Agricultural Operations Research Articles

Drone-Assisted Climate Smart Agriculture (DACSA): The design of the groundwork flow data for drone operations

The success of precision farming hinges on effective ground support and workflow. In pursuit of this, we undertook a thorough requirement study of the system necessary for precision farming and developed a precision farming data flow model in ground support. The prototype hardware ground support and conceptual data flow provided valuable guidance in the successful realization of Drone-Assisted Climate Smart Agriculture (DACSA). Using open-source software to accommodate a range of data processing algorithms becomes crucial in operationalizing ground support for precision farming. This study has culminated in a comprehensive prototype model for precision farming operations that can be executed with confidence. The management system of flow data for precision farming has been drawn, this platform is specifically crafted to streamline agriculture operations by transforming diverse inputs into useful spatial data. To maintain the growth of the database, it is necessary to incorporate it in the entire crop cycle. The integration of this database can significantly enhance the precision of predicting plant performance. While this innovative approach is still in progress, it has already demonstrated its potential in supporting informed decision-making. For the next, it is imperative that we prioritize research aimed at creating decision-support algorithms that can effectively gather and blend information pertaining to soil, crops, and weather into actionable maps. These maps must incorporate location-specific data and be utilized by agricultural professionals for on-site decision-making. Moreover, they must be well-suited for drone usage in tasks such as monitoring, mapping, or spraying.

ОБЩИЙ ПОДХОД К РЕШЕНИЮ ЗАДАЧИ ОБЕСПЕЧЕНИЯ РАБОТО-СПОСОБНОСТИ ТРАКТОРОВ В АПК

Agricultural production is characterized by the seasonality of mechanized work in the cultivation of crops. In addition, the amount of wear on aggregates and equipment systems is determined, on the one hand, by natural and climatic conditions and, on the other, by the nomenclature of operations performed in the planned period, organizational, production and technical conditions in a particular farm. Tractor units and systems are interconnect with each other and interdependent on each other, i.e. the amount of wear of some parts affects the wear of others. In this regard, the purpose of the research is to develop a concept for ensuring the operability of tractors in the agro-industrial complex by optimizing the resource consumption of aggregates and systems, taking into account the conditions of their operation. The most promising direction for improving the efficiency of tractor operation is the development of measures to optimize the resource consumption of their units and systems, taking into account the operating conditions of machinery in the agro-industrial complex. To predict changes in the physical parameter of a part, unit, and tractor system for the planned period of a certain amount of work, a concept is introduced - an indicator of resource consumption, which determines the amount of change in the diagnostic parameter of the unit state per unit of work performed. The assessment of the tractor condition is carry out by the level of equipment resource consumption, which makes it possible to promptly respond to the state of technical operation of equipment, the conditions of their operation and develop measures to optimize resource consumption. The resource consumption level (Urr) of the tractor, theoretically, can vary from 0 to 1. In this case, Urr=0 it is assumed that the tractor is written off from the balance sheet of the farm or is missing; with Urr=1, it is assumed that the tractor is in good condition and all tractor parameters have nominal values. With known patterns of changes in the level of resource consumption for various types of agricultural operations, it becomes possible to plan the range of work for each tractor for the planned period, taking into account the optimal resource consumption of their units and systems.

Do Agricultural Productive Services Impact the Carbon Emissions of the Planting Industry in China: Promotion or Inhibition?

Reducing carbon emissions from planting (PCE) is crucial for achieving the “dual carbon” objective within the agricultural sector. This paper aims to investigate the impact of agricultural productive services (APSs) on carbon emissions in the planting industry, providing novel insights into reducing carbon emissions in this field. Based on the panel data of 30 provinces and regions in China from 2005 to 2021, this study measured the PCE and the level of APSs. The internal relationship between the APSs and PCE is subsequently examined empirically using various statistical models, including the fixed effect model, instrumental variable model, mediating effect model, and threshold effect model. The findings suggest that the PCE experienced an initial increase, followed by a subsequent decrease between 2005 and 2021, with the peak observed in 2015. APSs has a significant inhibitory effect on PCE, which remains significant even after conducting various robustness tests. However, the carbon emission reduction effect of APSs varies across different crop-producing areas. The study also revealed that APSs can inhibit PCE by facilitating land transfer. Additionally, the inhibitory effect of APSs on PCE is influenced by the scale of land management and exhibits a U-shaped nonlinear relationship. To effectively reduce PCE and achieve sustainable agricultural development, policymakers should actively promote the integration of APSs with modern agriculture and form moderate-scale operations by facilitating land transfer to maximize APSs’ carbon emission reduction effect.

Development and implementation of a raspberry Pi-based IoT system for real-time performance monitoring of an instrumented tractor

The tractor serves as a crucial power source in agricultural operations. However, the tractor's power often remains underutilized due to a mismatch between the tractor and implement, considering specific field conditions. To enhance system output, it becomes vital to acquire data on performance-related parameters for the tractor-implement combination. In this study we develop a real-time Instrumented Tractor Performance Monitoring System (ITPMS) using the Internet-of-Things (IoT). This system consists of a Raspberry Pi, a GPS sensor, a proximity sensor, a rotary potentiometer, and a three-point hitch dynamometer. The rotary potentiometer measures tillage depth, while the three-point hitch dynamometer used to measure data on draft force. Proximity sensors are installed on a two-wheel drive (2WD) tractor to measure forward speed and drive-wheel slip. We establish a dedicated web server using a Google® Firebase® project to store data from all sensors through Raspberry Pi. Additionally, we design a web interface and a mobile application to provide real-time data generated from the sensors. Field experiments were done to evaluate and monitor the performance parameters of the tractor-implement combination utilising the developed ITPMS. The results demonstrate that the system effectively monitors the performance parameters necessary for tractor-implement combination. Furthermore, the system's capability to update data to the IoT server in real-time is validated. Overall, the development and implementation of this Raspberry Pi based IoT system, provides a reliable and efficient solution for real-time performance monitoring of instrumented tractors.

Towards Understanding Cat Vocalizations: A Novel Cat Sound Classification Model Based on Vision Transformers

Animal sound perception systems are highly developed compared to humans, crucial for survival in natural environments, with some species possessing specialized sensory capabilities such as vision, hearing, touch, and environmental awareness. Understanding animal sounds not only aids in their own communication and survival but also benefits humans in various fields including security, natural disaster prediction, ecological research, bioacoustics, precision agriculture, and search and rescue operations. Motivated by this fact, this study investigated the classification of cat sounds using deep learning models based on Vision Transformer (ViT) and Convolutional Neural Network (CNN) architectures. Cat vocalizations, represented as mel-spectrograms, were classified using models trained on a diverse dataset of cat sounds. Experimental results demonstrated the superiority of the proposed model based on Microsoft’s BERT Pre-Training of Image Transformers (BEiT) over the state-of-the-art as it obtained an exceptional accuracy of 96.95%. Additionally, it was observed that the proposed models based on ViT outperformed CNN-based models, highlighting the efficacy of transformer architectures in capturing complex patterns within audio data. These findings underscore the potential of ViT architectures in decoding animal communication systems and advancing wildlife conservation efforts.

Header Height Detection and Terrain-Adaptive Control Strategy Using Area Array LiDAR

During the operation of combine harvesters, the cutting platform height is typically controlled using manual valve hydraulic systems, which can result in issues such as delays in adjustment and high labor intensity, affecting both the quality and efficiency of the operation. There is an urgent need to enhance the automation level. Conventional methods frequently employ single-point measurements and lack extensive area coverage, which means their results do not fully represent the terrain’s variations in the area and are prone to local anomalies. Given the inherently undulating terrain of farmland during harvesting, a control strategy that does not adjust for minor undulations but only for significant ones proves to be more rational. To this end, a sine wave superposition model was established to simulate three-dimensional ground elevation changes, and an area array LiDAR was used to collect 8 × 8 data for the header height. The effects of mounds and stubble on the measurement results were analyzed, and a dynamic process simulation model for the solenoid valve core was developed to analyze the on/off delay characteristics of a three-position four-way electromagnetic directional valve. Moreover, a physical model of the hydraulic system was constructed based on the Simscape module in Simulink, and the Bang Bang switch predictive control system based on position threshold was introduced to achieve early switching of the electromagnetic directional valve circuit. In addition, an automatic control system for cutting platform height was designed based on an STM32 microcontroller. The control system was tested on the hydraulic automatic control test rig developed by Shanxi Agricultural University. The simulation and experimental results demonstrated that the control system and strategy were robust to output disturbances, effectively enhancing the intelligence and environmental adaptability of agricultural machinery operations.

A Quantum-Resistant Identity Authentication and Key Agreement Scheme for UAV Networks Based on Kyber Algorithm

Unmanned aerial vehicles (UAVs) play a critical role in various fields, including logistics, agriculture, and rescue operations. Effective identity authentication and key agreement schemes are vital for UAV networks to combat threats. Current schemes often employ algorithms like elliptic curve cryptography (ECC) and Rivest–Shamir–Adleman (RSA), which are vulnerable to quantum attacks. To address this issue, we propose LIGKYX, a novel scheme combining the quantum-resistant Kyber algorithm with the hash-based message authentication code (HMAC) for enhanced security and efficiency. This scheme enables the mutual authentication between UAVs and ground stations and supports secure session key establishment protocols. Additionally, it facilitates robust authentication and key agreement among UAVs through control stations, addressing the critical challenge of quantum-resistant security in UAV networks. The proposed LIGKYX scheme operates based on the Kyber algorithm and elliptic curve Diffie–Hellman (ECDH) key exchange protocol, employing the HMAC and pre-computation techniques. Furthermore, a formal verification tool validated the security of LIGKYX under the Dolev–Yao threat model. Comparative analyses on security properties, communication overhead, and computational overhead indicate that LIGKYX not only matches or exceeds existing schemes but also uniquely counters quantum attacks effectively, ensuring the security of UAV communication networks with a lower time overhead for authentication and communication.

Financial risk management in agriculture: mitigation strategies and protection against economic variables

This work explores essential strategies for managing financial and operational risks in the agricultural sector, a crucial pillar of the global economy, but one that is exposed to a wide range of financial risks. These risks include fluctuations in commodity prices, access to credit, operational risks, climate change, exchange rate variations, and government policies. To ensure the sustainability and profitability of agricultural operations, it is imperative that producers adopt effective financial risk management strategies. This article addresses the main mitigation and protection strategies against economic variables in the context of agriculture. The methodology of this study is based on a systematic literature review, focused on identifying, analyzing, and synthesizing financial risk management strategies in agriculture. The review covers the analysis of advanced precision technologies, which optimize agricultural production and reduce waste, as well as sustainable agricultural practices, adequate insurance to mitigate the impacts of climate risks, and commodity hedging. Strategies for diversifying crops and agricultural activities to distribute risk and improve financial resilience are also discussed. In conclusion, effective financial risk management in agriculture requires a multifaceted approach that integrates mitigation strategies for various types of risks. The adoption of advanced technologies, sustainable agricultural practices, adequate insurance, and a thorough understanding of market and policy dynamics are fundamental to strengthening resilience and ensuring the sustainability of the agricultural sector against adverse economic variables.

Classification of Avocado Ripeness Levels Using CNN Method

This research aims to develop a model for classifying the ripeness level of avocados using the Convolutional Neural Network (CNN) algorithm. The dataset comprises images of avocados categorized into three classes: unripe, ripe, and overripe. The CNN model is trained to classify the images into one of these three categories. The results indicate that the developed model can classify avocado images with high accuracy. The primary tool used for developing and implementing this method is MATLAB R2022a. The CNN algorithm is utilized to recognize and classify the ripeness level of avocados. This process involves several image processing steps, starting with preprocessing, image enhancement, and segmentation to isolate the avocado area. The dataset used in this research consists of 452 images distributed in 3 classes (unripe with 142, ripe with 66, and rotten with 244), with 80% used for training and 20% for testing. After 10 accuracy tests, the results indicate an accuracy rate of 90%. Additionally, features extracted from the images include color, shape, size, and texture characteristics, such as Mean, Standard Deviation, Kurtosis, Skewness, Variance, Entropy Value, Maximum Pixel, and Minimum Pixel. This research contributes to the field of agricultural technology by providing a robust method for the automatic classification of avocado ripeness. The findings are expected to facilitate accurate and efficient recognition of avocado ripeness, thereby supporting agricultural practices and market operations. Future research could explore the use of data augmentation techniques to further improve the accuracy and generalization of this model.

Assessing the carbon footprint of the agriculture, forestry, and fishing industry of Australia

Purpose This study examines the carbon emissions of Australia’s agriculture, forestry and fishing sectors from a consumption perspective to develop effective policy frameworks. The objective is to identify key supply chains, industries and products contributing to these emissions and provide recommendations for sustainable development. Design/methodology/approach A multiregional input-output lifecycle assessment was conducted using the Australian Industrial Ecology Virtual Laboratory (IELab) platform to disaggregate sectors and enable benchmarking against other economic sectors. Findings In 2018, the “agriculture, forestry, and fishing” sector was responsible for 12.15% of Australia’s carbon footprint. Major contributors included the “electricity, gas, water, and waste” category (26.1%) and the sector’s activities (24.3%). The “transport, postal, and warehousing” sector also contributed 18.4%. Within the industry, the agriculture subsector had the highest impact (71.3%), followed by forestry and logging (15%) and fishing, hunting and trapping (7.6%). Aquaculture and supporting services contributed 6.1%. Research limitations/implications The principal constraint encountered by the present study pertained to the availability of up-to-date data. The latest accessible data for quantifying the carbon footprint within Australia’s agriculture, forestry and fishing sector, utilizing the Input-Output analysis methodology through the Australian Industrial Ecology Virtual Laboratory (IELab) platform, about 2018. Practical implications The findings of this study provide policymakers with detailed insights into the carbon footprints of key sectors, highlighting the contributions from each subsector. This information can be directly used to develop effective emission-reduction policies, with a focus on reducing emissions in utility services, transport and warehousing. Social implications The study, by raising public awareness of the significant role of industrial agricultural methods in Australia’s carbon footprint and emphasizing the importance of renewable energy and sustainable fuels for electricity generation and road transport, underscores the urgent need for action to mitigate climate change. Originality/value This study stands out by not only identifying the most impactful industries but also by providing specific strategies to reduce their emissions. It offers a comprehensive breakdown of specific agricultural activities and outlines mitigation strategies for utility services, agricultural operations and transport, thereby adding a unique perspective to the existing knowledge.

Using Machine Learning to Identify Diseases and Perform Sorting in Apple Fruit

Fruit diseases play a major role in global agriculture, leading to substantial crop losses and influencing food production and economic stability. In this age of Industry 4.0 the fruit sorting is an important part in the food processing wherein this work plays a vital role. In this study, a solution for the detection and classification of apple fruit diseases is proposed and experimentally validated. Deep learning models offer promise for automating disease identification using fruit images, but encounter obstacles such as therequirement for extensive training data, computational complexity, and the risk of overfitting. This study introduces an innovative convolutional neural network (CNN) architecture aimed at addressing these challenges by incorporating a reduced number of layers, thus alleviating computational burdens while maintaining performance. Additionally, augmentation techniques such as shift, shear, scaling, zoom, and flipping are employed to diversify the training set without additional image acquisition. Our CNN model is specifically trained to identify common apple crop diseases like Scab, Rot, and Blotch. Rigorous experimental evaluation demonstrates the effectiveness ofour model, achieving a remarkable classification accuracy of 95.37%. Significantly, our model demonstrates reduced storage requirements and faster execution times compared to existing deep CNN architectures, enabling deployment on handheld devices and resource-limited environments. While other CNN models may offer similar accuracy levels, our approach emphasizes efficiency and resource optimization, rendering it practical for real-world applications in agriculture. Furthermore, our CNN model exhibits resilience to environmental variations and imaging parameters, enhancing its applicability across diverse agricultural settings. By leveraging advanced machine learning techniques, the approach developed in this experimental work contributes to modernizing fruits and vegetables sorting operations in food processing, crop management practices thus promoting agricultural sustainability. The scalability and portability of our model make it suitable for deployment in both small-scale farms and large-scale agricultural operations.

Impact of Drought on the Livelihoods of Farmers in Western Odisha: A Comparison Between Drought and Non-Drought Years

The agriculture of our country is increasingly affected due to the irregular climatic variations like drought, flood, etc. which is causing great distress to agriculture farmers financially, socially and emotionally during the period. The present study was undertaken to assess the impact of drought on livelihood of agriculture farmers in drought versus non-drought years in drought prone western Odisha. 194 respondents engaged in agriculture farming as their primary occupation were selected randomly from 3 districts of Western Odisha by stratified random sampling. Ten pertinent questions were selected after due consultation with different stakeholders of the agricultural sector, Govt. officials, University faculties and experts of the field to study the impact of drought. The questions were designed to be answered either in yes or no, which were related to the social, economic, food security and family responsibilities of the agricultural farmer and his family. It was found that the respondents were not able to meet the basic requirements in drought years which could be achieved in non-drought years through agricultural operations. This study also indicated that the basic needs like food, medicine and social needs are severely affected in the drought hit years as compared to the non-drought years. The famers were not even able to purchase medicines in drought hit years. The farmers showed their inability to take care of the basic needs of family during drought. The Government of Odisha may promote secondary sources of livelihood generating options like dairy farming, poultry farming, beekeeping, goat farming, etc., in the drought affected districts of Odisha along with agriculture for meeting basic needs of life during drought period.

Unveiling the impact and mechanism of digital technology on agricultural economic resilience

Enhancing the economic resilience of agriculture is essential for promoting sustainable and high-quality agricultural development. The emergence of digital technology has created new opportunities in this field. However, existing research predominantly focuses on traditional agricultural factors and technologies. Therefore, the impact of digital technology on agricultural economic resilience within the broader context of the “production–operation–industry” system in agriculture has not been comprehensively explored. To bridge this gap, this study analyzes panel data from 30 Chinese provinces from 2011 to 2020. It employs the static Van Dorn’s law and a dynamic spatial panel model to examine how digital technology empowers agricultural resilience. The findings indicate a continuous strengthening of digital technology development in China, albeit with significant polarization and spatial imbalances. Moreover, the resilience of the agricultural economy undergoes notable fluctuations, initially narrowing and subsequently displaying an upward trend. Digital technology clearly plays a pivotal role in empowering resilience through agricultural scale operation, industrial transformation, and technological progress. Its impact, particularly on the promotion of resilience in the eastern region and non-grain-producing areas and on high-level agricultural economies, also shows regional and technological variations.

Efficiency of Agricultural Insurance in Facilitating Modern Agriculture Development: From the Perspective of Production Factor Allocation

Agricultural insurance is instrumental in consolidating the gains of poverty alleviation and advancing rural revitalization. It significantly aids in the efficient allocation of agricultural production factors, which in turn enhances agricultural output and bolsters the evolution of modern agriculture. Therefore, utilizing data from 583 household surveys and employing endogenous transformation and intermediary effect models, this paper analyzes the production factor allocation effect and specific mechanism of agricultural insurance. It focuses on small-scale farmers and new agricultural operators, exploring how insurance contributes to the advancement of modern agricultural practices. The results show the following: (1) Agriculture insurance can significantly affect the agricultural scale input behavior of farmers such as land input scale and input scale, agricultural machinery application behavior such as the degree of mechanization and water conservancy application, agricultural technology adoption behavior, and planting structure selection behavior, thereby helping to modernize agriculture. (2) There is heterogeneity in the impact of agriculture insurance on the allocation of production factors for small farmers and new agricultural operators. For small farmers, agriculture insurance has a significant promoting effect on their agricultural machinery application behavior, agricultural technology adoption behavior, and planting structure selection behavior. For new agricultural operators, agriculture insurance significantly promotes their agricultural scale input behavior, agricultural machinery application behavior, and agricultural technology adoption behavior. (3) In terms of the mechanism of action, agriculture insurance mainly promotes agricultural scale input behavior through land transfer, facilitates agricultural machinery application behavior by purchasing agricultural machinery equipment and services, encourages agricultural technology adoption behavior by strengthening agricultural technology training, and enhances professional production levels by increasing the scale of insured planting, thereby contributing to the development of modern agriculture. Based on this, several policy suggestions have been proposed. These include enhancing the directionality of agriculture insurance policies, improving the collaborative interaction mechanism between agriculture insurance and agricultural credit financing, and adopting certain reward and punishment measures to curb moral hazard.

Circular economy of the agricultural sector: Strategies and challenges in the context of globalisation

The study aimed to validate the necessity of formulating and executing plans for incorporating the concepts of the circular economy into Ukraine's agriculture sector, while considering the impacts of globalisation and problems arising from the war. This study examined the fundamental principles and intellectual underpinnings that differentiate the circular economy from the conventional economic model. The undamental concept of the circular economy in the agricultural sector was justified, and the primary benefits of implementing this economic model in the field of agriculture were established. The European Commission has approved the strategy for the Common Agricultural Policy for the period 2023-2027. This strategy emphasises the primary responsibilities that EU member states must undertake to implement the principles of the circular economy in agriculture. The primary patterns in the operation of agriculture in Ukraine are recognised, and the significance of the agricultural sector in the framework of gross domestic product (GDP) formation and foreign exchange profits is established. The destruction of infrastructure, mining activities, and occupation of regions resulted in a decline in the cultivated land and animals, leading to a loss in physical productivity. Due to the war, there has been a decline in production and logistics capabilities, resulting in a large fall in the export of agricultural products. Consequently, the profitability of firms in this sector of the Ukrainian economy has also decreased. Ukrainian agricultural firms lack the financial resources to independently support creative development, which includes the implementation of new technology solutions that form the foundation of the circular economy. Considering the effects of globalisation and military conflict, it is justified that prioritising innovation and investment support, particularly by the State, should be a strategic direction for implementing the principles of the circular economy of agriculture in Ukraine. This approach will enhance economic efficiency and create new opportunities

Effects of conservation agriculture technologies on soil macrofauna community attributes in rainfed agriculture system

AbstractInappropriate rainfed crop management, especially wrong tillage and crop residue management, led to reduce the diversity of soil biological quality, especially soil macrofauna community, in semiarid region. This study was conducted to investigate the effect of 5 year conservation agriculture operation techniques on the quality and the diversity indices of soil macrofauna community in the rainfed lands in the west of Iran. This experiment was carried out in the statistical format of split‐split plots based on a randomized completely block design with three replications. The main treatments include control or conventional tillage, compound tillage, chisel tillage, and direct cultivation (no tillage) with three sub‐treatments of plant residues (no residues, one‐third, and two‐thirds of plant residues) in rainfed wheat (Triticum aestivum) rotation with rainfed chickpea (Cicer arietinum). Changes in soil biological quality indicators, such as soil microbial respiration were measured. Also, the indicators related to the population of soil macrofauna, including biodiversity index, uniformity index, and species richness, were measured and the effect of treatments on the measured indicators were compared with the classical statistical method of analysis of variance and Duncan's mean comparison test. The results showed that conservation agricultural treatments, including no‐tillage treatment, have led to significant increase of 19% and 15% of macrofauna biodiversity index in wheat and chickpea, respectively. Also, keeping one‐third of the wheat residues has led to an 18% increase in the biodiversity index of the soil macrofauna during the evaluation period. Biodiversity index and taxonomic richness of soil macrofauna were generally higher in the years of wheat than chickpea, and on the contrary, the species uniformity index of soil macrofauna biological community was higher in chickpea than wheat.

Performance analysis of agricultural machinery with aid digital agriculture

Digital agriculture has increasingly gained strength in the countryside, as it is considered a byproduct of growing globalization. As the search for production optimization, cost reduction, and productivity increase continues, these tools become the great allies of the producer in the final agricultural production process.Therefore, agricultural operations must also follow this evolution in the development of technological improvements, which carry positive aspects for large-scale production. In this context, the objective of this work was to evaluate the performance of seeders-fertilizers with different input distribution mechanisms. Since one of the biggest expenses in the production process is the acquisition of seeds and fertilizer, any savings in production costs become a significant advantage for the producer, who will have a greater net income. The experimental design used to evaluate the data followed the premises of Statistical Process Control (SPC), and the results were assessed using individual value control charts. It was concluded that the production process was negatively affected by the performance of the seeders-fertilizers, as in one area, more seeds were falling than necessary, increasing the cost per hectare and not adhering to the principles of cost minimization and profit maximization. Conversely, in the second area evaluated, the machinery's performance resulted in fewer seeds being deposited than planned. Consequently, according to the results, the producer lost the opportunity to produce 5.85 sc/ha without reducing costs in the operation, as the seeder simply did not deposit the seeds, thereby reducing the gain per area, which could have been more productive, increasing final profitability.

Rice Growth and Leaf Physiology in Response to Four Levels of Continuous Drought Stress in Southern China

Exploring the growth and physiological response mechanisms of rice under continuous drought stress circumstances can provide a significant scientific foundation and technological assistance for meeting drought difficulties, improving drought resistance and rice (Oryza sativa L.) output, and ensuring food security. In this study, a rice field experiment was conducted under a rain shelter with five different treatments set up: P1 (drought stress from tillering stage), P2 (drought stress from jointing–booting stage), P3 (drought stress from heading–flowering stage), P4 (drought stress from grain filling stage), and CK (adequate water management throughout the growth stage). Continuous drought stress from different growth stages with four levels (mild, medium, moderate, and severe). The results showed that the effects of different drought stress treatments on rice growth varied significantly. Compared with the CK treatment, plant height was reduced by 12.10%, 8.14%, 3.83%, and 1.06% in the P1, P2, P3, and P4 treatments, respectively, and the number of tillers was reduced by 23.83%, 18.91%, 13.47%, and 8.68%, respectively. With the increase in drought stress levels, SPAD values and Rubisco activity of rice leaf continued to decrease; SOD activity showed a decreasing trend, but the decreasing trend of POD and CAT activities was not significant, while MDA content showed an increasing trend. For yield components, continuous drought stress significantly reduced spike length of rice by an average of 3.5%, effective number of spikes by 18.9%, thousand grain weight by 3.7%, grain number per spike by 11.6%, and fruiting rate by 1.8%, respectively, compared to CK treatments during the growth period. In general, continuous drought stress during the early growth period affected the effective spike number and the grain number per spike. Continuous drought stress after the grain filling stage had the least effect on yield (17.62% of yield reduction), and water use efficiency (1.76 kg m−3) was much higher than other treatments. These researchers’ findings provide insight into how rice physiology and growth react to continuous drought stress, which is significant for agricultural operations.

A data-driven approach to agricultural machinery working states analysis during ploughing operations

In the field of precision agriculture, there is a significant shift towards data-driven methodologies that considerably enhance the efficiency and sustainability of agricultural operations. This research investigates the application of CAN-Bus and GNSS data to develop a comprehensive analysis of agricultural machinery's operational states during ploughing operations through advanced data analytics techniques, including machine learning. The primary tool utilized in this study is the Random Forest classifier, a robust algorithm well-suited for handling the complexity and volume of data typical in modern agricultural settings. The study evaluates Random Forest models trained on various feature subsets to accurately identify different operational states of agricultural machinery, including idle, moving, turning, and working states. By merging CAN-Bus data, which capture real-time operational parameters, with GNSS data, providing spatial and temporal context, it is possible to achieve a comprehensive understanding of machinery behaviour and its interaction with field conditions. This integration significantly enhances decision-making capabilities in farm management, leading to more effective and efficient operations.Furthermore, the findings from this study contribute to the broader agricultural community by illustrating how data-driven approaches can harness the vast amounts of data generated by modern agricultural machinery. This research underscores the potential of machine learning modelsnot only to interpret complex data sets but also to transform these insights into actionable knowledge, which can lead to more precise and sustainable agricultural practices. Overall, this study offers a systematic approach for analysing agricultural data and lays the groundwork for future advancements in incorporating machine learning and IoT technologies into the agricultural sector. This aims to enhance productivity and sustainability in farming practices.

Nonnative pest establishment: Spatial patterns and public detection

Early detection of new pests can reduce their long-term impacts by enabling more rapid management response. Knowledge of pest establishment risk and background rates of detection (e.g., by the public) can help inform more cost-effective targeting of formal early detection survey programs. Here we quantify county-level locational attributes associated with pest establishment and detection by the public using data for 113 new pest incursions detected in the United States from 2010 through 2018. Aligning with expectations, we find a higher likelihood of new pest establishment in counties with higher human population numbers, nearer to ports (<250 km), and with amenable climate characteristics. Controlling for potential sample selection issues, we find that pests are less likely to be first detected by the public (e.g., homeowners, community members) versus by other sources (e.g., agency surveys, researchers, or agricultural operators) in counties with higher total crop sales values and lower human population number. The negative association between public detection and high agricultural values may reflect greater survey efforts by other sources (e.g., by agency surveillance programs, researchers, and agricultural operators) in high-value agricultural areas. The positive association between public detection and human population size may reflect larger numbers of public detectors (i.e., people) available to encounter the pests. Our models provide spatially explicit estimates of the likelihood of new pest establishment across U.S. counties and of the likelihood that an established pest would first be detected by the public. These estimates can serve as quantitative inputs to decision-support activities for new pest surveillance planning.