Digital Agriculture Research Articles

Utility of AI Based Soil Testing in Agriculture

ABSTRACT The soil plays crucial role in any kind of agricultural crops. Depending upon the different ingredients of the soil the nature of crops to be farmed is decided.Optimizing agricultural practices is crucial for countries like India to address economic growth and escalating food demands. Historical challenges stemming from diverse weather conditions, geographical variations, conventional farming methods, and economic uncertainties have hindered improved crop yields. The dearth of information on crop yield productivity has also resulted in significant economic losses. In response, the adoption of advanced agricultural technologies such as smart farming, digital agriculture, and Data Analytics has become imperative. These innovations provide valuable insights into factors influencing crop yields, best-fit crops for a certain area, and enabling accurate predictions. With precise crop yield forecasts, farmers can develop tailored cultivation plans, implement efficient crop health monitoring systems, and manage yields effectively, thereby transforming agriculture into a highly profitable venture. This paper delves into the applications of technological advancements in agriculture, focusing on areas such as Digital Agriculture, Crop Management, and Crop Protection, and Data Analytics, with the aim of contributing to the optimization of agricultural practices and bolstering the economic impact of the agricultural sector. KEYWORDS: Agriculture, Artificial Intelligence, Machine learning, crop prediction

Fuzzy logic-based control for robot-guided strawberry harvesting: visual servoing and image segmentation approach

The concept of digital farming can help farmers increase their agricultural production yield. One of the technologies to support digital farming is robotics, which can be utilized to complete a redundant task efficiently for 24 hours. This paper presents a simple and effective harvesting robot that is applied to harvest a ripe strawberry. The mechanical and electrical design is kept simple to ensure it is reproducible. The input from a proximity sensor and image detection by a Pi camera is utilized by FLC (Fuzzy Logic Controller) to improve the effectiveness of the harvesting task. The image processing method in this study is image segmentation, which fits with the limited source of the microcontroller available in the market. The experiment included 60 times (20 times center, left, and right position) harvesting using the FLC algorithm and 60 times without FLC to show the effectiveness of the proposed method. From 60 experiments without an FLC experiment, there is an 80% hit rate for strawberries positioned in the middle of an image plane and 55% for left and right strawberries. From 60 times of FLC experiment, 95% hit rate for strawberries positioned in the middle of an image plane, 80% for left and right strawberries. The average time required to finish the task without FLC for strawberries in the middle is 13.51 s, the left is 11.04 s, and the right is 17.28 s. While the average time required to finish the task with FLC for strawberry in the middle is 12.90 s, the left side is 11.71 s, and the right side is 10.93 s. This study is intended to show that simple designs can be helpful and affordable when applied to greenhouse farming in Indonesia.

An experiential account with recommendations for the design, installation, operation and maintenance of a farm-scale soil moisture sensing and mapping system

Context The research explores the benefits of real time tracking of soil moisture for various land management contexts and the importance of spatio-temporal modelling and mapping to gain clear and visual understanding of soil moisture fluxes across a farm. Aims This research aims to outline the key processes required for building an operational on-farm soil moisture monitoring system where the product is highly granular daily soil moisture maps depicting variations temporally, spatially and vertically. Methods We describe processes of capacitance soil moisture probe installation, data collection infrastructure, sensor calibration, spatio-temporal modelling, and mapping. Key results An out-of-bag soil moisture evaluation modelling system was tested for nearly 2 years. We found a model accuracy (RMSE) estimate of 0.002 cm−3 cm−3 and concordance of 0.96 were found. This result is averaged over this period but fluctuated daily, and related to rainfall patterns across the target farm, which were not directly incorporated into the modelling framework. As expected, incorporating prior estimates of soil moisture into the modelling framework contributed to very accurate estimates of real time available soil moisture. Conclusions This research promotes the importance of iterative improvements to the soil moisture monitoring system, particularly in areas of sensor recalibration and spatio-temporal modelling. We stress the need for a longer-term view and plan for ongoing maintenance and improvement of such systems in the emerging digital farming ecosystem. Implications The results of this research will be useful for researchers and practitioners involved in the design and implementation of on-farm soil monitoring systems.

Strategic Foresight and Corporate Efficiency of Agricultural Research Institutions in Kenya: Mediating Influence of Leadership Commitment

In today's volatile, uncertain, complex, and ambiguous organizational landscape, effective leadership and management are pivotal for navigating challenges and seizing opportunities. This is especially crucial in agricultural research institutions in Kenya, tasked with enhancing productivity, sustainability, and food security. These institutions must innovate to address climate change, resource scarcity, and evolving consumer demands, yet face challenges in maintaining corporate efficiency. With global population growth and increasing food demand, there is urgency for these institutions to enhance strategic foresight and corporate efficiency. However, the impact of strategic foresight and leadership commitment on efficiency remains underexplored in Kenya's agricultural research sector. This study aimed to investigate the influence of strategic foresight on corporate efficiency and to explore how leadership commitment mediates this relationship. Utilizing a mixed methods approach—cross-sectional surveys for quantitative data and interpretive phenomenological analysis for qualitative insights—data were gathered from key segments of Kenyan agricultural research institutions. Statistical analyses, including regression and mediation analysis, were employed to test hypotheses and uncover relationships among strategic foresight, leadership commitment, and corporate efficiency. Results showed that the study achieved a robust response rate, ensuring reliable findings with strong internal consistency. Leadership in agricultural research institutes skewed male, revealing gender disparities. Age significantly influenced corporate efficiency, emphasizing strategic foresight's role. Concerns arose over short leader tenures and institutional memory loss. Supervisory roles correlated positively with corporate efficiency, consistent with prior research. Pearson's correlations showed significant relationships among corporate efficiency, strategic foresight, and leadership commitment. Structural equation modeling confirmed significant relationships, with leadership commitment partially mediating the relationship between strategic foresight and corporate efficiency. Management boards were pivotal in strategic activities and policy coordination. Despite policy alignment challenges in organizations, government support for policy enactment received positive feedback. The study recommended expansions into resource mobilization, patenting, and policy revisions aligning with digital agriculture trends. Effective governance, supportive policies, and strategic implementation were crucial for advancing agricultural research and development.

E-Rupee: Unlocking India's Digital Economy, Challenges, and Opportunities

This paper explores the concept of digital currency, differentiating between digitization and digitalization, and analyzes its different forms, specifically centralized and decentralized digital currencies. The analysis comprehensively examines Central Bank Digital Currencies (CBDCs), specifically focusing on India's e-rupee. It delves into the various forms of CBDCs, their management strategies, and the potential economic consequences they may have. The significance of digital currency in promoting financial inclusion, improving transaction efficiency, and influencing monetary policy is emphasized. Moreover, the study discusses the difficulties and dangers linked to the deployment of digital money, such as cyber-attacks, financial instability, privacy issues, and regulatory obstacles. The paper emphasizes the significance of strong risk management systems and consistent monitoring to guarantee the efficiency and safety of digital currencies. This study offers unique insights into the revolutionary potential of digital currencies and the strategic considerations required for their effective adoption by conducting a thorough assessment of the digital currency ecosystem. Moreover, the emergence of the 4th Industrial Revolution (4.0), driven by digital technologies, is transforming industries on a global scale within the field of agriculture, digitalization is transforming farming methods by implementing smart farming, precision agriculture, and digital agriculture. This paper also provides the guidelines for mitigating operational risks associated with digital currency, which may be found in the NIST Risk Management Framework and the CPMI-IOSCO Principles for Financial Market Infrastructures. Ultimately, the embrace of digital currencies and the digitization of numerous industries have the potential to stimulate economic growth, enhance financial accessibility, and proper technological advancement.

Performance analysis of agricultural machinery with aid digital agriculture

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

Aligning interoperability architectures for digital agri-food platforms

Interoperability is a critical challenge in Agri-food digitalisation, aligning with European data strategies and fostering pan-European cooperation, innovation, cost reduction, and market competition. Within the last years, the European Commission funded several projects to address interoperability in digital agriculture. We compare the projects IoF2020, ATLAS, and DEMETER which address interoperability for Agri-food systems with varying architectures and approaches, supported by the OpenDEI project. Interoperability, defined as the ability of systems to exchange and use information, encompasses multiple dimensions, including legal, organisational, semantic, and technical aspects. The analysis in this comparison focuses on the semantic and technical interoperability aspects of each project, covering architectural approaches, contextualization, marketplace functionalities, and user management and security measures.The examined projects highlight a variety of solutions, including service-oriented, data-centric, and process-oriented approaches, with the optimal choice between them remaining open, and an emphasis on the need for data consistency and plausibility. Advancements in standards and technologies, such as NGSI-LD, OGC, GAIA-X, and DSBA's implementation-driven plan, indicate a shift toward open interoperability in the broader ecosystem.

Transformative Technologies in Digital Agriculture: Leveraging Internet of Things, Remote Sensing, and Artificial Intelligence for Smart Crop Management

This paper explores the potential of smart crop management based on the incorporation of tools like digital agriculture, which considers current technological tools applied in agriculture, such as the Internet of Things (IoT), remote sensing, and artificial intelligence (AI), to improve crop production efficiency and sustainability. This is essential in the context of varying climatic conditions that affect the availability of resources for agriculture. The integration of tools such as IoT and sensor networks can allow farmers to obtain real-time data on their crops, assessing key health factors, such as soil conditions, plant water status, presence of pests, and environmental factors, among others, which can finally result in data-based decision-making to optimize irrigation, fertilization, and pest control. Also, this can be enhanced by incorporating tools such as drones and unmanned aerial vehicles (UAVs), which can increase monitoring capabilities through comprehensive field surveys and high-precision crop growth tracking. On the other hand, big data analytics and AI are crucial in analyzing extensive datasets to uncover patterns and trends and provide valuable insights for improving agricultural practices. This paper highlights the key technological advancements and applications in smart crop management, addressing challenges and barriers to the global adoption of these current and new types of technologies and emphasizing the need for ongoing research and collaboration to achieve sustainable and efficient crop production.

A Review on Scaling Up Successful Agricultural Extension Techniques for Global Benefit

Agricultural extension services are important for enhancing agricultural productivity, food security, and rural development. Examination of future and research needs highlights the significance of innovative approaches and emerging technologies, such as digital agriculture, precision farming, artificial intelligence, blockchain technology, and biotechnology, in transforming extension services. These technologies offer the potential to optimize resource use, increase yields, and improve supply chain transparency. Comprehensive impact assessments, including randomized controlled trials and mixed-methods approaches, are essential for evaluating the effectiveness of extension programs and understanding their long-term impacts on productivity, food security, and livelihoods. Supportive policy development and advocacy efforts are crucial for creating an enabling environment that promotes innovation, collaboration, and sustainable practices. This includes establishing regulatory frameworks, providing financial incentives, and integrating extension services into national development plans. Cross-disciplinary research and international collaboration are key to addressing the complex challenges facing agriculture, fostering the exchange of knowledge and best practices, and enhancing the capacity of national extension systems. Interdisciplinary research teams and systems thinking approaches can provide comprehensive solutions to agricultural challenges, while international research networks and development assistance programs can facilitate the transfer of appropriate technologies and practices. South-South cooperation offers additional opportunities for knowledge exchange and technology transfer between developing countries. By addressing these future directions and research needs, agricultural extension services can effectively contribute to sustainable agricultural development, improved livelihoods for farmers, and greater resilience to climate change. This approach will ensure that extension services remain responsive to the evolving challenges and opportunities in the agricultural sector, ultimately promoting global food security and economic growth.

Overcoming ‘Digital Divides’: Leveraging higher education to develop next generation digital agriculture professionals

Agriculture 4.0 technologies like Artificial Intelligence (AI), the Internet of Things, Computer Vision, and Robotics, have revolutionized the agricultural sector. But the adoption of digital technologies has arguably created new or exacerbated existing socioeconomic disparities, while raising ethical issues, particularly when considering technology access and adoption. So far, the literature has placed little emphasis on how to effectively bridge knowledge and skills disparities in the agriculture sector—or mend agriculture’s ‘digital divides’—through workforce development strategies that leverage higher education and extension programs. In this article, we argue that academic programs have a much bigger role to play via educational and extension efforts to equip tomorrow’s AI-ready workforce. This may be accomplished through imparting technological know-how in the form of upskilling and reskilling, and through the strategic management of equitable technological change and incorporation of ethical data management. We explain what this upskilling should consist in, where it is needed the most, and why engaging with the ethics of emerging technologies is important. The reader will get an appreciation of how academic programs can leverage digital agriculture (DA) to develop skills and talent needed for the agricultural sector to reap the benefits of digitalization and address the workforce development challenges encountered.

An in-depth review on the concept of digital farming

An in-depth review on the concept of digital farming

A Comparative Study of the Influence of Communication on the Adoption of Digital Agriculture in the United States and Brazil

A Comparative Study of the Influence of Communication on the Adoption of Digital Agriculture in the United States and Brazil

Raman Spectroscopy for Plant Disease Detection in Next-Generation Agriculture

The present review focuses on recent reports on the contribution of the Raman method in the development of digital agriculture, according to the premise of maximizing crops with a minimal impact of agriculture on the environment. The Raman method is an optically based spectrum technique that allows for the species-independent study of plant physiology as well as the real-time determination of key compounds in a non-destructive manner. The review focuses on scientific reports related to the possibility of using the Raman spectrometer to monitor the physiological state of plants and, in particular, to effectively diagnose biotic and abiotic stresses. This review primarily aims to draw attention to and raise awareness of the potential of Raman spectroscopy as a digital tool capable of bridging the gap between scientists’ detailed knowledge of plants grown under laboratory conditions and farmers’ work. The Raman spectrometer allows plant breeders to take appropriate measures in a well-defined area, which will reduce the territory occupied by biotic and abiotic stresses, thus increasing yields and improving their quality. Raman technology applied to modern agriculture can positively affect the accuracy and speed of crop quality assessments, contributing to food safety, productivity and economic profitability. Further research and analysis on cooperation between farmers and scientists is indispensable to increase the viability and availability of Raman spectrometers for as many farmers and investors as possible.

Digital Agriculture using advanced ICT and Agricultural Information Systems- The general and financial aspects in Indian Context

"Agricultural Informatics is a modern and interdisciplinary field of practice and study. It has incorporated from the information technologies, basic technologies and various aspects of agriculture, and horticulture. Agricultural Informatics sometimes refereed as ICT in Agriculture, Agricultural Information Systems, Agro Information Technology, etc, It is dedicated in transforming agriculture sector supported by information driven systems which are supported by ICT). Agro Informatics is responsible in delivering as well as accessing information. The applications in Agro Informatics in agriculture enhancing agriculture production, nationally and internationally and as a result new practicing concepts been developed called Digital Agriculture and Smart Agriculture. In addition to basic Information Technologies Agricultural Informatics is also supported by different other emerging sub components such as Big Data, Data Analytics, Cloud and Virtualization, Internet of Things, Wireless network with Converged Network, Usability and HCI, UXD, Sensor and Multimedia Systems. Different economical and financial issues are alarming and growing internationally in respect of Agriculture and environment. A proper Agro Informatics practices can lead the development a healthy and sophisticated agricultural systems, and proper applications helps in reaching the status of Digital Agriculture and Smart Farming supported by government, administration, universities, agricultural institutes and associations. This paper highlights the basics of Agro Informatics and Financial aspects related to the Smart Agriculture in Indian context. As a review based work existing works complied and reported here."

Robotic irrigation complex for crop cultivation in irrigated areas

Intelligent technologies used in digital agriculture, such as remote sensing, serve as a necessary tool for collecting bio-object data. It has been established that data collected through remote sensing and the Internet of Things (IoT), including comprehensive imaging of agricultural bio-objects, and their subsequent analysis will help solve numerous challenges in agricultural production. (Research purpose) The research aims to develop and validate a robotic irrigation complex for crop cultivation. This system will optimize irrigation processes by considering various environmental factors such as soil moisture, air temperature, wind speed, and other relevant conditions. (Materials and methods) The paper evaluates the signifi cance of developing a design that can optimize the process of crop cultivation and details the applications and databases integrated within the central computing center. The central computing hub monitors the current state of the irrigation area, performs calculations and develops management decisions. Utilizing mobile communications, this hub receives, processes, and transmits commands to the control panels of sprinklers, the pumping station, and the dispatcher for the machine-tractor team. (Results and discussion) The research has validated the design and main components of a robotic irrigation system for crop cultivation. Furthermore, an algorithm has been established to orchestrate the processes of data collection, calculation, and the control and management of operations. It was found that such a complex would optimize the irrigation process, reduce costs and resource consumption in the irrigated area, and decrease environmental pollution. (Conclusions) The implementation of a robotic irrigation complex will reduce water consumption, as well as material and energy costs associated with irrigation. By maintaining soil moisture within optimal ranges, this complex will enable the consistent production of high-quality crops under various weather conditions.

Development of a computational routine to evaluate the height of cotton plants

Aerial photogrammetry with drones is being frequently used in agriculture. Since computational tools can optimize various personalized workflows for Digital Agriculture, the objective of this study was to develop modules (scripts) of a computational routine called FindPLANT-V1, leaving it personalized for testing on a preliminary basis, through products aerial photogrammetry obtained with a drone, the spatial and temporal variability of the height of cotton plants. To develop the study, an experimental area of ​​500 m2 was selected with irrigated cotton cultivation at the State University of Montes Claros (Unimontes) Experimental Farm, in Janaúba/MG. The FindPLANT-V1 routine has four modules, structured through object-oriented programming with Python 3.8 in the Spyder IDE. The following libraries were used to develop the scripts: Gdal, Imageio, Math, Matplolib, Numpy, OpenCV, OS, OSR, OGR, Pandas, Rasterstats and Scipy. A script was also developed to create the essential directories (inputs and outputs) for the operation of this routine. As a result of aerophotogrammetric geoprocessing, the average ground sample distances (GSD) of classic orthophotomosaics was 1.6 cm. The average GSD of the Digital Surface Model (DSM) and Digital Terrain Model (DTM) was 3.2 cm. Such products, routine inputs, made it possible to estimate the spatial and temporal variability (module 1.ST), the canopy height model (module 2.ProCHM), the zonal statistics of the plots (3.ZonalStat). The results of the routine developed, although partial, made it possible to analyze the spatial variability of cotton plants and quantify the height in different phases of the cycle, presenting flexibility of adaptation for herbaceous crops.

Exploring inclusive innovation: A case study in operationalizing inclusivity in digital agricultural innovations in Kenya

CONTEXTDigital technologies promise to transform agri-food systems such that they contribute to a more inclusive, economically, and/or environmentally sustainable future. However, without careful attention to the approaches for their introduction, development, and rollout, they are likely to fail to address key aspects of inclusion. Contemporary inclusive innovation mechanisms focus on the involvement of farmers in the innovation processes, but do not elucidate how to operationalize a deeper inclusivity that takes account of the nature of engagement with the farmers and dynamics of the relationships required for successful outcomes. OBJECTIVEThis article analyzes the concept of inclusivity and its operationalization using the case of the Kenya Agricultural Livestock and Research Organization (KALRO). We develop lines of inquiry on how inclusivity can be defined, sought and operationalized in digital agri-food systems. METHODSBased on a case study approach, we interviewed 36 stakeholders in the KALRO digital transformation initiative reflecting on literature discussions from secondary data. Primary and secondary data were synthesized to develop lines of inquiry that enabled a deeper exploration of inclusivity. RESULTS AND CONCLUSIONSKALRO's rollout and scale-up of digital agriculture technologies for farmers hinges on three evaluative criteria – usability, accessibility, and relevance – which are associated with aspirations for inclusive innovation. Despite these efforts, some technologies they developed included some farmers and excluded others. For the included, inclusion was limited to the accessibility of certain types of information while desired forms of information lacked sufficient mechanisms for access or were not made available. The practice of promoting access, usability and relevance of the technologies while has supported innovation for and with the farmers, there are still challenges in innovating with the farmer. The lines of inquiry “inclusive innovation with” and “inclusive innovation for” introduced in this paper not only help stakeholders gain a deeper, more nuanced and holistic understanding of inclusive innovation, they also reveal the complexity of inclusivity and its operationalization in agri-food systems that are digitalizing. SIGNIFICANCEThis paper analyzes inclusivity from a practice perspective highlighting areas where the theoretical perspective and empirical observations diverge. The paper makes the case for meaningful changes in how inclusivity is approached, considering the practical needs and preferences of those directly impacted by innovation and the potential of operationalizing ‘inclusive innovation with’ as a deeper and more effective approach to inclusive innovation.

Effects of Heat and Drought Stress on Sustainable Agriculture and Future Food Security in Türkiye

This review investigates the effects of heat and drought stress on future food security of Turkish agriculture. Temperature average is expected to rise to 3.2°C at the end of the current century while annual precipitation will decline more than 10% in the west and south and rise by 20% in the north of Türkiye, implying that climate change will affect ecosystem sustainability. It is therefore crucial to develop strategies to mitigate and adapt to climate change such as adjusting the planting schedule, reduced tillage, fertiliser microdosing, pre-sowing seed treatment, and the application of growth promoting bacteria to improve tolerance to stress by comprehending how plants respond physiologically and biochemically under these stress conditions. Long-term heat stress may hinder photosynthetic electron transport, decreasing the plant's ability to make use of energy for photosynthesis. The immediate response of plants under drought stress involves closing stomatal openings to reduce water loss through stomatal conductance. Combined heat and drought stress have a greater adverse effect on plant development and production than their effects in isolation. Plant phenotyping can play a major role in “climate-proofing” Turkish agriculture through the identification and development of crop varities with improved prouctivity, climate resilience and input requirements. Digital agriculture will also improve the efficiency of Turkish agricultural systems as the adapt to a hotter drier climate. To ensure future food security and the viability of the agro-economic system in Türkiye steps must be taken to make Turkish agriculture more robust in preparation for the impacts of climate change.

The Main Aspects of Wireless Sensor Nodes for Digital Agriculture

The Main Aspects of Wireless Sensor Nodes for Digital Agriculture

Interviews with farmers from the US corn belt highlight opportunity for improved decision support systems and continued structural barriers to farmland diversification

Diversifying high-input, monocropped landscapes like the US Corn Belt would provide both economic and ecosystem service benefits to the agricultural landscape. Decision support systems (DSS) and digital agriculture could help farmers decide if diversification is suitable for their operation. However, adoption of DSS by farmers remains low, likely due to lack of farmer engagement before and during the DSS development process. This study aimed to better understand the tasks, tools, and people involved in implementing farmland diversification with the goal to inform design of agricultural DSS. Semi-structured interviews were conducted with 11 farmers who had diversified their corn/soybean cropland with government-supported conservation programs (e.g., CRP, wetlands) and alternative crops (e.g., small grains, pasture) in the past four years. Interview data was transcribed and then analyzed using affinity diagramming. Results show farmers needed DSS to layer multiple sources of data and observations over several years to identify field productivity trends and drivers; spatial orientation of practices to fit management and field constraints; matching operation goals to alternative practices; financial planning and market exploration; and information on promising emerging practices like subsidized pollinator habitat. However, the interviews also highlighted structural barriers to diversification that DSS cannot or can only partially address. These included social pressures; market access; crop insurance policy; and quality of relationships with governmental agencies. Results indicate better DSS design can empower individual farmers to diversify cropland, but structural interventions will be needed to successfully diversify the agricultural landscape and support economic and ecosystem health.