logistics data through the SMART-PBFT algorithm of the alliance chain, which optimizes the logistics management process, improves the quality of logistics services, and reduces operating costs. It improves the logistics efficiency and data transparency of local agricultural products, and also provides a demonstrative case for the cold chain management of other high-value agricultural products around the world, with broad application potential and reference value.

Predictive soil mapping using random forest models: Applications in pH and soil organic matter assessment

Digital Soil Mapping (DSM) presents a highly scalable and efficient alternative to traditional soil analysis, which is typically limited by its labor-intensive processes, time constraints and low spatial resolution. By utilizing advanced computational techniques such as machine learning and remote sensing, DSM overcomes these limitations and improves the accuracy, efficiency and scalability of soil property assessments. This study, conducted across Tamil Nadu, India, applied DSM and Random Forest (RF) models to predict 2 key soil properties: pH and Soil Organic Matter (SOM). We employed Conditioned Latin Hypercube Sampling (cLHS) for optimized sampling point selection and utilized the Boruta algorithm to identify the most relevant covariates for accurate modeling. The RF models were fine-tuned using a comprehensive grid search, with the optimal configuration spanning from 500 to 2000 trees (ntree) and mtry from 1 to 11. The best-performing model was found with 2000 trees and mtry set to 1 yielding superior prediction for SOM and pH with Root Mean Square Error (RMSE) values of 0.71 and 0.60 respectively, showcasing a high level of predictive accuracy. Our findings emphasize the critical role that remote sensing indices play in predicting SOM, while pH was influenced by both terrain features and remote sensing data. In comparison to previous studies, this research offers novel improvements in both sampling optimization and model configuration, leading to enhanced predictive performance. These results hold significant potential for sustainable land-use planning, agricultural productivity and environmental management.

Runoff Estimation in Kajurli Watershed Using SCS-CN and GIS Techniques

The study focuses on estimating surface runoff using the Soil Conservation Service Curve Number (SCS-CN) method, combined with Remote Sensing (RS) and Geographic Information System (GIS) techniques, for the Kajurli Watershed in Ratnagiri District, Maharashtra. Surface runoff is a key factor influencing agricultural productivity, soil erosion, and water management at the watershed level. The SCS-CN method, a widely used tool for runoff estimation, utilizes rainfall, land use, and soil type data. By integrating RS and GIS, the study achieves a more accurate assessment of land use and soil characteristics, which are critical inputs for runoff calculations. The Kajurli Watershed falls under hydrological soil group B, known for moderate runoff potential. The study uses 33 years of historical rainfall data (1990-2022) to compute annual runoff using the SCS-CN method. Key parameters such as Curve Numbers (CN) are derived based on land use, hydrological soil groups, and antecedent moisture conditions (AMC). The average annual rainfall in the watershed is found to be 3392.8 mm, with 39.69% of this rainfall contributing to surface runoff. The integration of RS and GIS simplifies spatial analysis, enabling accurate and efficient runoff estimation. The study highlights the sensitivity of the SCS-CN method to CN values, emphasizing the importance of precise land use and soil data for reliable hydrological modelling. These findings offer crucial insights for water resource management, flood control, and agricultural planning in the region.

Spatial-temporal analysis and trend prediction of regional crop disease based on electronic medical records

Intelligent diagnosis of individual crop diseases has matured. How to understand the evolution patterns and predict regional disease trends remains a significant challenge. Plant Electronic Medical Records (PEMRs) offer valuable spatial-temporal characteristics about crop diseases, presenting a new opportunity for predicting the occurrence of regional diseases. In this study, we used a large prescription database from Beijing (2018-2021) to reframe regional disease prediction as a time series forecasting task. Firstly, to analyze spatial-temporal evolution patterns, we use ArcGIS to extract key information and identify potential connections between different disease occurrence points. Then, we developed a novel deep learning combined model SV-CBA, which combines Seasonal and Trend decomposition (STL) with Variational Mode Decomposition (VMD) to identify trend, seasonal, and residual components, and re-decomposes the residuals. STL-VMD can capture long-term trends and periodic variations while managing nonlinear and volatile characteristics. The CNN-BiLSTM-Attention model calculates disease trends by linearly integrating predictions of each sub-series. To reduce computational complexity while maintaining predictive performance, we propose an improved simplified attention mechanism. Our model demonstrates superior performance in both comparative and ablation experiments using the PEMRs dataset, outperforming numerous other models. This study provides accurate disease trend predictions, aiding farmers and regional managers in agricultural production management.

Solar irrigation system: Analysis of the solution for livestock production in North Cameroon

This article examines the impact of implementing a solar irrigation system on livestock production in North Cameroon, a region marked by climatic challenges and increasingly dry conditions with 600 to 1,200 mm of rainfall per year and temperatures between 30°C and 40°C. Livestock farmers in the region rely heavily on natural pastures and forage crops to feed their livestock, but these resources are insufficient during periods of drought. The area is home to around 2 million head of cattle, with an estimated 500,000 to 700,000 head of sheep and 300,000 to 500,000 head of goats. Our methodology involved a comparative before-and-after analysis, where data on crop yields, pasture quality and animal performance were collected. Statistical simulation was used to assess improvements in pasture biomass, forage yield and animal performance. The results show significant improvements if the solar irrigation system is installed, with: a 30% increase in biomass; a 50% increase in forage yield with a 16.7% gain in animal weight; a 20% increase in milk production; a 30% reduction in forage procurement costs; and a 33.3% increase in income generated. These results indicate that solar irrigation will have a positive impact on farm productivity and profitability. The installation of a solar irrigation system in Northern Cameroon represents a sustainable and economically viable solution for improving livestock production in a region vulnerable to drought.In sub-Saharan Africa, solar irrigation systems have been successfully implemented in several countries, including Kenya, Ethiopia, Mali and Burkina Faso. These projects have demonstrated the viability of this technology in semi-arid and arid environments, with notable improvements in agricultural production and water management. The climatic and geographical conditions in Northern Cameroon are similar to those in the regions where solar irrigation systems have been tested.

Spatiotemporal Dynamics of Hot-Dry Winds in Northern China: A Multidimensional Analysis Using Gridded Datasets

Abstract Hot-dry winds (HDWs), characterized by simultaneous high temperature, low humidity, and strong wind speed, represent prevalent agrometeorological hazards in northern China. To date, our comprehension of HDWs has been constrained to regional scales due to the dependence upon in situ meteorological observations and the lack of a universal definition of HDWs. In this study, we leveraged a gridded dataset to elucidate the spatiotemporal dynamics of HDWs in northern China from 1961 to 2022. We also introduced a standardized HDW index (sHDWI) to investigate HDW severity. The results indicated more HDW occurrences in eastern Northwest China (NW) and western Inner Mongolia (IM). While there were declining trends in HDW frequency, extent, and severity on average in northern China from 1961 to 1993, these trends were reversed by an overall increase afterward. Intriguingly, regions spanning from NW to IM exhibited significant upward trends in HDW severity according to sHDWI. The spatial extents of light, moderate, and heavy HDWs consistently decreased in North China (NC), with notable expansions over IM, NW, and Northeast China (NE) from 2001 to 2022. Importantly, the increased frequency, expanded extent, and intensified severity of HDWs, particularly, the heavy ones, underscored the escalation of HDW events in recent decades over the arid IM and hyperarid NW, known for their fragile ecological environment. The findings from this research carry significant implications for agricultural production and water management in northern China influenced by the changing patterns of HDWs in the context of climate change. Significance Statement This study aims to enhance our understanding of the large-scale spatiotemporal dynamics of hot-dry winds (HDWs) in northern China using a 0.25° × 0.25° gridded dataset spanning the period from 1961 to 2022. The adoption of the gridded dataset enables a multidimensional exploration of HDWs, encompassing their frequency, extent, and severity which cannot be fully captured by in situ observations. Meanwhile, to quantify changes in HDW severity, we introduce a standardized HDW index that surpasses existing HDW definitions relying on absolute thresholds or relative percentiles of maximum air temperature, relative humidity, and wind speed. Our results reveal a consistent reversal in HDW frequency, extent, and severity around 1993 in northern China and highlight the intensification of heavy HDWs, particularly in the arid and hyperarid regions within the study area.

Winter Wheat Yield Prediction Based on the ASTGNN Model Coupled with Multi-Source Data

Timely and accurate prediction of winter wheat yields, which is crucial for optimizing production management, maintaining supply–demand balance, and ensuring food security, depends on interactions among numerous factors, such as climate, surface characteristics, and soil quality. Despite the extensive application of deep learning models in this field, few studies have analyzed the effect of the large-scale geospatial characteristics of neighboring regions on crop yields. Therefore, we present an attention-based spatio-temporal Graph Neural Network (ASTGNN) model coupled with geospatial characteristics and multi-source data for improved accuracy of winter wheat yield estimation. The datasets used in this study included multiple types of remote sensing, meteorological, soil, crop yield, and planting area data for Anhui, China, from 2005 to 2020. The results showed that multi-source data led to higher prediction performance than single-source data, and enabled accurate prediction of winter wheat yields three months prior to harvest. Furthermore, the ASTGNN model provided better prediction performance than two traditional crop yield prediction models (R2 = 0.70, RMSE = 0.21 t/ha, MAE = 0.17 t/ha). Therefore, ASTGNN enhances the accuracy of crop yield prediction by incorporating geospatial characteristics. This research has implications for improving agricultural production management, promoting the development of digital agriculture, and addressing climate change in agriculture.

Land use and land management in Kazakhstan

Global climate warming annually leads to a decrease in the agro-climatic potential of farming, many areas of traditional cultivation of land become unsuitable for agriculture due to desertification, swamping and erosion processes. Purpose - the concept of sustainable land use is considered, taking into account foreign experience. Methods are based on the system analysis of works of domestic and foreign scientists and data from the Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan, analytical reports of the Committee on Land Resources Management. Results - different approaches to solving the problems of agroindustrial complex are presented, the main trends in the development of the agrarian sector of the republic, the need to combat land degradation, to preserve their productivity and ecosystem functions are shown. Conclusions - the analysis of world practice shows that the principles of land cultivation based on environmental, social and economic criteria should be implemented based on the effective use of agricultural land, that is, through the optimization of agricultural land fund. The research has an interdisciplinary character. The systematic study of this issue contributes to the application of rational methods of land management in agricultural production. The authors note that the use of digital technologies in the sphere of land relations will help to increase the efficiency of production and economic activity, to unite the main factors and efforts of the participants of this process in a single information space, to increase the reliability and completeness of transmitted data. Digital infrastructure in the farming system is the driver of future development. Provision of all types of services in accordance with land legislation should be digitized.

Pyramid-YOLOv8: a detection algorithm for precise detection of rice leaf blast

Rice blast is the primary disease affecting rice yield and quality, and its effective detection is essential to ensure rice yield and promote sustainable agricultural production. To address traditional disease detection methods’ time-consuming and inefficient nature, we proposed a method called Pyramid-YOLOv8 for rapid and accurate rice leaf blast disease detection in this study. The algorithm is built on the YOLOv8x network framework and features a multi-attention feature fusion network structure. This structure enhances the original feature pyramid structure and works with an additional detection head for improved performance. Additionally, this study designs a lightweight C2F-Pyramid module to enhance the model’s computational efficiency. In the comparison experiments, Pyramid-YOLOv8 shows excellent performance with a mean Average Precision (mAP) of 84.3%, which is an improvement of 9.9%, 4.3%, 7.4%, 6.1%, 1.5%, 3.7%, and 8.2% compared to the models Faster-RCNN, RT-DETR, YOLOv3-SPP, YOLOv5x, YOLOv9e, and YOLOv10x, respectively. Additionally, it reaches a detection speed of 62.5 FPS; the model comprises only 42.0 M parameters. Meanwhile, the model size and Floating Point Operations (FLOPs) are reduced by 41.7% and 23.8%, respectively. These results demonstrate the high efficiency of Pyramid-YOLOv8 in detecting rice leaf blast. In summary, the Pyramid-YOLOv8 algorithm developed in this study offers a robust theoretical foundation for rice disease detection and introduces a new perspective on disease management and prevention strategies in agricultural production.

Research on the Human-Computer Interaction Optimization of Supply Chain Management of Agricultural Products Based on AI-Powered CAD

Research on the Human-Computer Interaction Optimization of Supply Chain Management of Agricultural Products Based on AI-Powered CAD

Prediction and spatial–temporal changes of soil organic matter in the Huanghuaihai Plain by combining legacy and recent data

Soil organic matter (SOM) is critical for soil fertility, crop growth, and plays an important role in the global carbon cycle and climate change. Therefore, spatial prediction of SOM is important to rational soil resource utilization, agricultural production, and ecological environment management. However, large-area SOM mapping research heavily relies on legacy soil data, and large-scale recent SOM mapping may not be possible or have lower accuracy due to limited or less recent data availability. In this study, we aimed to improve SOM prediction and mapping accuracy by combining legacy data with limited recent data. Three models, namely, partial least squares regression (PLSR), random forest (RF), and one-dimensional convolutional neural network (1D-CNN), were applied and compared. The results showed that combining legacy and recent data effectively improved SOM prediction accuracy compared to using only recent data. Among the three modeling methods, 1D-CNN exhibited superior performance, with an averaged determination coefficient of the prediction (R2) of 0.58, a root mean square error (RMSE) of 4.56 g/kg, and a ratio of performance to interquartile distance (RPIQ) of 2.05. The predicted SOM content for both legacy (1980 s) and recent (2010 s) periods showed similar spatial distribution patterns throughout the Huanghuaihai Plain. Generally, there was a noticeable trend of increasing SOM content from northwest to southeast, with higher values observed in Jiangsu and lower values concentrated in Henan, Hebei, and Shandong regions within the study area. Over time, SOM contents in the Huanghuaihai Plain showed an increasing trend, with an average increase of 5.90 g/kg from legacy to recent period. This study provides a promising approach for improving SOM prediction and mapping accuracy at large scales, particularly when recent data availability is limited.

Application of 5G network technology in agricultural production and information management

Establishing a management information system that adapts to the continuous development of agricultural production management today, adopting advanced 5G technology and means, and comprehensively managing the entire agricultural production is an inevitable trend in the development of agricultural production management. In order to improve the effect of agricultural production informatization management, this paper combines 5G (fifth-generation) network technology to construct agricultural production and informatization management models. In view of the massive dynamic agricultural production management data, this paper studies the efficient multi-copy management strategy, which can consider the two aspects of load balancing and energy efficiency at the same time, so as to achieve efficient data access and energy efficiency. Moreover, this paper introduces a time series-based file access heat calculation model. In terms of determining the number of copies of files, this paper proposes a copy factor allocation algorithm centered on the ranking of file access popularity, and gives a characteristic function. Through cluster analysis, it can be seen that the agricultural production informatization management model based on 5G technology proposed in this paper can effectively improve the efficiency of agricultural information management.

Research on the Wild Mushroom Recognition Method Based on Transformer and the Multi-Scale Feature Fusion Compact Bilinear Neural Network

Wild mushrooms are popular for their taste and nutritional value; however, non-experts often struggle to distinguish between toxic and non-toxic species when foraging in the wild, potentially leading to poisoning incidents. To address this issue, this study proposes a compact bilinear neural network method based on Transformer and multi-scale feature fusion. The method utilizes a dual-stream structure that integrates multiple feature extractors, enhancing the comprehensiveness of image information capture. Additionally, bottleneck attention and efficient multi-scale attention modules are embedded to effectively capture multi-scale features while maintaining low computational costs. By employing a compact bilinear pooling module, the model achieves high-order feature interactions, reducing the number of parameters without compromising performance. Experimental results demonstrate that the proposed method achieves an accuracy of 98.03%, outperforming existing comparative methods. This proves the superior recognition performance of the model, making it more reliable in distinguishing wild mushrooms while capturing key information from multiple dimensions, enabling it to better handle complex scenarios. Furthermore, the development of public-facing identification tools based on this method could help reduce the risk of poisoning incidents. Building on these findings, the study suggests strengthening the research and development of digital agricultural technologies, promoting the application of intelligent recognition technologies in agriculture, and providing technical support for agricultural production and resource management through digital platforms. This would provide a theoretical foundation for the innovation of digital agriculture and promote its sustainable development.

Classification of Agricultural Crops with Random Forest and Support Vector Machine Algorithms Using Sentinel-2 and Landsat-8 Images

Monitoring crop development and mapping cultivated areas are important for reducing risks to food security due to climate change. Remote sensing techniques contribute significantly to the efficient and effective management of agricultural production. In this study, agricultural fields (sunflower, wheat, maize, oat, chickpea, sugar beet, alfalfa, onion, fallow) and other fields (non-agricultural, pasture, lake) were identified by using Random Forest (RF) and Support Vector Machines (SVM) machine learning algorithms with Sentinel-2 and Landsat-8 images in the area covering Polatlı, Haymana and Gölbaşı districts of Ankara province Multi-temporal images were used to distinguish winter and summer crops, taking into account crop development periods. As a result of classification; the overall accuracy of RF and SVM models with S2 images are 89.5% and 84.6% and kappa coefficients are 0.88 and 0.83, while the overall accuracy of RF and SVM models with L8 images are 79% and 78.1% and kappa coefficients are 0.76 and 0.75. RF model was found to have higher prediction accuracy than SVM. Sentinel-2 imagery has a higher accuracy in all classes compared to Landsat-8, indicating that Sentinel-2 imagery with its high temporal and spatial resolution is more suitable and has a great potential for agricultural crop pattern detection.

Factors Influencing the Performance Evaluation of Agricultural Product Supply Chain in Guangxi Province, the People's Republic of China

The research questions of this paper are: (1) What factors have an impact on the performance management of the agricultural product supply chain in Guangxi Province? (2) What are the problems in the supply chain management of agricultural products in Guangxi Province? (3) What is the model of the factors influencing the performance of evaluation of agricultural product supply chain in Guangxi province, The People's Republic of China? Through the study of this paper, it is found that there are the following problems in the supply chain management of agricultural products in Guangxi Province: the quality of goods supplied by suppliers is uneven, the timeliness is slow, and the loss during transportation is large. The information systems of various agricultural enterprises are not fully popularized, data between different enterprises cannot be shared, the level of information management is not high enough, the efficiency of transportation management is low, and the quality of transported products cannot be guaranteed.  Major Findings: The research model takes the performance of the agricultural product supply chain in Guangxi Province as the dependent variable, the supplier performance evaluation (SPE) as the independent variable, and the information technology evaluation (ITE) and logistics performance evaluation (LPE) as the mediating variables. The validity of the survey plan and data was tested by confirmatory factor analysis and reliability analysis. Subsequently, the structural equation model and regression analysis were used to verify the various assumptions in the supply chain organization efficiency hypothesis model. This study deeply explores the many factors that affect the efficiency of the agricultural product supply chain. Finally, a new model for analyzing the factors affecting the performance of the agricultural product supply chain in Guangxi was constructed.

Recent Development Trends in Plant Protection UAVs: A Journey from Conventional Practices to Cutting-Edge Technologies—A Comprehensive Review

Uncrewed aerial vehicles (UAVs) for plant protection play a vital role in modern agricultural operations. In recent years, advancements in UAVs and pest control technologies have significantly enhanced operational efficiency. These innovations have addressed historical challenges in agricultural practices by improving automation and precision in managing insect pests, diseases, and weeds. UAVs offer high operational efficiency, wide adaptability to different terrain, and safe applications. The development and demand for these technologies have increased to boost agricultural production. In agricultural settings where conventional machinery struggles to carry out farming operations, UAVs have transformed farming practices by providing high operational efficiency and significant profitability. The integration of UAVs and other smart technologies has driven advancements. The UAV sector has received substantial attention as a convergence of production, service, and delivery, introducing synergy through the presence of several developing areas. The market for this technology is expected to grow in the future. In this comprehensive review, we analyzed an overview of historical research, diverse techniques, the transition from conventional to advanced application, development trends, and operational milestones across diverse cropping systems. We also discussed adoption and subsidy policies. In order to properly understand UAV operational efficiency, we also analyzed and discussed smart atomization systems, spray drift, droplet deposition detection technologies, and the capabilities of related technologies. Additionally, we reviewed the role of software programs, data-driven tools, biodegradable materials, payloads, batteries, sensing technologies, weather, and operational and spraying factors. Regulatory limitations, operating and farmer’s training, economic effects, and guidelines were also acknowledged in this review. This review highlights deficiencies and provides essential knowledge of the use of UAVs for agriculture tasks in different regions. Finally, we examine the urgency of UAV technology implementations in the agricultural sector. In conclusion, we summarize the integration of UAVs and their related technologies with applications and future research prospects, offering directions for follow-up research on the key technologies of UAVs and encouraging the enhancement of agricultural production management in terms of efficiency, accuracy, and sustainability.