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This paper presents ideas for a new generation of agricultural system models that could meet the needs of a growing community of end-users exemplified by a set of Use Cases. We envision new data, models and knowledge products that could accelerate the innovation process that is needed to achieve the goal of achieving sustainable local, regional and...
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... addition to the farm-to-landscape scale analysis represented in Fig. 2, there will be a growing demand for agricultural systems models to simulate and integrate the different components of the agricultural value chain, to meet both policy requirements and corporate sustainability goals (Fig. 3). Genetics, agronomic management (produc- tion input), weather, soil, information technology and machinery will need to be linked in a system approach to address these informational needs. This is a new frontier for agricultural system modeling that would extend to the broader food system and raise additional data and analytical ...
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The purpose of this Special Issue of Agricultural Systems is to lay the foundation for the next generation of agricultural systems data, models and knowledge products. In the Introduction to this Special Issue, we described a vision for accelerating the rate of agricultural innovation and meeting the growing global need for food and fiber. In this...
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... Another key challenge is the spatial heterogeneity of soil nutrients across open field soil cultivation. Although advanced sensing technologies provide granular data, the spatial variability of nutrients, driven by differences in topography, soil type, and manage-ment practices, requires robust interpolation techniques to generate accurate nutrient maps [181]. In some cases, even the most sophisticated sensors may not fully capture the complex spatial distribution of nutrients. ...
Efficient management of soil nutrients is essential for optimizing crop production , ensuring sustainable agricultural practices, and addressing the challenges posed by population growth and environmental degradation. Smart agriculture, using advanced technologies, plays an important role in achieving these goals by enabling real-time monitoring and precision management of nutrients. In open-field soil cultivation, spatial variability in soil properties demands site-specific nutrient management and integration with variable-rate technology (VRT) to optimize fertilizer application, reduce nutrient losses, and enhance crop yields. Hydroponic solution cultivation, on the other hand, requires precise monitoring and control of nutrient solutions to maintain optimal conditions for plant growth, ensuring efficient use of water and fertilizers. This review aims to explore recent trends in soil and solution nutrient sensing technologies for open-field soil and facilitated hydroponic cultivation, highlighting advancements that promote efficiency and sustainability. Key technologies include electrochemical and optical sensors, Internet of Things (IoT)-enabled monitoring, and the integration of machine learning (ML) and artificial intelligence (AI) for predictive modeling. Blockchain technology is also emerging as a tool to enhance transparency and traceability in nutrient management, promoting compliance with environmental standards and sustainable practices. In open-field soil cultivation, real-time sensing technologies support targeted nutrient application by accounting for spatial variability , minimizing environmental risks such as runoff and eutrophication. In hydroponic solution cultivation, precise solution sensing ensures nutrient balance, optimizing plant health and productivity. By advancing these technologies, smart agriculture can achieve sustainable crop production, improved resource efficiency, and environmental protection, fostering a resilient food system.
... Combining agronomic knowledge with RS and ML ensures these tools meet scientific and agricultural needs. • Scalable models: Modular and adaptable frameworks are needed to support adoption across various systems, from large-scale farms to smallholder operations [8], ensuring that technological benefits are accessible regardless of scale or available resources. • Climate resilience: RS and ML play a crucial role in creating strategies to mitigate climate risks by monitoring vegetation stress and predicting the impact of weather on yields [9], promoting sustainable resource use and resilience. ...
Remote sensing (RS) and machine learning (ML) are driving significant changes in agriculture [...]
... Because crop growth can vary depending on the physiological properties of individual crops, predicting crop growth should consider physiological and genomic responses in conjunction with the developmental stage and cultivation conditions [45,46]. In this study, it was assumed that today's crop growth can be represented with yesterday's environment and crop growth. ...
Crop growth information is collected through destructive investigation, which inevitably causes discontinuity of the target. Real-time monitoring and estimation of the same target crops can lead to dynamic feedback control, considering immediate crop growth. Images are high-dimensional data containing crop growth and developmental stages and image collection is non-destructive. We propose a non-destructive growth prediction method that uses low-cost RGB images and computer vision. In this study, two methodologies were selected and verified: an image-to-growth model with crop images and a growth simulation model with estimated crop growth. The best models for each case were the vision transformer (ViT) and one-dimensional convolutional neural network (1D ConvNet). For shoot fresh weight, shoot dry weight, and leaf area of lettuce, ViT showed R2 values of 0.89, 0.93, and 0.78, respectively, whereas 1D ConvNet showed 0.96, 0.94, and 0.95, respectively. These accuracies indicated that RGB images and deep neural networks can non-destructively interpret the interaction between crops and the environment. Ultimately, growers can enhance resource use efficiency by adapting real-time monitoring and prediction to feedback environmental controls to yield high-quality crops.
... Bifactorial ANOVA that estimated the genotype, environment and genotype by environment interaction (GEI) was used as a control technique for assessing the suitability of two different three-way data analysis methods. On the one hand, following [12], the Multiple Factor Analysis (MFA) technique [13] allowed us to explore, using descriptive measures and graphs, the possible interactions between the varieties, the variables and the environment. On the other hand, Multiple Dual Factor Analysis (DMFA) [14] was applied as a methodological proposal in order to evaluate its suitability to study the agroecological aptitude of a certain environment in relation to the introduction of a crop that presents known diversity, at least in other productive environments. ...
Banana (Musa spp.) is an important crop in the economies of many developing countries. In the north of Argentina, a subtropical region, banana plants grow in a suboptimal environment that limits yield because only one harvest per year is achieved. The objective of this work was to characterize the agroecological aptitude of Formosa, Argentina, for banana production through the behavior of three varieties of international use: Williams, Jaffa and Grand Naine, evaluated over five consecutive years. The three-way data analysis technique called Multiple Factor Analysis (MFA) was used for evaluating the varieties’ performances across cycles of production. The results allowed for inferring the existence of a genotype x environment interaction (GEI), corroborated by two-way factorial ANOVA. In order to determine how this suboptimal environment affected the development of each genotype of this perennial crop, Dual Multiple Factor Analysis (DMFA) was applied to jointly analyze the correlation structure between the traits that contributed to the performance of each variety in each year. The correlation structures between variables were different in each population and varied between years. All traits showed great variation between the years and genotypes, with the fruit peel thickness being the most discrepant throughout the years. However, Formosa appeared as a promising subtropical agroecological environment to produce banana because the varieties’ performances were acceptable for large-scale production systems. In addition to evaluating the adequate aptitude for cultivating banana in Formosa considering the significant effect of the GEIs, this research made a methodological contribution by proposing the use of three-way data analysis in Agronomy Science via MFA and DMFA.
... At present, there are serious problems in the packaging design of agricultural products, such as the lack of brand image shaping, regional cultural connotations, and ecological design concepts [3][4]. Therefore, the discovery of the current problems in the packaging design of characteristic agricultural products in rural areas put forward the innovative development strategy of the packaging design of characteristic agricultural products to meet the people's demand for packaging, which can bring the double benefits of the economy and the market [5][6], to drive the economic development of rural areas, and effectively promote the further consolidation and implementation of the strategy for the revitalization of the countryside [7]. ...
Utilizing Internet technology and resources to enhance the packaging design of agricultural products is a critical task for promoting their development in the current era. This study proposes an automatic generation design for agricultural product packaging based on deep learning theory, grounded in the principles of packaging design and market value. To represent the spatial relationship and distribution of color pixels, color moments and color correlation maps are used after obtaining color features of agricultural products through the HSV color space. The grayscale covariance matrix method is employed to get the texture features necessary for the packaging design. Proportion, boundary, pairing, white space, and balance calculations are combined to achieve the layout and typography of feature elements in the agricultural packaging design. The impact of deep learning technology-supported packaging design is analyzed. Data show that in the first group, the average values of the sample group (P001, P002, P004) are 2.98, 3.08, and 2.93, respectively, with an overall average of 2.997, while the overall average of the intelligent group (P003) is 2.96. Overall, deep learning technology-assisted image generation has a positive impact on agricultural product packaging design, contributing to a new level of economic development of farming products.
... Product design, encompassing aspects like drawing, modelling, material selection, and market research, is integral to contemporary design (Chandrasegaran, et al., 2013). Its application across finance, agriculture, and chemistry necessitates diverse design methodologies (Antle et al., 2017). To navigate these challenges, suitable tools and techniques are required (Lutters et al., 2014). ...
... Designers should devise checklists for new sustainable product development and collaborate with production personnel to ensure product performance and quality (Chen et al., 2012). To optimize sustainability throughout the lifecycle, companies must recognize that sustainability is not solely about technological advancement, but also about its impact (Antle et al., 2017;Iqbal, Ahmad, and Li., 2021b). Comparatively, sustainable product design prioritizes environmental, resource, and usage considerations over traditional design. ...
... Sustainable product design encompasses diverse fields like finance, agriculture, and chemistry, aiming to ameliorate their inherent drawbacks (Iqbal and Piwowar-Sulej., 2021). Various models can be employed to assist in resolving design issues, particularly those in chemistry, where they form a decision framework for sustainability and utilize computer-aided design tools and information and communication technology models (Antle et al., 2017;Heintz et al., 2014). Literature on sustainability research provides comprehensive insights into the application of sustainable models in product design research, enhancing design precision and efficacy at every stage (Ma and Kremer, 2016). ...
The integration of product design across diverse sectors has fueled technological innovation and progression throughout various industries. Recognizing this trend, academia has revisited its approach to product design education and research. This paper presents a bibliometric analysis of product design literature published between 2011 and 2020, utilizing data from the Scopus database. The analysis identifies key terms, countries, sources, and research clusters, providing insights into prevailing trends within the field. The paper further discusses these findings through an examination of publication patterns. The bibliometric analysis uncovers annual publication volumes, contributing nations, source expansion rates, predominant keywords, and collaborative efforts. The research topics are categorized into three primary domains: product design models, sustainable design, and design methodologies/evaluation strategies. The qualitative analysis segment provides a comprehensive review of the most frequently referenced articles.
... Agricultural production is a data-rich environment that requires advanced analysis for informed decision-making processes [30,31]. Advanced training can be accomplished through outreach and extension services with fit-for-purpose tools that reduce barriers of application by end users. ...
Modern dairy farm management requires meaningful data and careful analysis to maximize profitability, cow health, and welfare. Current data platforms, such as DairyComp, lack robust integrated data analysis tools. Producers and consultants need dedicated tools to turn collected data sets into assets for informed decision-making processes. The DairyCoPilot app allows users to rapidly extract health and production data from DairyComp, then compile and analyze the data using a menu-driven point-and-click approach. Prospects for training consultants in applied data analysis skills make DairyCoPilot a tool to identify farm management bottlenecks with less time spent for data analysis, improving cow health, and dairy production. The DairyCoPilot Dashboard R Shiny application is published using RStudio Connect: https://connect.doit.wisc.edu/dairy-copilot/.
... Food security crises, sustainability concerns, technology and computer advances, open information and data accessibility, interdisciplinary and transboundary science, and user-adapted models summarise a new context that must lead to a new generation model focused on management for sustainability and productivity [113]. ...
In 2030, the world population will exceed 8.5 billion, increasing the challenges to satisfy basic needs for food, shelter, water, and/or energy. Irrigation plays a vital role in productive and sustainable agriculture. In the current context, it is determined not only by water availability but also by optimal management. Several authors have attempted to measure the performance of irrigation networks through various approaches in terms of technical indicators. To improve the sustainability in the pipe sizing of the pressurised irrigation networks, 25 different models were evaluated to discuss the advantages and disadvantages to consider in future methodologies to size water systems, which guarantee the network operation but contribute to improving the sustainability. They enable water managers to use them as tools to reduce a complex evaluation of the performance of a system, and focusing on better management of resources and sustainability indicators for agricultural ecosystems are clear and objective values.
... The former directly establishes the relationship between environmental inputs and yield outputs without concerning the inherent growth processes of the crops [7,8]. The latter, on the other hand, focuses on describing the essential physiological and physical processes that influence yield formation [9][10][11]. ...
It is crucial to assess the impact of climate change on crop productivity and sustainability for the development of effective adaptation measures. Crop models are essential for quantifying this impact on crop yields. To better express crops’ intrinsic growth and development patterns and their plasticity under different environmental conditions, the functional–structural plant model (FSPM) “GreenLab” has been developed. GreenLab is an organ-level model that can describe the intrinsic growth and development patterns of plants based on mathematical expressions without considering the influence of environmental factors, and then simulate the growth and development of plants in expressing plant plasticity under different environmental conditions. Moreover, the distinctive feature of GreenLab lies in its ability to compute model source–sink parameters affecting biomass production and allocation based on measured plant data. Over the past two decades, the GreenLab model has undergone continuous development, incorporating novel modeling methods and techniques, including the dual-scale automaton, substructure methods, the inverse of source–sink parameters, crown analysis, organic series, potential structure, and parameter optimization techniques. This paper reviews the development history, the basic concepts, main theories, characteristics, and applications of the GreenLab model. Additionally, we introduce the software tools that implement the GreenLab model. Last, we discuss the perspectives and directions for the GreenLab model’s future development.
... Presumably, this intensification leads to a transition into terminal drought, completely eliminating soybean AGB and, consequently, yield. Given that most crop models have not incorporated biotic stressors (Antle et al., 2017), they consequently fail to capture AGB and yield losses due to the contemporaneous presence of weeds. MONICA uses a field-condition modifier parameter to consider any yield-reducing factors that are not explicitly represented through biophysical processes in the model. ...
Moving from sole cropping to intercropping is a transformative change in agriculture, contributing to several ecosystem services. However, modelling intercropping is challenging due to intensive parameterisation, complex calibration, and experiment scarcity. To facilitate future understanding, design and adaptation of intercropping, it is therefore necessary to develop simple modelling routines capable of simulating essential features. In this paper, we integrated a light competition module requiring four parameters into MONICA, a generic agroecosystem model, with the goal of simulating a wheat–soybean relay-row intercropping system. We tested three calibration approaches using data from two years of field experiments located in Müncheberg, Germany: sole cropping-based calibration, intercropping-based calibration and a default calibration method that incorporates both systems. Under both irrigated and rainfed conditions, MONICA successfully reproduced the aboveground biomass and yield of sole crops from field experiments, with RMSEA ranging from 0.64 t ha−1 to 2.74 t ha−1 and RMSEY ranging from 0.003 t ha−1 to 0.47 t ha−1. By taking light competition into account, the modified MONICA was able to simulate interactive performance in relay-row intercropping. Generally, MONICA overestimated the aboveground biomass and yield across the three calibration strategies, and simulations for wheat were more accurate than those for soybean. However, a comparison among the calibration strategies revealed that the intercropping-based strategy outperformed the others. It significantly improved the model efficiency for soybean yield in intercropping, increasing the Index of Agreement from 0.27 to 0.73, and it decreased the Mean Bias Error for yield by up to 76%. Our results demonstrate the feasibility of using a model that is simple in both calibration and inputs, yet detailed enough to simulate the complex aboveground light competition of intercropping. Additionally, they underscore the significance of cropping system specific calibration, highlighting the importance of calibrating crop performance specifically for intercropping in order to capture genotype-by-environment interactions.
