Search

In the public debate, plant protection is often reduced to the use of synthetic chemical pesticides and their possible consequences, especially for the environment. In fact, the protection of our cultivated plants is almost inherent in the system, because many of the cultivated plants have been deprived of their own defence mechanisms in the course of their cultivation in order to make them digestible for us or even edible in the first place. The instruments of sustainable plant protection consist of a multitude of options for action which, due to the progressive loss of important active substances in the future and the demands of society, are increasingly needed in their interplay to protect plants from pests, competition and diseases. In addition to low-risk or selective crop protection products and breeding efforts, technical innovations in equipment technology, forecasting, and farm and machinery management are particularly important for comprehensive crop protection. In addition to the conservation of natural resources, the focus must be on the production of safe, regional and high-quality food as well as the economic livelihood of the predominantly family-owned farms. Existing conflicting goals must be resolved in an open and consensus-oriented dialog in order to achieve social acceptance for the protection of our crops. [ABSTRACT FROM AUTHOR]

Due to the complexity and the magnitude of optimisation models that appeared in sustainable supply chain management (SSCM), the use of meta-heuristic algorithms as competent solution approaches is being increased in recent years. Although a massive number of publications exist around SSCM, no extant paper explicitly investigates the role of meta-heuristics in the sustainable (forward) supply chain. To fill this gap, a literature review is provided on meta-heuristic algorithms applied in SSCM by analyzing 160 rigorously selected papers published by the end of 2020. Our statistical analysis ascertains a considerable growth in the number of papers in recent years and reveals the contribution of 50 journals in forming the extant literature. The results also show that in the current literature the use of hybrid meta-heuristics is overtaking pure meta-heuristics, the genetic algorithm (GA) and the non-dominated sorting GA (NSGA-II) are the most-used single- and multi-objective algorithms, the aspects of sustainability are mostly addressed in connection with product distribution and routing of vehicles as pivotal operations in supply chain management, and last but not least, the economic-environmental category of sustainability has been further noticed by the scholars. Finally, a detailed discussion of findings and recommendations for future research are provided. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to analyse and classify the literature that contributed to three questions, namely, what are the benefits of big data analytics (BDA) in the field of supply chain management (SCM) and logistics, what are the challenges in BDA applications in the field of SCM and logistics and what are the determinants of successful applications of BDA in the field of SCM and logistics. Design/methodology/approach: This paper conducts a systematic literature review (SLR) to analyse the findings of 44 selected papers published in the period from 2016 to 2020, in the area of BDA and its impact on SCM. The designed protocol is composed of 14 steps in total, following Tranfeld (2003). The selected research papers are categorized into four themes. Findings: This paper identifies sets of benefits to be gained from the use of BDA in SCM, including benefits in data analytics capabilities, operational efficiency of logistical operations and supply chain/logistics sustainability and agility. It also documents challenges to be addressed in this application, and determinants of successful implementation. Research limitations/implications: The scope of the paper is limited to the related literature published until the beginning of Corona Virus (COVID) pandemic. Therefore, it does not cover the literature published since the COVID pandemic. Originality/value: This paper contributes to the academic research by providing a roadmap for future empirical work into this field of study by summarising the findings of the recent work conducted to investigate the uses of BDA in SCM and logistics. Specifically, this paper culminates in a summary of the most relevant benefits, challenges and determinants discussed in recent research. As the field of BDA remains a newly established field with little practical application in SCM and logistics, this paper contributes by highlighting the most important developments in contemporary literature practical applications. [ABSTRACT FROM AUTHOR]

• Reviewed "precision agriculture", "smart agriculture" and "digital agriculture" concepts. • Suggested "smart agriculture" and "digital agriculture" be phases of "precision agriculture". • Proposed a few research focuses on smart and digital technologies for agriculture. [ABSTRACT FROM AUTHOR]

Sensor-based agriculture monitoring systems have limited outcomes on the detection or counting of vegetables from agriculture fields due to the utilization of either conventional color transformations or machine learning-based methods. To overcome these limitations, this research is aimed at proposing an IoT-based smart agriculture monitoring system with multiple algorithms such as detection, quantification, ripeness checking, and detection of infected vegetables. This paper presents smart agriculture monitoring systems for Internet of Things (IoT) applications. The CHT has been applied to detect and quantify vegetables from the agriculture field. Using color thresholding and color segmentation techniques, defected vegetables have also been detected. A machine learning method-convolutional neural network (CNN) has been used for the development and implementation of all algorithms. A comparison between traditional methods and CNN has been simulated in MATLAB to find out the optimal method for its implementation in this agricultural monitoring system. Compared to the traditional methods, the CNN is the optimal method in this research work which performed better over the previously developed algorithms with an accuracy of more than 90%. As an example (case study), a tomato field in Chittagong, Bangladesh, was chosen where a camera-mounted mobile robot captured images from the agriculture field for which the proposed IoT-based smart monitoring system was developed. This system will benefit farmers through the digitally monitored output at an agriculture field in Bangladesh as well as in Malaysia. Since this proposed smart IoT-based system is still driven by bulky, costly, and limited powered sensors, in a future work, for the required power of sensors, this research work is aimed at the design and development of an energy harvester (hybrid) (HEH) based on ultralow power electronics circuits to generate the required power of sensors. Implementation of multiple algorithms using CNN, circular Hough transformation (CHT), color thresholding, and color segmentation methods for the detection, quantification, ripeness checking, and detection of infected crops. [ABSTRACT FROM AUTHOR]

Through this article, the author aims to identify the adoption rates and types of precision farming technologies embraced by farmers in the USA and the EU. Research papers in relation to the adoption of precision agriculture technologies were collected and divided into two groups, according to their geographic region: USA and EU. Books, scientific articles, reports and conference papers were reviewed and studied. Likewise, the material about the adoption of precision agriculture technologies was accumulated. The level of adoption in the USA differs from one state to another. The percentage rate of adoption is higher in the Southern States, and the overall adoption of precision agriculture technologies reaches to about 91%. United Kingdom, Denmark and Germany have higher rates of adoption compared with other countries in the EU. Similarly, the percentage rate of adoption is higher in the USA in comparison with EU countries. In the USA prevails a diversification of precision agriculture technologies adopted by US farmers. On the contrary, in the EU, the majority of research papers reported mainly some level of adoption of yield monitors/mapping and variable rate technologies for applying inputs. [ABSTRACT FROM AUTHOR]

Pesticides are largely used at worldwide level to improve food production, fulfilling the needs of the global population which is increasing year by year. Although pesticides are beneficial for crop production, their extensive use has serious consequences for the pollution of the produced food as well as for soil and groundwaters. Indeed, it is reported that 50% of sprayed pesticides reach different destinations other than their target species, including soil, surface waters, and groundwaters. For this reason, we developed a flower-like origami paper-based device for pesticides detection in aerosol phase for precision agriculture. In detail, the paper-based electrochemical platform detects paraoxon, 2,4-dichlorophenoxyacetic acid, and glyphosate at ppb levels by measuring their inhibitory activity towards three different enzymes namely butyrylcholinesterase, alkaline phosphatase, and peroxidase enzyme, respectively. This integrated electrochemical device is composed of three office paper-based screen-printed electrodes and filter paper-based pads loaded with enzymes and enzymatic substrates. The pesticide detection is carried out by measuring through chronoamperometric technique the initial and residual enzymatic activity by using a smartphone-assisted potentiostat and evaluating the percentage of inhibition, proportional to the amount of aerosolized pesticides. This paper-based device was able to detect the three classes of pesticides in aerosol phase with limits of detection equal to 30 ppb, 10 ppb, and 2 ppb, respectively for 2,4-D, glyphosate, and paraoxon. [Display omitted] • Paper-based flower like biosensor for pesticide detection. • Pesticide multiclass analysis using origami paper-based devices. • Smartphone paper-based devices for boosting precision agriculture. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has triggered new research areas in supply chain resilience. One of these new areas is viability. Viability extends the resilience understanding from performance-based assessment of firm’s responses to disruptions towards survivability of both supply chains and associated ecosystems not only during some short-term disruptions but also under conditions of long-term crises. To explore the state-of-the-art knowledge on methods, models, capabilities, and technologies of supply chain viability, we edited this important IJPR special issue. To introduce the special issue, we review the existing literature on supply chain viability, conceptualise seven major pillars of supply chain viability theory (i.e. viable supply chain design, viability in process planning and control, ripple effect, intertwined and reconfigurable supply networks, ecosystems, digital supply chain, and Industry 5.0), and establish some associated future research directions. The findings of this editorial paper, as well as the articles in the special issue, can be used by researchers and practitioners alike to consolidate recent advances and practices of viability in supply chain networks and lay the solid foundation for further developments in this area. [ABSTRACT FROM AUTHOR]

This document is a compilation of summaries of various research papers related to different aspects of the food supply chain. The summaries cover topics such as sustainability in meat production, information accessibility in logistics response, supplier diversity programs, the use of drones in agriculture, digital traceability, circular supply chains, reducing food loss, digital platforms in e-commerce, transparency and traceability in agri-food supply chains, zero-deforestation supply chain management, the organic pineapple value chain, and integrating technology and sustainability in the agri-food value chain. Each summary provides a brief overview of the key findings and implications of the respective research paper. The document aims to provide library patrons with a diverse range of perspectives on the challenges and innovations in the food supply chain, allowing them to explore specific topics of interest in more depth. [Extracted from the article]

Vegetation indices provide information for various precision-agriculture practices, by providing quantitative data about crop growth and health. To provide a concise and up-to-date review of vegetation indices in precision agriculture, this study focused on the major vegetation indices with the criterion of their frequency in scientific papers indexed in the Web of Science Core Collection (WoSCC) since 2000. Based on the scientific papers with the topic of "precision agriculture" combined with "vegetation index", this study found that the United States and China are global leaders in total precision-agriculture research and the application of vegetation indices, while the analysis adjusted for the country area showed much more homogenous global development of vegetation indices in precision agriculture. Among these studies, vegetation indices based on the multispectral sensor are much more frequently adopted in scientific studies than their low-cost alternatives based on the RGB sensor. The normalized difference vegetation index (NDVI) was determined as the dominant vegetation index, with a total of 2200 studies since the year 2000. With the existence of vegetation indices that improved the shortcomings of NDVI, such as enhanced vegetation index (EVI) and soil-adjusted vegetation index (SAVI), this study recognized their potential for enabling superior results to those of NDVI in future studies. [ABSTRACT FROM AUTHOR]

At the end of 2019, a severe respiratory syndrome named COVID‐19 is started to be transmitted in the world and it has rapidly spread to a global pandemic. Every day, a series of data are collected for real‐time monitoring of the development of this pandemic. The data validation and the verification are becoming very important to manage the pandemic and give recommendations to the people. Nevertheless, sometimes, it is not possible to guarantee the truthfulness of such data and some information may be lost during collection. Due to its characteristics, the Blockchain technology can become an important support to face the COVID‐19 pandemic. In this regard, the aim of this research is to propose a literature review to understand how the blockchain technology has been used for health care and supply chain management to guarantee an efficient tracing, tracking, and monitoring solution, ensure a transparent and safe data transmission, and to delineate the emerging future research directions. [ABSTRACT FROM AUTHOR]

An epidemic outbreak largely disrupts supply chains (SCs) worldwide through plummeting business confidence, especially when it becomes a pandemic; its unpredictable re-emergence and spreadability may lead to inappropriate decision-making, in turn causing severe economic shocks. In March 2020, the coronavirus disease 2019 (COVID-19) outbreak attained a pandemic level, and many millions of cases were confirmed globally. Many countries reported an increasing number of active cases and formulated long-term lockdown guidelines, which resulted in an unexpected disruption of SCs. A key challenge in this scenario is that the rising number of confirmed COVID-19 cases does not necessarily reflect the already infected or asymptomatic cases. It is thus critical to understand the impact of asymptomatic carriers on the SC, as they may be the key driver of the novel virus spread, disrupting long-term SCs. This paper generalised the susceptible-exposed-infected-recovered (S-E-I-R) approach to create a mathematical model for which the impact of a proposed asymptomatic situation on the SC is evaluated through the basic reproduction number (R0), considered the main driver of SC disruption and the equilibrium status of infection over time. This paper presents an action plan for reducing disruption in the SC based on the R0 of the model. Overall, the current study as validated through a case study suggests that the asymptomatic-situation-based model is more convenient for critically understanding as well as forecasting the outbreak’s impact on SCs. This study also highlights different perspectives of SCs for managing such types of pandemics using modelling approaches. [ABSTRACT FROM AUTHOR]

The occurrence of maize diseases is frequent but challenging to manage. Traditional identification methods have low accuracy and complex model structures with numerous parameters, making them difficult to implement on mobile devices. To address these challenges, this paper proposes a corn leaf disease recognition model SNMPF based on convolutional neural network ShuffleNetV2. In the down-sampling module of the ShuffleNet model, the max pooling layer replaces the deep convolutional layer to perform down-sampling. This improvement helps to extract key features from images, reduce the overfitting of the model, and improve the model's generalization ability. In addition, to enhance the model's ability to express features in complex backgrounds, the Sim AM attention mechanism was introduced. This mechanism enables the model to adaptively adjust focus and pay more attention to local discriminative features. The results on a maize disease image dataset demonstrate that the SNMPF model achieves a recognition accuracy of 98.40%, representing a 4.1 percentage point improvement over the original model, while its size is only 1.56 MB. Compared with existing convolutional neural network models such as EfficientNet, MobileViT, EfficientNetV2, RegNet, and DenseNet, this model offers higher accuracy and a more compact size. As a result, it can automatically detect and classify maize leaf diseases under natural field conditions, boasting high-precision recognition capabilities. Its accurate identification results provide scientific guidance for preventing corn leaf disease and promote the development of precision agriculture. [ABSTRACT FROM AUTHOR]

Modern and precision agriculture is constantly evolving, and the use of technology has become a critical factor in improving crop yields and protecting plants from harmful insects and pests. The use of neural networks is emerging as a new trend in modern agriculture that enables machines to learn and recognize patterns in data. In recent years, researchers and industry experts have been exploring the use of neural networks for detecting harmful insects and pests in crops, allowing farmers to act and mitigate damage. This paper provides an overview of new trends in modern agriculture for harmful insect and pest detection using neural networks. Using a systematic review, the benefits and challenges of this technology are highlighted, as well as various techniques being taken by researchers to improve its effectiveness. Specifically, the review focuses on the use of an ensemble of neural networks, pest databases, modern software, and innovative modified architectures for pest detection. The review is based on the analysis of multiple research papers published between 2015 and 2022, with the analysis of the new trends conducted between 2020 and 2022. The study concludes by emphasizing the significance of ongoing research and development of neural network-based pest detection systems to maintain sustainable and efficient agricultural production. [ABSTRACT FROM AUTHOR]

This paper presents a systematic literature review on the application of Radio Frequency Identification (RFID) technology for sustainability purposes in various supply chains while primarily focusing on the pharmaceutical supply chain. The review takes into consideration sustainability from an economic, environmental, and social perspective and investigates the various aspects of such a system from both the manufacturer and consumer points of view. The paper highlights the importance, process, and effects of implementing RFID technology in the pharmaceutical supply chain to create a more sustainable and traceable system. The lack of strong scientific studies in the field of sustainability of RFID-enabled pharmaceutical supply chain was identified. In addition to the literature review, this paper also takes into account results from an original survey conducted with current industry professionals and their views on sustainability in the pharmaceutical chain. The review's findings can help frame future work and fill the gaps in the literature focusing on the sustainability impact of RFID technology in the pharmaceutical and healthcare supply chain. [ABSTRACT FROM AUTHOR]

Although plant disease recognition has witnessed a significant improvement with deep learning in recent years, a common observation is that current deep learning methods with decent performance tend to suffer in real-world applications. We argue that this illusion essentially comes from the fact that current plant disease recognition datasets cater to deep learning methods and are far from real scenarios. Mitigating this illusion fundamentally requires an interdisciplinary perspective from both plant disease and deep learning, and a core question arises. What are the characteristics of a desired dataset? This paper aims to provide a perspective on this question. First, we present a taxonomy to describe potential plant disease datasets, which provides a bridge between the two research fields. We then give several directions for making future datasets, such as creating challenge-oriented datasets. We believe that our paper will contribute to creating datasets that can help achieve the ultimate objective of deploying deep learning in real-world plant disease recognition applications. To facilitate the community, our project is publicly available at https://github.com/xml94/PPDRD with the information of relevant public datasets. [ABSTRACT FROM AUTHOR]

Data has evolved into one of the principal resources for contemporary businesses. Moreover, corporations have undergone digitalization; consequently, their supply chains generate substantial amounts of data. The theoretical framework of this investigation was built on novel concepts like big data analytics—artificial intelligence (BDA-AI) and supply chain ambidexterity's (SCA) direct impacts on sustainable supply chain management (SSCM) and indirect impacts on sustainable innovation ambidexterity (SIA) and environmental performance (EP). This study selected employees of manufacturing industries as respondents for environmental performance, sustainable supply chain management, big data analytics, artificial intelligence, and supply chain ambidexterity. The results from this study show that BDA-AI and SCA significantly affect SSCM. SSCM has significant associations with SIA and EP. Finally, SIA has a significant impact on EP. According to the results indicating the indirect impacts, BDA-AI has significant indirect relationships with SIA and EP by having SSCM as the mediating variable. Furthermore, SCA has significant indirect associations with SIA and EP, with SSCM as the mediating variable. Additionally, both BDA-AI and SCA have significant indirect associations with EP, while SIA and SSCM are mediating variables. Finally, SSCM has an indirect association with EP while having SIA as a mediating variable. The findings of this paper provide several theoretical contributions to the research in sustainability and big data analytics artificial intelligence field. Furthermore, based on the suggested framework, this study offers a number of practical implications for decision-makers to improve significantly in the supply chain and BDA-AI. For instance, this paper provides significant insight for logistics and supply chain managers, supporting them in implementing BDA-AI solutions to help SSCM and enhance EP. [ABSTRACT FROM AUTHOR]

This paper presents an actuator fault-tolerant control (FTC) strategy for a hexacopter unmanned aerial vehicle (UAV) designed specifically for precision agriculture applications. The proposed approach integrates advanced sensing techniques, including the estimation of Near-Infrared (NIR) reflectance from RGB imagery using the Pix2Pix deep learning network based on conditional Generative Adversarial Networks (cGANs), to enable the calculation of the Normalized Difference Vegetation Index (NDVI) for health assessment. Additionally, trajectory flight planning is developed to ensure the efficient coverage of the targeted agricultural area while considering the vehicle's dynamics and fault-tolerant capabilities, even in the case of total actuator failures. The effectiveness of the proposed system is validated through simulations and real-world experiments, demonstrating its potential for reliable and accurate data collection in precision agriculture. An NDVI test was conducted on a sugarcane crop using the estimated NIR to assess the crop's condition during its tillering stage. Therefore, the main contributions this paper include (i) the development of an actuator FTC strategy for a hexacopter UAV in precision agriculture applications, integrating advanced sensing techniques such as NIR reflectance estimation using deep learning network; (ii) the design of a flight trajectory planning method ensuring the efficient coverage of the targeted agricultural area, considering the vehicle's dynamics and fault-tolerant capabilities; (iii) the validation of the proposed system through simulations and real-world experiments; and (iv) the successful integration of FTC scheme, advanced sensing, and flight trajectory planning for reliable and accurate data collection in precision agriculture. [ABSTRACT FROM AUTHOR]

A precision agriculture approach that uses drones for crop protection and variable rate application has become the main method of rice weed control, but it suffers from excessive spraying issues, which can pollute soil and water environments and harm ecosystems. This study proposes a method to generate variable spray prescription maps based on the actual distribution of weeds in rice fields and utilize DJI plant protection UAVs to perform automatic variable spraying operations according to the prescription maps, achieving precise pesticide application. We first construct the YOLOv8n DT model by transferring the "knowledge features" learned by the larger YOLOv8l model with strong feature extraction capabilities to the smaller YOLOv8n model through knowledge distillation. We use this model to identify weeds in the field and generate an actual distribution map of rice field weeds based on the recognition results. The number of weeds in each experimental plot is counted, and the specific amount of pesticide for each plot is determined based on the amount of weeds and the spraying strategy proposed in this study. Variable spray prescription maps are then generated accordingly. DJI plant protection UAVs are used to perform automatic variable spraying operations based on prescription maps. Water-sensitive papers are used to collect droplets during the automatic variable operation process of UAVs, and the variable spraying effect is evaluated through droplet analysis. YOLOv8n-DT improved the accuracy of the model by 3.1% while keeping the model parameters constant, and the accuracy of identifying weeds in rice fields reached 0.82, which is close to the accuracy of the teacher network. Compared to the traditional extensive spraying method, the approach in this study saves approximately 15.28% of herbicides. This study demonstrates a complete workflow from UAV image acquisition to the evaluation of the variable spraying effect of plant protection UAVs. The method proposed in this research may provide an effective solution to balance the use of chemical herbicides and protect ecological safety. [ABSTRACT FROM AUTHOR]

At present, there is little research on the application of wireless sensor networks in the agricultural field. In order to improve the operating performance and practical effects of the intelligent agricultural system, based on data mining technology, this paper uses ZigBee wireless sensor network as the networking technology to cover all aspects of crops under the guidance of the concept of sustainable agricultural development, and realizes the data collection and remote-control process of the agricultural production process, and conducts data analysis and processing through data mining. Moreover, in order to improve the performance of the model, this paper proposes to use the single-point crossover multiple-generation genetic algorithm to optimize the weights and thresholds of the BP neural network to establish the Multi-Generation Genetic Algorithm Back Propagation MGABP model. In addition, in application, the analytic hierarchy process is introduced as the guidance mechanism of neural networks. Finally, this paper designs experiments to analyze the performance of the system constructed in this paper, and uses mathematical statistics to perform statistics on experimental results. The experimental analysis and statistical diagrams of various parameters shows the outcome of this study. The research results show that the intelligent agricultural system model constructed in this paper has certain practical effects. [ABSTRACT FROM AUTHOR]

The article highlights the worldwide dissemination of precision agriculture scientific researches published from the period of 1996–2018, data gathered in the Scopus citation database, using the science mapping method. The findings show that there is a constant rise in the number of publications in precision agriculture. The USA is not only leading in the adoption of precision agriculture technologies but also in the publication of papers, accompanied by China placed in second place. The most frequent keywords highlighted the main topics authors concentrated on more, and the national affiliation of most cited papers was the USA. The main prominence and contributions of the results present scientific research trends in precision agriculture in the last two decades, and demonstrate the main countries, authors and organizations who have contributed, and were more productive in this area. [ABSTRACT FROM AUTHOR]

This paper presents an innovative method for fabricating reflectarray antennas using inkjet printing technology on flexible substrates, markedly enhancing integration and manufacturability compared to traditional PCB methods. The technique employs inkjet printing to deposit conductive inks directly onto a flexible polyethylene naphthalate (PEN) substrate, seamlessly integrating feed and reflectarray components without complex assembly processes. This streamlined approach not only reduces manufacturing complexity and costs but also improves mechanical flexibility, making it ideal for applications requiring deployable antennas. The design process includes an origami-inspired folding of the substrate to achieve the desired three-dimensional antenna structures, optimizing the focal length to dimension ratio (F/D) to ensure maximum efficiency and performance. The feed and the reflectarray geometry are optimized for an F/D of 0.6, which achieves high gain and aperture efficiency, demonstrated through detailed simulations and measurements. For normal incidence, the configuration achieves a peak gain of 9.3 dBi and 48% radiation efficiency at 10 GHz; for oblique incidence, it achieves 7.3 dBi and 40% efficiency. The study underscores the significant potential of inkjet-printed antennas in terms of cost-efficiency, precision, and versatility, paving the way for new advancements in antenna technology with a substantial impact on future communication systems. [ABSTRACT FROM AUTHOR]

The development and utilization of unconventional water resources has become a strategy to alleviate the agricultural water crisis in many countries and regions. To understand the research progress, hot spots, and future trends in the field of unconventional water agricultural irrigation (UWAI), this paper systematically analyzes 6738 publications based on the core database of Web of Science 1990–2023 using the scientific bibliometric analysis software CiteSpace, VOSviewer, and Scimago Graphica. The results showed that the research on UWAI is always rapidly developing. Soil science, crop science, and bioengineering are the main disciplines involved. Most research on WUAI has occurred in China and the United States. Countries with higher levels of development tend to have more influence. Collaboration among authors is fragmented, and collaboration between authors and states needs to be strengthened. Through keyword analysis, the research hotspots are summarized as follows: (1) The effects of traditional and emerging pollutants brought by unconventional water irrigation on soil physicochemical properties, crop growth, and groundwater quality; (2) the health threats caused by pollutants entering the food chain and groundwater; (3) unconventional water utilization technologies, including rainwater harvesting agriculture, precision agriculture, and urban agriculture. Future research hotspots will focus on the mechanisms of pollutant solute transport and transformation in the water–soil–crop system under non-conventional water irrigation conditions and crop physiological responses. We suggest that the research on traditional and emerging pollutants in unconventional water should be strengthened in the future, and the risk control system of unconventional water irrigation should be improved. International cooperation should be strengthened, especially with poor countries in arid regions, to promote the formation of unified international standards and guidelines for non-conventional water irrigation in agriculture. [ABSTRACT FROM AUTHOR]

Recent advancements in deep learning have spurred the development of numerous novel semantic segmentation models for land cover mapping, showcasing exceptional performance in delineating precise boundaries and producing highly accurate land cover maps. However, to date, no systematic literature review has comprehensively examined semantic segmentation models in the context of land cover mapping. This paper addresses this gap by synthesizing recent advancements in semantic segmentation models for land cover mapping from 2017 to 2023, drawing insights on trends, data sources, model structures, and performance metrics based on a review of 106 articles. Our analysis identifies top journals in the field, including MDPI Remote Sensing, IEEE Journal of Selected Topics in Earth Science, and IEEE Transactions on Geoscience and Remote Sensing, IEEE Geoscience and Remote Sensing Letters, and ISPRS Journal Of Photogrammetry And Remote Sensing. We find that research predominantly focuses on land cover, urban areas, precision agriculture, environment, coastal areas, and forests. Geographically, 35.29% of the study areas are located in China, followed by the USA (11.76%), France (5.88%), Spain (4%), and others. Sentinel-2, Sentinel-1, and Landsat satellites emerge as the most used data sources. Benchmark datasets such as ISPRS Vaihingen and Potsdam, LandCover.ai, DeepGlobe, and GID datasets are frequently employed. Model architectures predominantly utilize encoder–decoder and hybrid convolutional neural network-based structures because of their impressive performances, with limited adoption of transformer-based architectures due to its computational complexity issue and slow convergence speed. Lastly, this paper highlights existing key research gaps in the field to guide future research directions. [ABSTRACT FROM AUTHOR]

Sensed and soil sample data are used in two approaches for mapping soil properties in precision agriculture: management zone (MZs) and contour maps. This is the second paper in a two-part series that focuses on contour maps. Detailed and accurate contour maps of soil properties for precision agriculture are often costly to produce because of the large sampling effort required. Such maps or those of sensed ancillary data are often simplified to represent MZs. This research investigated the accuracy of detailed maps of soil properties produced inexpensively from sensed data by transforming them to z-scores. The z-scores of ancillary values are then transformed to values of soil variables using the mean and standard deviation of a small soil data set. The errors from this mapping approach are examined with historic soil data from three field sites with different scales of spatial variation in the United Kingdom. Errors from the conversion of z-scores of sensed data to soil variable ranges are compared with those from MZ averages (Paper I in this series). For soil properties with a moderate relation to ancillary data, the errors related to the z-score conversion were small irrespective of sample size. The root mean squared errors associated with the MZ mean rather than values from the digital map were generally smaller except when sample size was very small. The results suggest that when the scale of variation is small and more samples are required to define MZs, calibrating z-scores of sensed ancillary data may provide better MZ averages than sampling on a grid; it also provides a detailed map of spatial variation within the field. The z-score conversion approach is less sensitive to sample size and captures small features of the variation compared to the standard 100 m grid sampling to determine MZ averages. [ABSTRACT FROM AUTHOR]

Sensed and soil sample data are used in two main approaches for mapping soil properties in precision agriculture: management zones (MZs) and contour maps. This is the first of two papers that explores maps of MZs. Management zones based on variation in sensed data that are related to the more permanent soil properties assume that the zones are multi-purpose. Soil properties are then often sampled on a grid to provide the average values of each property per zone. This paper examines the plausibility of this approach by examining how the number of samples taken on a grid and the application of kriging affect mean soil property values for MZs. The suitability of MZs based on ancillary data for managing several agronomically important properties simultaneously is also considered. These concepts are examined with historic soil data from four field sites in southern UK with different scales of spatial variation. Results showed that when the grid sampling interval is large, there is less difference in the means of properties between MZs, but kriging the soil data increased the differences between zones when the sampling interval was large and sample small. Sensed data are used increasingly to aid the identification of MZs, but these could not be considered multi-purpose at all sites. The MZs produced were most useful for phosphorus (P), pH and volumetric water content (VWC) at the Wallingford site and useful for most properties at the Clays and Y215 sites. For the latter site this was true only when the most dense data were used to calculate MZ averages. The results show that sampling interval for MZ averages should relate to the scale of variation or the size of the MZs at a site. The sampling density could be based on the variogram range of ancillary data. This research suggests that there should be 6–8 samples per zone to obtain accurate averages of soil properties. Nutrient data for more than one year were examined at two sites and showed that patterns remained consistent in the short term unless variable-rate management was used, but also the range of values changed in the short term. [ABSTRACT FROM AUTHOR]

This research delves into the transformation of agriculture through precision techniques and advanced technology. The focus is on an autonomous AI weed cutter, utilising TinyML, the ESP32 camera module, and a proximity sensor to revolutionise weed management. The study introduces a holistic approach by integrating TinyML's advanced computer vision techniques for real-time weed detection and classification. The ESP32 camera captures field images, processed by TinyML algorithms to identify crops and weeds accurately. Augmenting visual perception, a proximity sensor ensures safe navigation, detecting obstacles and maintaining safe distances from crops. This fusion empowers the system to make informed decisions. Field trials confirm exceptional weed detection and removal accuracy. Integration of TinyML-based vision, ESP32 camera tech, and the proximity sensor optimises efficiency, adaptability, and safety. Economic and environmental analyses highlight benefits: sustainable practices, potential yield increase, and reduced herbicide usage. In conclusion, this research showcases an autonomous AI weed cutter, seamlessly integrating TinyML, ESP32, and a proximity sensor. This technology amalgamation offers precise weed management, boosting efficiency, reducing environmental impact, and promoting sustainability in agriculture—a pivotal stride toward precision farming's future. [ABSTRACT FROM AUTHOR]

This paper presents an innovative approach to improving agricultural practices using modern technologies such as machine learning, IoT, and image processing. The proposed system includes two main modules: fertilizer prediction and crop recommendation and crop disease detection. The first module utilizes machine learning algorithms to analyze data from various sources such as soil sensors, weather stations, and historical crop yield data to predict optimal fertilizer requirements and recommend suitable crops for the specific soil type and climate. The second module employs image processing and Convolutional Neural Network (CNN) algorithms to detect crop diseases by analyzing images captured by IoT devices such as drones and cameras. The proposed system aims to increase crop yield, reduce the cost of crop maintenance, and promote sustainable farming practices. The results of this research demonstrate the potential of smart agriculture technologies in improving food security and agricultural sustainability. [ABSTRACT FROM AUTHOR]

The study of yield gaps has become more complex, prompting the use of varied approaches to measure yields and a wider range of factors to explain these gaps. In the Global North, the focus is on precision farming, whereas in sub-Saharan Africa (SSA), a broader perspective is necessary due to pronounced variability in farmland conditions. While biogeophysical and management factors have been traditional focal points in yield gap analyses, socio-economic and institutional factors are increasingly recognized as significant, especially in SSA. This review synthesizes research from the past decade in SSA that integrates biogeophysical, management, farm characteristics, and institutional factors in yield gap discussions. The findings indicate a slow shift in including socio-economic factors, with management, particularly nutrient supply and crop management, remaining predominant. However, there is a growing trend towards methodological diversity, such as the adoption of remote sensing and GIS in recent years. Case studies from Kenya and Ghana, utilizing field surveys, interviews, panel data, and spatial analysis, highlight how a multifaceted approach can enhance our understanding of the various elements influencing maize yield gaps in SSA. [ABSTRACT FROM AUTHOR]

This paper presents the outcome from a case study analysis for a Canadian farm that does site-specific fertilisation (SSF), a precision farming approach which takes into consideration the spatial variability of soils. The economic results for three years of wheat and canola production are compared to a neighbouring farm, which is practicing conventional broadcast application of fertilisers. Since no additional investments in machinery are needed, the annual variable cost is 6 CAD/acre. In the standard case, the average profit is 30 CAD/acre. The rather pronounced difference in the effects from SSF application in wheat vs. canola leads one to question whether this is a crop-related systematic outcome or instead represents something more random. Sensitivity analyses generated two main insights. First, the economics of SSF are sensitive to a modification in commodity prices -- a 50 % cut would reduce the average profit to about 9 CAD/acre. Second, another scenario calculation in which no-till is assumed to generate a 5% increase in yields suggests that the net profit would be just 7 CAD/acre. Given the existence of so many uncertainties, this paper calls for more farm-based economic analysis of SSF, one which should also include a comparison of different service providers for application maps. [ABSTRACT FROM AUTHOR]

In the last decades, geostatistics has been widely used for precision agriculture (PA) producing quite exciting results. Research on this topic is important for sustainable agriculture growth in Brazil. The objective of the review is an attempt to outline the current state of using geostatistical tools for PA applications in Brazil in the last 20 years (2002–2022), but not to provide an exhaustive review of models. We analyzed the scientific literature on this field in Brazil to identify their merits and weaknesses in the present, and to conjecture on future developments. We analyzed 151 proceeding papers and 144 peer-reviewed journal articles regarding applications of geostatistics in PA in Brazil from 2002 to 2022 using bibliometric techniques to reveal current research trends and hotspots. We detected using geostatistics for PA has been limited, mostly for univariate interpolation purposes. The co-citation analysis reveals four broad research clusters in the literature: (i) spatial variability, semivariogram, soil management, (ii) soil fertility, ordinary kriging, spatial dependence, (iii) coffee plant, coffee, Coffea arabica, and (iv) glycine max, zea mays, management zones. The presented review is a springboard to future modeling developments useful for geostatistics applications to PA in Brazil. We suggest expanding the use of geostatistics for smart agricultural technology by adding new potential approaches in new research. Combined with other approaches, such as machine learning, uncertainty modeling, efforts for more geostatistical training, and data fusion from multi-sensor and multi-source are a new frontier to be explored more often by the Brazilian PA community. [ABSTRACT FROM AUTHOR]

Weeds pose a persistent threat to farmers' yields, but conventional methods for controlling weed populations, like herbicide spraying, pose a risk to the surrounding ecosystems. Precision spraying aims to reduce harms to the surrounding environment by targeting only the weeds rather than spraying the entire field with herbicide. Such an approach requires weeds to first be detected. With the advent of convolutional neural networks, there has been significant research trialing such technologies on datasets of weeds and crops. However, the evaluation of the performance of these approaches has often been limited to the standard machine learning metrics. This paper aims to assess the feasibility of precision spraying via a comprehensive evaluation of weed detection and spraying accuracy using two separate datasets, different image resolutions, and several state-of-the-art object detection algorithms. A simplified model of precision spraying is proposed to compare the performance of different detection algorithms while varying the precision of the spray nozzles. The key performance indicators in precision spraying that this study focuses on are a high weed hit rate and a reduction in herbicide usage. This paper introduces two metrics, namely, weed coverage rate and area sprayed, to capture these aspects of the real-world performance of precision spraying and demonstrates their utility through experimental results. Using these metrics to calculate the spraying performance, it was found that 93% of weeds could be sprayed by spraying just 30% of the area using state-of-the-art vision methods to identify weeds. [ABSTRACT FROM AUTHOR]

Agriculture is confronting many challenges, from climate change to the considerable decreasing of the natural resources. In order to limit the impact of these challenges and ensure food sufficiency for the global growing population, precision agriculture is defined as one of sustainable solutions. This solution manages the whole agricultural cycle and it is based mainly on the use of information technologies. It ensures precision in the applied treatments, quantities and in time. By taking into account the nature of the agricultural field, this paper is interested in wireless technologies, more precisely, in IoT. In fact, many architectures and implementations have been proposed in this context, which can cause issues in scalability and interoperability. Thus, the principal aim of this paper is to define a conventional IoT architecture that can be used for precision agriculture. [ABSTRACT FROM AUTHOR]

We review and analyze the farming (upstream agribusiness supply chain) research literature since 1965 to identify farming research opportunities for operations management (OM) researchers. A majority of reviewed papers in our corpus, until the turn of the 21st century, primarily focus on improving operational efficiency and effectiveness of farming using optimization techniques. However, during the last two decades, farmers' welfare and the interests of other stakeholders have drawn OM researchers' attention. This expanded focus on farming research has become possible due to the proliferation of mobile communication devices and the Internet as well as advancements in information technology platforms and social media. Our review also shows that there is a paucity of OM literature that leverages increased data availability from the emergence of precision agriculture and blockchain to address major challenges for the farming sector emanating from climate change, natural disasters, food security, and sustainable and equitable agriculture, among others. Big data, in conjunction with opportunities for field‐based experimentation, artificial intelligence and machine learning, and integration of predictive and prescriptive analytics, can be leveraged by OM scholars engaged in farming research. We zero in on specific questions, issues, and opportunities for research in farming. [ABSTRACT FROM AUTHOR]