Digital Agriculture Research Articles

A Temporal–Geospatial Deep Learning Framework for Crop Yield Prediction

With the rapid development of information technology, the demand for digital agriculture is increasing. As an important agricultural production topic, crop yield has always attracted much attention. Currently, artificial intelligence, particularly machine learning, has become the leading approach for crop yield prediction. As a result, developing a machine learning method that accurately predicts crop yield has become one of the central challenges in digital agriculture. Unlike traditional regression prediction problems, crop yield prediction has a significant time correlation. For example, weather data for each county show strong temporal correlations. Moreover, geographic information from different regions also impacts crop yield to a certain extent. For example, if a county’s neighboring counties have a good harvest, then this county is likely to have high yields as well. This paper introduces a novel hybrid deep learning framework that combines convolutional neural network (CNN), graph attention network (GAT) and long short-term memory (LSTM) modules to enhance prediction accuracy. Specifically, CNN is employed to extract the features from the input data for each county in each year. GAT is introduced to model the geographical relationships between neighboring counties, allowing the model to capture spatial dependencies more effectively. LSTM is used to extract the temporal information within many years. The proposed hybrid deep learning framework CNN-GAT-LSTM captures both the temporal and spatial relationships, thereby improving the accuracy of yield prediction. We conduct experiments on a nationwide dataset that includes data from 1115 soybean-producing counties in 13 states in the United States covering the years from 1980 to 2018. We evaluate the performance of our proposed CNN-GAT-LSTM model based on three metrics, namely root of the mean squared error (RMSE), R-squared (R2) and correlation coefficient (Corr). The experimental results demonstrate that the proposed model achieves significant performance improvements over the existing state-of-the-art model, with RMSE reduced by 5%, R2 improved by 6% and Corr enhanced by 4%.

Unlocking Innovation in Crop Resilience and Productivity: Breakthroughs in Biotechnology and Sustainable Farming

Recent advancements in agricultural biotechnology and sustainable practices are revolutionizing crop production and resilience, addressing global food security challenges. This review highlights cutting-edge innovations in plant sciences, including genetic engineering, genome editing technologies, and biotechnological interventions that enhance crop resistance to biotic and abiotic stresses. It discusses the integration of precision agriculture and digital farming tools that optimize resource use and boost productivity, providing data-driven insights for better crop management. Additionally, sustainable agricultural practices such as agroecology, organic farming, and integrated pest management are examined for their potential to reduce environmental impact while maintaining high yield. The review investigates the key practices like conservation tillage, crop rotation, and biological pest control, emphasizing their roles in enhancing soil health, biodiversity, and agricultural resilience. Significant contributions of biotechnological interventions, including genetic engineering and genome editing, are explored for developing stress-resistant and high-yield crop varieties. Moreover, the integration of precision agriculture and digital farming technologies is discussed for their potential to optimize resource utilization and improve crop management through advanced data analytics.

Internet of Things : A Way of Transforming Conventional Agriculture

The Internet of Things (IoT) is the network of various devices seamlessly exchanging data. IoT has initiated the 4th agricultural revolution known as Agriculture 4.0/Digital Agriculture (DA)/Precision Agriculture (PA)/Smart Agriculture (SA). All farming activities can be monitored and managed remotely. With IoT, agriculture is becoming more energy-efficient, precise and effective. IoT is emerging as a potential solution to current world problems. In this review paper, the application of IoT in various agricultural activities is discussed. The paper highlights how IoT is transforming the execution of various agricultural activities.

Impact of inbound marketing on Consoagro junior company of the State University of Londrina

This work addressed the transition of Consoagro Empresa Júnior to Inbound Marketing in 2021, 2022, and 2023, highlighting the growing relevance of agricultural innovation and communication strategies. The adoption of Inbound Marketing was motivated by the need to acquire a strategy that would strengthen Consoagro's brand presence in the digital environment and assist in the passive acquisition of clients. With the general objective of analyzing the impacts of this transition, a quantitative approach was employed, using Consoagro as a case study. The methodology included document analysis and impact assessment over the years using Excel data spreadsheets and statistical analysis using the ANOVA and Tukey test in R software. After analyzing the results, it was possible to identify that the strategy not only strengthened Consoagro's online presence but also contributed to the promotion of digital agriculture. Facing challenges such as increasing market competition and the lack of prospecting, the company turned these difficulties into strategic opportunities, using the production of interesting and relevant content to attract and engage its audience effectively. In summary, this study highlighted the importance of innovation as an essential catalyst for facing contemporary challenges in the agricultural context. By integrating Inbound Marketing as part of a broader strategy of growth and adaptation, Consoagro not only improved its visibility and operational efficiency but also established a successful model for other junior companies in the agricultural sector seeking to differentiate and thrive in a competitive and dynamic environment.

Predicting oil accumulation by fruit image processing and linear models in traditional and super high-density olive cultivars

The paper focuses on the seasonal oil accumulation in traditional and super-high density (SHD) olive plantations and its modelling employing image-based linear models. For these purposes, at 7-10-day intervals, fruit samples (cultivar Arbequina, Fasola, Frantoio, Koroneiki, Leccino, Maiatica) were pictured and images segmented to extract the Red (R), Green (G), and Blue (B) mean pixel values which were re-arranged in 35 RGB-derived colorimetric indexes (CIs). After imaging, the samples were crushed and oil concentration was determined (NIR). The analysis of the correlation between oil and CIs revealed a differential hysteretic behavior depending on the covariates (CI and cultivar). The hysteresis area (Hyst) was then quantified and used to rank the CIs under the hypothesis that CIs with the maximum or minimum Hyst had the highest correlation coefficient and were the most suitable predictors within a general linear model. The results show that the predictors selected according to Hyst-based criteria had high accuracy as determined using a Global Performance Indicator (GPI) accounting for various performance metrics (R2, RSME, MAE). The use of a general linear model here presented is a new computational option integrating current methods mostly based on artificial neural networks. RGB-based image phenotyping can effectively predict key quality traits in olive fruit supporting the transition of the olive sector towards a digital agriculture domain.

Digital agriculture’s impact on carbon dioxide emissions varies with the economic development of Chinese provinces

Digital empowerment is a dynamic process of complementary digital technology and production factors and plays a critical role in achieving the dual carbon objectives of the carbon peak by 2030 and carbon neutrality by 2060. Here, we use the input-output method, employing panel data on environmental and social factors from 30 provincial-level regions in China from 2012 to 2021. We explore the impact of digital empowerment development on agricultural carbon dioxide emissions and underlying mechanisms. We found that the relationship between digital empowerment and carbon dioxide emissions is nonlinear and follows an inverted U-curve. The carbon dioxide emissions increase as digital empowerment increases and decrease when digital empowerment crosses the inflection point of 0.0862. Digital empowerment reduces agricultural carbon emissions by optimizing carbon-intensive factor inputs such as fertilizers and improving factor allocation efficiency. Our research provides evidence for policymakers to enable the promotion of digital empowerment of farmers across Chinese provinces.

Awareness and perception on the use of smartphone applications for crop protection among undergraduate students in Southwestern Nigeria

Mobile applications in crop protection especially for speedy pest diagnosis, monitoring, and management are increasingly gaining global attention and support. An understanding of the awareness and perception of the youths, particularly students in tertiary institutions (who are heavy users of smartphones), on the use of smartphone applications for crop protection, is crucial for sharpening their orientations towards embracing the digital ecosystem in agriculture. The imperativeness is evident in the ongoing government drive towards promoting students’ engagement in digital agriculture as accommodated in the National Agricultural Technology and Innovation Policy (2022–2027). Consequently, this study investigated awareness and perception of mobile phone applications among crop and agricultural extension-related final-year students in selected Universities in Southwest Nigeria. A multistage sampling procedure was used in selecting 407 students. Data were obtained with the aid of questionnaires. Data were summarized using descriptive statistics such as frequency counts, percentages, mean and standard deviation using SPSS software. Findings show that very few (less than 8%) of the respondents were aware of the usage of specific smart/android phone applications for pest identification, disease diagnosis, and weather forecasting. Less than 4% ever used smart/android phones for pest identification, crop protection, and information sourcing on disease diagnosis and management. However, their perception of the need for pest and disease diagnosis as a foundation step to enhance crop protection (4.65 points on a scale of 5), and readiness to use android/smartphone applications for this purpose (MS = 4.45) was positive. There are enormous implications for increased awareness and knowledge sharing on the use of android/smartphone applications for crop protection and other interventions to promote digital agriculture among students in Nigeria.

Sensing and Perception in Robotic Weeding: Innovations and Limitations for Digital Agriculture

The challenges and drawbacks of manual weeding and herbicide usage, such as inefficiency, high costs, time-consuming tasks, and environmental pollution, have led to a shift in the agricultural industry toward digital agriculture. The utilization of advanced robotic technologies in the process of weeding serves as prominent and symbolic proof of innovations under the umbrella of digital agriculture. Typically, robotic weeding consists of three primary phases: sensing, thinking, and acting. Among these stages, sensing has considerable significance, which has resulted in the development of sophisticated sensing technology. The present study specifically examines a variety of image-based sensing systems, such as RGB, NIR, spectral, and thermal cameras. Furthermore, it discusses non-imaging systems, including lasers, seed mapping, LIDAR, ToF, and ultrasonic systems. Regarding the benefits, we can highlight the reduced expenses and zero water and soil pollution. As for the obstacles, we can point out the significant initial investment, limited precision, unfavorable environmental circumstances, as well as the scarcity of professionals and subject knowledge. This study intends to address the advantages and challenges associated with each of these sensing technologies. Moreover, the technical remarks and solutions explored in this investigation provide a straightforward framework for future studies by both scholars and administrators in the context of robotic weeding.

Data-Driven Innovations and Sustainability of Food Security: Can Asymmetric Information Be Blamed for Food Insecurity in Africa?

Africa still struggles to end hunger, partly because not all Africans have access to nutritious food. Although studies have established the connection between digital technologies and food security, the reality in Africa is that, despite the laudable feat in the use of digital technologies, the accessibility and utilization of food still face challenges. Digital agriculture, or technology, is a data-driven innovation that predicts agricultural outcomes and guides food producers throughout the different phases of operations on the farm. The literature documents the efficacy of digital agriculture in food production and availability well, but it has hardly examined how it enhances food accessibility and utilization. And even though studies that have examined food accessibility and utilization have merely assessed income as a tool that guarantees food accessibility and utilization, not much attention has been paid to how digital resources can aid in the access to and utilization of food. Drawing on information asymmetry theory and the systematic qualitative method, this article investigates how digital agriculture, through the internet and mobile phones, enhances efforts towards the accessibility and utilization of food as prerequisites for the attainment of SDG 2 in Africa. The findings provide an understanding of the potential of digital technologies in promoting the accessibility and utilization of food. It advocates strategies through which stakeholders in the agricultural sector can utilize technology in ways that aid Africa’s strategic efforts to attain food security and zero hunger.

A tool for integrating agrometeorological observation data for digital agriculture: A Minnesota case study

AbstractAgrometeorological data are essential for understanding production using digital agriculture techniques. However, integrating agrometerological observations from multiple sources remains a challenge. Often, digital agriculture scientists download and clean the same datasets many times. We present a prototype system that simplifies the process of collecting, cleaning, integrating, and aggregating data from meteorological data sources by providing a simplified user interface, database, and application programming interface. The prototype provides a standard interface for querying multiple geospatial formats (raster and vector) and integrates observation networks including the National Oceanic and Atmospheric Administration Global Historical Climatology Network (NOAA GHCN), NOAA NClim‐Grid (NOAA's Gridded Climate Normals), and Ameriflux BASE. The system automatically checks and updates data, saving storage space and processing time, and allows users to summarize data spatially and temporally. Provided as open source code and browser‐based user interface, the application and integration system can be run across Windows, Linux, and Mac environments to support broader use of multi‐source agrometeorology data.

Effects and mechanisms of digitalization to enhance the resilience of the hog breeding industry: evidence from China

IntroductionWith the rapid development of digital agriculture, digitalization has gradually become a key factor affecting the resilience of China’s pig farming industry.MethodsFrom both the test results and the theoretical point of view, the individual fixed-effect model is more suitable for this paper’s study. Therefore, based on the panel data of 31 provinces in China from 2011 to 2022, this study constructs an individual fixed-effect model to examine how digitization affects the resilience of China’s pig farming industry.Results and discussionThe findings indicated that digitization significantly enhanced the resilience of China’s hog breeding industry, especially in potential growth areas. Improving breeding technology and scale is a crucial avenue for digitalization to bolster the industry’s resilience. Digitalization exerts a double threshold effect on the industry’s resilience, with its promotional impact only realized within a specific range. The article proposes policy suggestions, such as strengthening infrastructure construction and giving play to regional advantages.

From profitability to trust: factors shaping digital agriculture adoption

From profitability to trust: factors shaping digital agriculture adoption

Winter Wheat Yield Prediction Based on the ASTGNN Model Coupled with Multi-Source Data

Timely and accurate prediction of winter wheat yields, which is crucial for optimizing production management, maintaining supply–demand balance, and ensuring food security, depends on interactions among numerous factors, such as climate, surface characteristics, and soil quality. Despite the extensive application of deep learning models in this field, few studies have analyzed the effect of the large-scale geospatial characteristics of neighboring regions on crop yields. Therefore, we present an attention-based spatio-temporal Graph Neural Network (ASTGNN) model coupled with geospatial characteristics and multi-source data for improved accuracy of winter wheat yield estimation. The datasets used in this study included multiple types of remote sensing, meteorological, soil, crop yield, and planting area data for Anhui, China, from 2005 to 2020. The results showed that multi-source data led to higher prediction performance than single-source data, and enabled accurate prediction of winter wheat yields three months prior to harvest. Furthermore, the ASTGNN model provided better prediction performance than two traditional crop yield prediction models (R2 = 0.70, RMSE = 0.21 t/ha, MAE = 0.17 t/ha). Therefore, ASTGNN enhances the accuracy of crop yield prediction by incorporating geospatial characteristics. This research has implications for improving agricultural production management, promoting the development of digital agriculture, and addressing climate change in agriculture.

Analysis and Design of UI/UX Models for Elderly Users in Digital Agricultural Business Transformation

Digital agriculture faces challenges, namely in integrating Elderly People (SENIORS) in digital transformation. The purpose of the study is to analyze and design UI/UX models according to user needs, expectations, and challenges. The method used is the Design Thinking approach which begins with literature study, then interviews are conducted to identify problems faced by users, and analyze the need to determine problem priorities. UI/UX prototypes are carried out to design solutions that suit user needs, and evaluations are carried out to collect user feedback. The data is assessed through the System Usability Scale (SUS) approach to appraise the designed user interface (UI). The evaluation results showed a "good" rating based on the SUS category, with a final average score of 74,391, so that the solutions designed meet usability standards for SENIORS users in the context of digital agriculture. Thus, the research carried out contributes to increasing the effectiveness and adoption of SENIORS in digital agriculture, as well as by designing user-friendly UI/UX models, making an important step towards inclusive and sustainable digital agriculture.

An Expert Model Using Deep Learning for Image-based Pest Identification with the TSLM Approach for Enhancing Precision Farming

The rise of digital agriculture has sparked considerable interest in its potential to revolutionize farming practices by integrating of advanced technologies. Unmanned aerial vehicles (UAVs), commonly known as drones, have emerged as a crucial tool in precision agriculture, offering diverse applications for real-world scenarios. This research focuses on harnessing the power of drones in the context of digital agriculture support, particularly in addressing the challenges of crop protection and pest management. The objective is to develop an innovative approach for crop-disease diagnosis using an upgraded Transfer-Driven Self-Adaptive Learning Model (TSLM) that leverages multispectral remote sensing data of drone-captured images to improve pest classification and streamline pesticide application. The study explores nine potent deep neural network models' capabilities for identifying plant diseases using various approaches within the digital agriculture framework. Transfer learning and advanced feature extraction techniques are employed to tailor these deep neural networks to the specific crop protection context. Using of pre-trained deep learning models for feature extraction and fine-tuning enhances the effectiveness of the proposed model. Evaluating the model's performance, precision, sensitivity, specificity, and F1-score metrics are assessed, leading to the discovery that deep feature extraction and Self-Adaptive Learning Model (SLM) classification outperform traditional transfer learning methods. The novelty of this work lies in its application of communication protocols to coordinate a fleet of drones for crop protection within the digital agriculture framework. By combining deep learning techniques with transfer-driven self-adaptive learning, the proposed approach significantly enhances the accuracy and efficiency of pest classification in precision agriculture. The study's findings offer valuable insights into optimizing drone-based technologies to combat plant disease epidemics, thereby contributing to the advancement of digital agriculture and its role in supporting sustainable farming practices.

Investigating the impact of the internet on managing green financial innovation and improving agricultural conditions in water-scarce Asian regions using ANN modeling

This research focuses on the importance of management strategies, green innovation, and sustainable practices in the agricultural sector. These factors are crucial for job creation, food security, and environmental conservation. Particularly in water-scarce regions, effective management is necessary to overcome natural resource constraints and encourage a shift towards digital agriculture (AGRI). The study aims to identify and analyze the challenges and issues related to agricultural research and technology in Asian countries. The collected data were analyzed using exploratory factor analysis in the SPSS software. The analysis revealed a range of issues and challenges for agricultural development, including those related to the structure and policy framework, the availability and quality of resources and infrastructures, and the provision of effective support services, all of which encompassed factors such as research and technology investment, research management, productivity, research culture, networking, and the integration of higher education and agricultural research. To estimate the efficiency of technology development, agricultural development, and support services for AGRI, an artificial neural network (ANN) was utilized. The ANN was trained by incorporating changes in management strategies, green innovation, and sustainability across a broader range of experimental scenarios. The evaluation of the ANN's predictions showed that improvements in management strategies and the adoption of green innovation and sustainability significantly impacted the productivity of technology development, agricultural development, and support services for AGRI. The accuracy of the ANN's predictions was further assessed using linear regression. The results indicated an acceptable level of error when compared to the target results obtained from experimental tests. Overall, this study emphasizes the importance of effective management, green innovation, and sustainable practices in driving advancements in technology and agricultural development.

Research on the Behavior Recognition of Beef Cattle Based on the Improved Lightweight CBR-YOLO Model Based on YOLOv8 in Multi-Scene Weather.

In modern animal husbandry, intelligent digital farming has become the key to improve production efficiency. This paper introduces a model based on improved YOLOv8, Cattle Behavior Recognition-YOLO (CBR-YOLO), which aims to accurately identify the behavior of cattle. We not only generate a variety of weather conditions, but also introduce multi-target detection technology to achieve comprehensive monitoring of cattle and their status. We introduce Inner-MPDIoU Loss and we have innovatively designed the Multi-Convolutional Focused Pyramid module to explore and learn in depth the detailed features of cattle in different states. Meanwhile, the Lightweight Multi-Scale Feature Fusion Detection Head module is proposed to take advantage of deep convolution, achieving a lightweight network architecture and effectively reducing redundant information. Experimental results prove that our method achieves an average accuracy of 90.2% with a reduction of 3.9 G floating-point numbers, an increase of 7.4%, significantly better than 12 kinds of SOTA object detection models. By deploying our approach on monitoring computers on farms, we expect to advance the development of automated cattle monitoring systems to improve animal welfare and farm management.

VIKSHIT BHARAT @ 2047: PATHWAY TO SUSTAINABLE AGRICULTURE DEVELOPMENT IN INDIA

India’s economic growth is projected to reach 8.2%in 2023-24(National Statistical Office).It is to be ranked fifth globally in terms of GDP and third in Purchasing Power Parity (PPP) in 2024. The IMF estimates that India’s GDP will grow by 7% in 2024. As India approaches 2047, celebrating 100 years of independence, the vision of “Vikshit Bharat @2047” (Developed India) is emerging, aiming to move to a High-income status and surpass $30 trillion GDP. This study explores the role of sustainable agricultural development in achieving the vision of “Vikshi Baharat”, focusing on incorporating advanced technologies into farming practices. The study explores the potential of innovations such as Precision Farming, Biotechnology, Machine Learning and Digital Agricultural, along with Sustainable Farming Practices, Water Resource Management, Soli Health Practices and Supporting Policy. Furthermore, the study highlights the importance of Agricultural Finance and Public-Private Partnership in economic development.

Incorporation of computational routines in a microservice architecture in AgDataBox platform

Agriculture has been undergoing a digital process that aims to apply digital technologies to make the sector more productive, profitable, and environmentally responsible. This trend has been adopted since applying precision agriculture (PA) techniques and, more recently, with digital agriculture (DA). DA aims to use all available information and knowledge to enable the automation of sustainable processes in agriculture, applying data analysis methods and techniques by specific software and platforms to collect and transform data into meaningful information for agriculture. Platform AgDataBox (ADB) offers tools to allow agriculture specialists to obtain, process, and visualize data for the correct decision-making. However, its structure needed to be readjusted to new software architecture to allow the aggregation of new functionalities and expand the ADB platform. This study aimed to develop a web microservices architecture (ADB-MSA) to incorporate the required functionalities to create thematic maps (TMs) and delineate management zones (MZs). ADB-MSA provided eight microservices, six of which (statistics, spatial, interpolation, clustering, rectification, and lime/nutrient recommendation) execute procedures based on JavaScript, R, and Python programming languages. At the same time, the other two are used to store data. In the case study, the procedures to create TMs and delineate MZs were performed with data from one commercial area. Thus, the services provided in the architecture meet the steps of creating TMs and delineating MZs, as MZs for fertilizer application were generated and evaluated according to phosphorus and potassium requirements. ADB-MSA allows the development of several new client applications (web, mobile, desktop, and embedded systems) to promote solutions in agriculture, streamlining processes, as it abstracts the implementation and execution complexity of available algorithms.

How does digital technology application empower specialty agricultural farmers? Evidence from Chinese litchi farmers

IntroductionSpecialty agricultural farmers have widely adopted digital technologies in all aspects of their specialty agricultural operations. However, the effect of digital technology application (DTA) on specialty agricultural farmers’ income has not been fully evaluated. Moreover, it remains unclear whether DTA enhances farmers’ income (FI) by improving their varied capabilities. To fill this gap, we analyzed the intrinsic relationship and mechanism between DTA, farmers’ capability (FC), and specialty agricultural farmers’ income.MethodsUsing field survey data from 635 litchi farmers in China, we employed OLS regression models and mediation effect models to empirically investigate DTA’s direct and indirect impacts on litchi farmers’ income. Additionally, we examined the group heterogeneity and regional heterogeneity.Results and discussionThe empirical results show that DTA can promote specialty agricultural farmers’ income by enhancing their production capacity (PC) and transaction capacity (TC). Heterogeneity analysis showed that the empowering effect of DTA is especially significant for disadvantaged farmers, and farmers who are in specialty agricultural resources-rich regions.ConclusionThis study contributes to the body of research on the empowerment of specialty agricultural farmers by examining the effect of DTA from a capability perspective. Consequently, to better play the empowering effect of DTA for specialty agricultural farmers, policymakers should account for regional disparities in promoting digital agriculture, and enhance heterogeneous farmers’ DTA capability.