Digital Agriculture Research Articles

Ecological Cognition, Digital Agricultural Technology Adoption and the Sustainable Development of Family Grain Farms – An Empirical Study from China

Ecological Cognition, Digital Agricultural Technology Adoption and the Sustainable Development of Family Grain Farms – An Empirical Study from China

Digital transformation metamodel in smart farming: Crop classification prediction based on recurrent neural network

Agriculture 4.0 is an opportunity for farmers to meet the current challenges in food production. It has become necessary to adopt a set of agricultural practices based on advanced technologies. Agriculture 4.0 enables farms to create added value by combining innovative technologies, such as precision agriculture, information and communication technology, robotics, and Big Data. As an enterprise, a connected farm is highly sensitive to strategic changes in organizational structures, objectives, modified variety, new business objects, processes, etc. To control the farm’s information system strategically, we proposed a metamodel based on the ISO/IS 19440 standard, where we added some new constructs relating to advanced digital technologies for smart and connected agriculture. We applied the proposed metamodel to the crop classification prediction process. This involved using machine learning methods such as recurrent neural networks to predict the type of crop being grown in a given agricultural area. Our research bridges farming with modern technology through our metamodel for a connected farm, promoting sustainability and efficiency. Furthermore, our crop classification study demonstrates the power of advanced machine learning, guided by our metamodel, in accurately predicting crop conditions, emphasizing its potential for crop management and food security. In essence, our work advances the transformative role of digital agriculture in modern farming.

Effects of digital agriculture solutions on smallscale wage workers and employment

Effects of digital agriculture solutions on smallscale wage workers and employment

Enhancing public health through modern agronomy: Sustainable and nutrient-rich practices

Introduction: This commentary explores modern agronomy concepts aimed at enhancing public health through sustainable and nutrient-rich agricultural practices. We explore various innovative approaches, includ­ing precision agriculture, sustainable farming, efficient nutrient management, crop diversity, biofortification, climate-resilient farming, vertical farming and urban agriculture, digital agriculture, and agroecology (see Figure 1). Our commentary delves into each of these modern agronomy prac­tices, unveiling their intricate web and profound implications for public health. We aim to demon­strate the potential of modern agronomy as a catalyst for improving public health and the quality of life for individuals and communities worldwide. These concepts collectively strive to elevate public health by improving food quality, enhancing nutrition, and safeguarding the well-being of individuals and communities. By harnessing these cutting-edge agronomic concepts, we aspire to transform agriculture into a powerful force for improving public health and enhancing overall quality of life. . . .

Who and what gets recognized in digital agriculture: agriculture 4.0 at the intersectionality of (Dis)Ableism, labor, and recognition justice

Who and what gets recognized in digital agriculture: agriculture 4.0 at the intersectionality of (Dis)Ableism, labor, and recognition justice

Intelligent Integrated System for Fruit Detection Using Multi-UAV Imaging and Deep Learning.

In the context of Industry 4.0, one of the most significant challenges is enhancing efficiency in sectors like agriculture by using intelligent sensors and advanced computing. Specifically, the task of fruit detection and counting in orchards represents a complex issue that is crucial for efficient orchard management and harvest preparation. Traditional techniques often fail to provide the timely and precise data necessary for these tasks. With the agricultural sector increasingly relying on technological advancements, the integration of innovative solutions is essential. This study presents a novel approach that combines artificial intelligence (AI), deep learning (DL), and unmanned aerial vehicles (UAVs). The proposed approach demonstrates superior real-time capabilities in fruit detection and counting, utilizing a combination of AI techniques and multi-UAV systems. The core innovation of this approach is its ability to simultaneously capture and synchronize video frames from multiple UAV cameras, converting them into a cohesive data structure and, ultimately, a continuous image. This integration is further enhanced by image quality optimization techniques, ensuring the high-resolution and accurate detection of targeted objects during UAV operations. Its effectiveness is proven by experiments, achieving a high mean average precision rate of 86.8% in fruit detection and counting, which surpasses existing technologies. Additionally, it maintains low average error rates, with a false positive rate at 14.7% and a false negative rate at 18.3%, even under challenging weather conditions like cloudiness. Overall, the practical implications of this multi-UAV imaging and DL-based approach are vast, particularly for real-time fruit recognition in orchards, marking a significant stride forward in the realm of digital agriculture that aligns with the objectives of Industry 4.0.

Leveraging digital agriculture for on-farm testing of technologies

The Precision Nitrogen Project (PNP) worked with more than 80 corn and winter wheat producers to inexpensively design and implement randomized, replicated field strip trials on whole commercial farm fields, and to provide site-specific testing of current nitrogen (N) technologies. This article proposes a conceptual framework and detailed procedure to select the N technology to be tested; design and implement field trials; generate, process, and manage field trial data; and automatically analyze, report, and share benefits from precision N technology. The selection of the N technology was farmer-driven to ensure a good fit and to increase the likelihood of future technology adoption. The technology selection method was called the “N tiered approach”, which consisted of selecting a technology that progressively increases the level of complexity without exceeding the farmer’s learning process or farm logistic constraints. The N tools were classified into (1) crop model-based, (2) remote sensing-based, (3) enhanced efficiency fertilizers, and (4) biologicals. Field strip trials comparing producers’ traditional management and the selected N technology were combined with site-specific N rate blocks placed in contrasting areas of the fields. Yield data from the N rate blocks was utilized to derive the site-specific optimal N rate. The benefits of current N technologies were quantified by comparing their yield, profit, and N use efficiency (NUE) to growers’ traditional management and to the estimated site-specific optimal N rate. Communication of the trial results back to the growers was crucial to ensure the promotion and adoption of these N technologies farm wide. The framework and overall benefits from N technologies was presented and discussed. The proposed framework allowed researchers, agronomists, and farmers to carry out on-farm precision N experimentation using novel technologies to quantify benefits of digital ag technology and promote adoption.

Impacts of Crop Type and Climate Changes on Agricultural Water Dynamics in Northeast China from 2000 to 2020

Northeast China (NEC) is one of the most important national agricultural production bases, and its agricultural water dynamics are essential for food security and sustainable agricultural development. However, the dynamics of long-term annual crop-specific agricultural water and its crop type and climate impacts remain largely unknown, compromising water-saving practices and water-efficiency agricultural management in this vital area. Thus, this study used multi-source data of the crop type, climate factors, and the digital elevation model (DEM), and multiple digital agriculture technologies of remote sensing (RS), the geographic information system (GIS), the Soil Conservation Service of the United States Department of Agriculture (USDA-SCS) model, the Food and Agriculture Organization of the United Nations Penman–Monteith (FAO P-M) model, and the water supply–demand index (M) to map the annual spatiotemporal distribution of effective precipitation (Pe), crop water requirement (ETc), irrigation water requirement (IWR), and the supply–demand situation in the NEC from 2000 to 2020. The study further analyzed the impacts of the crop type and climate changes on agricultural water dynamics and revealed the reasons and policy implications for their spatiotemporal heterogeneity. The results indicated that the annual average Pe, ETc, IWR, and M increased by 1.56%/a, 0.74%/a, 0.42%/a, and 0.83%/a in the NEC, respectively. Crop-specifically, the annual average Pe increased by 1.15%/a, 2.04%/a, and 2.09%/a, ETc decreased by 0.46%/a, 0.79%/a, and 0.89%/a, IWR decreased by 1.03%/a, 1.32%/a, and 3.42%/a, and M increased by 1.48%/a, 2.67%/a, and 2.87%/a for maize, rice, and soybean, respectively. Although the ETc and IWR for all crops decreased, regional averages still increased due to the expansion of water-intensive maize and rice. The crop type and climate changes jointly influenced agricultural water dynamics. Crop type transfer contributed 39.28% and 41.25% of the total IWR increase, and the remaining 60.72% and 58.75% were caused by cropland expansion in the NEC from 2000 to 2010 and 2010 to 2020, respectively. ETc and IWR increased with increasing temperature and solar radiation, and increasing precipitation led to decreasing IWR in the NEC. The adjustment of crop planting structure and the implementation of water-saving practices need to comprehensively consider the spatiotemporally heterogeneous impacts of crop and climate changes on agricultural water dynamics. The findings of this study can aid RS-GIS-based agricultural water simulations and applications and support the scientific basis for agricultural water management and sustainable agricultural development.

Big Data Review of the Influence of Agricultural Sector Development on Economic Resilience

The agricultural sector in the East Java's GDP structure has been stable at around 11% over the past 5 years, despite disruptions caused by the Covid pandemic. The stable development of the agricultural sector has contributed to the region's increased resilience. This study aims to identify the relationship between factors influencing agricultural development and resilience in East Java and to formulate related development strategies. The method used in this study is Correlation and Regression. Regional resilience is viewed as the output of changes in the rate of GDP growth. Factors in agricultural development are viewed from Agricultural Production, Land Factors using Big data, including LST, NDVI, and NDWI, Internet User Farmers and Farmer Populations. The results of the study indicate that significant influential factors in agricultural development are found in NDVI, NDWI, and Farmer Population. The study shows that for East Java, development strategies through digital farming have not yet been able to increase regional resilience, and conventional agricultural development methods still dominate.

Plant disease recognition in a low data scenario using few-shot learning

Plant disease is one of the major problems in agriculture. Diseases damage plants, reduce yields and lower the quality of the produce. Traditional approaches to detecting plant diseases are usually based on visual inspection and laboratory testing, which can be expensive and time-consuming. They require trained plant pathologists as well as specialised equipment. Several studies demonstrate that artificial intelligence (AI) methods can produce promising results. However, AI methods are generally data-hungry and require large annotated datasets, and the collection and annotation of such datasets can be a limiting factor. It often appears that only a small amount of data is available for certain disease types. Whereas the performance of typical AI methods drops significantly when they are trained with inadequate data. This paper proposes a novel few-shot learning (FSL) method to detect plant diseases and alleviate the data scarcity problem. The proposed method uses as few as five images per class in the machine learning process. Our method is based on a state-of-the-art FSL pipeline called pre-training, meta-learning, and fine-tuning (PMF), integrated with a novel feature attention (FA) module; we call the overall method PMF+FA. The FA module emphasises the discriminative parts in the image and reduces the impact of complicated backgrounds and undesired objects. We used ResNet50 and Vision Transformers (ViT) as the feature learner. Two publicly available plant disease datasets were repurposed to meet the FSL requirements. We thoroughly evaluated the proposed method on the PlantDoc dataset, which contains disease samples in field environments with complex backgrounds and unwanted objects. The PMF+FA method with ViT achieved an average accuracy of 90.12% in disease recognition. The results demonstrate that the PMF+FA pipeline consistently outperforms the baseline PMF. The results also highlight that the method using ViT generates better results than ResNet50 for diagnosing complex data. ViT and ResNet50 implementations are computationally efficient, taking 1.11 and 0.57 ms on average per image to evaluate the test set respectively. The high throughput and high-quality performance with only a small training dataset indicate that the proposed technique can be used for real-time disease detection in digital farming systems.

ASSESING ROMANIAN FARMERS’ MOTIVATION FOR DIGITALIZATION: A UNIFIED THEORY OF ACCEPTANCE AND USAGE OF TECHNOLOGY (UTAUT) BASED RESEARCH MODEL

The digital agriculture adoption strive by farmers could be assimilated with the adoption efforts of the computer technologies (ICT) by their respective users in other professional fields. Therefore, this paper proposes the adaptation of the Unified Theory of Acceptance and Usage of Technology (UTAUT) model to explore the motivations for the use of digital tools by field crop farmers in Romania. The history of TAM and UTAUT is explored as well as the usage of UTAUT2 and UTAUT3 in digital agriculture adoption studies globally. Furthermore, comparison is also made with other farmer studies based on a different research architecture conceived also to explore their motivations to use digital agriculture tools. The proposed model is building on the decades long research and validation of the UTAUT model, and it will be employed in subsequent quantitative studies to evaluate the Romanian farmers’ motivations to embrace the digital agriculture technologies and practices. The paper is the outcome of the individual research done by the author and it was enhanced following academic debates and conference presentations. It is deemed to have a dual applicability, as a field tool for academic research but also provides guidance for companies developing digital tools for farmers in Romania and beyond.

Exploring the impact of agricultural digitalization on smallholder farmers' livelihoods in Ghana

With the government's pursuit of a digitalization agenda, Ghana is at the forefront of championing digital transformation in Africa. However, people in rural areas are being left behind in harnessing the immense benefits of digitalization for their livelihoods. This study contributes to policy efforts aimed at bridging that gap by investigating the drivers of agricultural digitalization (AD) as well as its effects on the livelihoods of smallholder farmers in rural Ghana. Data from a cross-sectional survey of 525 rural farmers across northern, middle and southern Ghana was employed. The study used the probit and tobit estimators to analyze the drivers and intensity of adoption of digital technologies in agriculture and the inverse probability weighting and regression adjustment estimator to mitigate endogeneity concerns. The results show that while female farmers trail male farmers in the intensity of applying digital technologies, higher educational attainment, better perception of digitalization, group/cooperative membership, number of economically active household members, and access to reliable electricity, internet and mobile money services significantly promote the use of digital technologies in agricultural activities. The results further show that AD is significantly associated with perceived improvements in livelihood assets, and ultimately livelihood outcomes of smallholder farmers in rural Ghana. These findings highlight the importance of investing in rural digital infrastructure by policymakers, the private sector and other stakeholders, so as to expand access to and the uptake of digital technologies in agriculture to bolster rural development in Ghana.

Creating intelligent cyberinfrastructure for democratizing AI

AbstractArtificial intelligence (AI) has the potential for vast societal and economic gain; yet applications are developed in a largely ad hoc manner, lacking coherent, standardized, modular, and reusable infrastructures. The NSF‐funded Intelligent CyberInfrastructure with Computational Learning in the Environment AI Institute (“ICICLE”) aims to fundamentally advance edge‐to‐center, AI‐as‐a‐Service, achieved through intelligent cyberinfrastructure (CI) that spans the edge‐cloud‐HPC computing continuum, plug‐and‐play next‐generation AI and intelligent CI services, and a commitment to design for broad accessibility and widespread benefit. This design is foundational to the institute's commitment to democratizing AI. The institute's CI activities are informed by three high‐impact domains: animal ecology, digital agriculture, and smart foodsheds. The institute's workforce development and broadening participation in computing efforts reinforce the institute's commitment to democratizing AI. ICICLE seeks to serve as the national nexus for AI and intelligent CI, and welcomes engagement across its wide set of programs.

Research on Market Access Issues for Agricultural Management Entities of Different Scales in Heilongjiang Province in the Development of Digital Agriculture

Research on Market Access Issues for Agricultural Management Entities of Different Scales in Heilongjiang Province in the Development of Digital Agriculture

Extraction of Lilium davidii var. unicolor Planting Information Based on Deep Learning and Multi-Source Data.

Accurate extraction of crop acreage is an important element of digital agriculture. This study uses Sentinel-2A, Sentinel-1, and DEM as data sources to construct a multidimensional feature dataset encompassing spectral features, vegetation index, texture features, terrain features, and radar features. The Relief-F algorithm is applied for feature selection to identify the optimal feature dataset. And the combination of deep learning and the random forest (RF) classification method is utilized to identify lilies in Qilihe District and Yuzhong County of Lanzhou City, obtain their planting structure, and analyze their spatial distribution characteristics in Gansu Province. The findings indicate that terrain features significantly contribute to ground object classification, with the highest classification accuracy when the number of features in the feature dataset is 36. The precision of the deep learning classification method exceeds that of RF, with an overall classification accuracy and kappa coefficient of 95.9% and 0.934, respectively. The Lanzhou lily planting area is 137.24 km2, and it primarily presents a concentrated and contiguous distribution feature. The study's findings can serve as a solid scientific foundation for Lanzhou City's lily planting structure adjustment and optimization and a basis of data for local lily yield forecasting, development, and application.

Achieving the Rewards of Smart Agriculture

From connected sensors in soils, on animals or crops, and on drones, to various software and services that are available, “smart” technologies are changing the way farming is carried out. These technologies allow producers to look beyond what the eye can see by collecting non-traditional data and then using analytics tools to improve both food sustainability and profitability. “Smart Agriculture/farming” (SA) or “Digital Agriculture” (DA), often used interchangeably, refer to precision agriculture that is thus connected in a network of sensing and acting. It is a concept that employs modern information technologies, precision climate information, and crop/livestock developmental information to connect production variables to increase the quantity and quality of agricultural and food products. This is achieved by measuring and analyzing variables accurately, feeding the information into the cloud from edge devices, extracting trends from the various data, and subsequently providing information back to the producer in a timely manner. Smart agriculture covers many disciplines, including biology, mechanical engineering, automation, machine learning, artificial intelligence, and information technology-digital platforms. Minimum standards have been proposed for stakeholders with the aim to move toward this highly anticipated and ever-changing revolution. These foundational standards encompass the following general categories, including precise articulation of objectives, and baseline standards for the Internet of Things (IoT), including network infrastructure (e.g., stable 4G or 5G networks or a wireless local area network (WLAN) are available to end users). To sum up, SA aims to improve production efficiency, enhance the quality and quantity of agricultural products, reduce costs, and improve the environmental footprint of the industry. SA’s ecosystem should be industry self-governed and collaboratively financed. SA stakeholders and end-users’ facilities should meet standard equipment requirements, such as sensor accuracy, end data collectors, relevant industry compliant software, and trusted data analytics. The SA user is willing to be part of the SA ecosystem. This short perspective aims to summarize digital/smart agriculture concept in plain language.

Comprehensive Survey on Applications of Internet of Things, Machine Learning and Artificial Intelligence in Precision Agriculture

A comprehensive, multidisciplinary analysis of the latest developments in digital agriculture is conducted with the use of artificial intelligence (AI), machine learning (ML), and the Internet of Things. By automation and the use of modern, scalable technology solutions that reduce risks, support sustainability, and give farmers predictive advice, traditional agricultural processes are being updated and improved to maximize production. In this paper, the applications of AI, IoT, and ML in agricultural production systems are discussed in detail. The applications that have been explored can be broadly categorized into three areas: soil management, livestock management, and crop management. Weed detection, disease identification, and yield forecasting are some of the applications for crop management. Two applications of livestock management are animal welfare and production. The use of AI, IoT, and ML will make it possible to collect data from agricultural activities for analysis and the extraction of insightful knowledge, facilitating prompt and accurate decision making to increase agricultural productivity. This will result in farming that is more exact and efficient while requiring less labour and producing high-quality produce.

AIFARMS: Artificial intelligence for future agricultural resilience, management, and sustainability

AbstractThe AIFARMS Artificial Intelligence for Future Agricultural Resilience, Management, and Sustainability national AI institute brings together over 40 world‐class AI and agriculture researchers, with the common mission to develop foundational advances in AI and use them to ensure that future agriculture is environmentally friendly, sustainable, affordable, and accessible to diverse farming communities. Since its establishment in 2020, AIFARMS has advanced the state of the art in autonomous farming, cover crop planting, machine learning for improved outcomes from remote sensing, dynamic estimation of yield loss from weeds, and livestock management. The institute has prioritized the creation and utilization of high‐quality, openly available data sets for advancing foundational AI and tackling agricultural challenges. AIFARMS leverages a close partnership between UIUC and Tuskegee University to build programming for a skilled and diverse next‐generation workforce in digital agriculture. We are expanding the reach of AIFARMS outside of the current partners to collaborate with national AI institutions and international partners.

Digital Technology: A Game Changer in Vegetable Cultivation

Vegetables are important constituents of Indian agriculture and nutritional security due to their short duration, high yielding capacity, nutritional richness, economic viability and ability to generate on farm and off farm employment. Increasing per capita income, health consciousness, urbanisation, increasing working women, shifting of farmers to high value vegetables are also important ingredients for fuelling vegetable growth in India. But we are still not getting the required /capita vegetables/day.so the answer to increasing the production of various vegetables employing various digital technologies is required. Growing high yielding varieties/hybrids and applying new production methods can significantly increase yield in most vegetables. The application of digital technologies to integrate agricultural output from the paddock to the customer is known as digital agriculture. These technologies can give the agricultural business the tools and data it needs to make better decisions and increase productivity). Information and communication technology (ICTs) and Artificial intelligence (AI) are the two key technologies used in digital agriculture. Information and communication technology (ICT) can be defined as a collection of technologies that aid or support the storage, processing, or distribution of data/information.Despite a big, well-educated, well-trained, and well-organized agricultural extension workforce, over 60% of farmers in the country remain unreached, with no extension organisation serving them. Radio and television are the primary sources of information. e- Extension, Agrisnet, Digital green, e- Sagu, Agmarknet, iKisan, Village Knowledge Centers, Kisan Call Centers, SMS Portal/mKisan portal are some of the e-Agriculture efforts in India. Furthermore, information and communication technology (ICT) aid in the timely completion of decisions, as well as the discovery of the best solutions and efficient systems for water management and irrigation in order to maximise production Artificial intelligence is assisting several agricultural sectors in increasing output and efficiency. In every industry, AI technologies are help in overcoming traditional hurdles. AI intervention in agriculture is assisting farmers in increasing their farming efficiency while reducing harmful environmental impacts. The agriculture business has embraced AI with open arms in order to improve overall results.AI is changing the way food is produced, resulting in a 20% reduction in agriculture sector emissions. Incorporating AI technology into agriculture, aids in the control and management of any unwelcome natural occurrence. It consists of robotic process automation, a machine learning platform, and sensors, among other things. Harvesting, weed management, pest and disease monitoring, irrigation control, and predictive agricultural analytics are all possible with these. Artificial intelligence and information and communication technologies (ICT) have the potential to be a technological revolution and a boom in agriculture, helping to feed the world's growing population.

Digital agriculture for sustainable development in China: The promise of computerization

The pivotal role of computer technology in augmenting crop productivity, optimizing supply chain management in food production, and reducing wastage through precision agriculture techniques is undeniable. The utilization of computers has significantly transformed the field of food production, enhancing effectiveness, environmental sustainability, and economic viability. This study examines the asymmetric and long-term effects of computer usage on grain output in the selected Chinese provinces. Additionally, it investigates the impact of other variables, such as energy use and the application of pesticides and fertilizers on grain yields. To analyze the dataset spanning from 2002 to 2020, the researchers employed the FGLS, D-K, and Dumitrescu-Hurlin causality methods. The study's findings underscore the substantial positive impact of computer utilization on grain production. Besides, fertilizer application and adoption of irrigation practices positively influence grain yields. Conversely, the utilization of pesticides, power consumption, and rural labor engagement exhibit a negative impact on grain output. Therefore, evaluating the potential of investing in sophisticated technologies, considering multiple elements including local conditions, the availability of financial resources, and the role of agricultural industrialization in regional development is imperative. We discuss the policy implications of these findings in detail in the Conclusion section.