User-Technological Index of Precision Agriculture

Using data from a survey of cotton producers in 14 US states, and a bivariate probit regression, this study examined the effects of the following measured parameters on the adoption of Automatic Section Control (ASC) technologies and GPS Auto-Guidance (AG) systems: age, education, farm size, field geometry, information sources, as well as the use of specific production practices and other Precision Agriculture (PA) technologies. Results suggest that younger, more educated producers, consulting farm dealers for information about PA technologies, using other PA technologies, and managing larger farming operations located in counties with more irregularly shaped fields are more likely to adopt ASC technologies and AG systems. Predicted adoption probabilities estimated using regression results suggest the use of other PA technologies and farm dealers as a source of precision farming information have the largest impact on the probability of adopting ASC by cotton farmers. Additionally, these results suggest farmers with operations in eastern Arkansas, western Tennessee, and a couple of counties in middle Tennessee are more likely to adopt ASC technologies. Producers in these regions had the highest percentages of users of other PA technologies and farm dealers to obtain PA information.

Modeling soil organic carbon and carbon dioxide emissions in different tillage systems supported by precision agriculture technologies under current climatic conditions

This chapter is a collection of selected abstracts presented at the 10th Asian-Australasian Conference on Precision Agriculture, held from October 24th to 26th in Putrajaya, Malaysia. It aims to emphasize the transformative potential of technology in precision agriculture and smart farming. The featured studies highlight the transformative impact of technology and current improvements in agriculture, offering modern solutions including machine learning, robotics, remote sensing, and geographic information systems (GIS). From autonomous navigation for mobile robots to stress classification in crop production systems, and from phenotypic analysis with LiDAR technology to real-time sensor monitoring in greenhouse agriculture, the majority of abstracts underline the integration of digital tools in different fields of farming with the core objective of reshaping conventional farming techniques and eliminating dependency on manual works. Key examples include the development of a distributed sensing system (DSS) used for orchard robots, stress classification for tomato seedlings through image-based color features and machine learning, and the integration of remote sensing and AI in crop protection. Other solutions, such as automated spraying robots for cherry tomato greenhouses, active back exoskeletons for rice farm lifting tasks, and advancements in seedling transplanting techniques, have shown promising results for contributing to sustainable farming practices by providing accurate and timely information for decision-making amid climate change-induced uncertainties.

Adoption of Precision Agriculture (PA) technologies has apparent benefits for agricultural production management, cost savings, and environmental sustainability. However, these technologies are also extremely complicated and influenced by various social and economic forces, which impact their adoption rate, particularly in developing nations. Demographic and agricultural characteristics also play an important role in determining willingness to adopt PA. This study is a survey aimed at establishing the willingness indices of farmers and other relevant personnel to embrace precision farming technologies in Gombe and Bauchi states of Nigeria. The survey included individuals of varied ages; but an average age of 28.5 years for Gombe and 36.5 years for Bauchi, educational levels, and employment statuses, as well as agricultural and operational features. The willingness to adopt PA is high in both states, with 92% in Gombe and 96% in Bauchi. The findings suggest that, while high costs and a lack of technical know-how had a negative impact on readiness to adopt PA in both states, the level of complexity was rated higher by the older generation in Bauchi state, which had a comparatively high mean farmer age. The size of the farm and the number of employees had no direct influence on the adoption of PA technology. Other criteria, such as age and number of years of operation, indicate overlap between the various stages of internet and digital technology adoption, mechanization, and PA. The high willingness rating indicates that farmers are willing to adopt PA technologies if made accessible. It is therefore advised that legislative tools, training programs, and necessary access to financing facilities be employed to make these technologies available and cheap for farmers.

Advancements in precision agriculture technologies enable producers to achieve higher yields; however, in some cases, these innovations have not reached widespread adoption despite years of availability. We sought to understand producers’ adoption experiences with two precision agriculture technologies: Nitrogen (N)-Rich Strips and the Sensor Based Nitrogen Rate Calculator (SBNRC). These technologies can help producers optimize their application of nitrogen fertilizer on growing crops, especially small grains such as wheat. Using Rogers’ (2003) diffusion of innovations theory as an explanatory framework, this descriptive-exploratory study examined the adoption behaviors of producers from two midwestern states. Rogers’ (2003) theoretical lens guided instrument development and interpretation of results. To better understand the effects of change agents’ actions and potential adopters’ behaviors during the innovation-decision process, more research is needed regarding disenchantment discontinuance and replacement discontinuance, the potential for pro-innovation bias, and of the innovation attribute compatibility. The future development of precision agriculture technology with the perceptions of potential adopters in mind, especially those averse to adoption and continuance, may assist in overcoming barriers to widespread diffusion.

The purpose of this research is to study the experience of introducing precision farming technologies abroad and to substantiate the directions of development and practical implementation of these technologies in Belarus. The article discuss-es the leading countries in the field of practical testing of modern technologies of precision farming, analyzes the experience of their use in the EAEU countries, studies the developments in the main directions of the introduction of precision farming in Belarus: the use of GPS for determining field boundaries, remote sensing of the earth, mapping and monitoring the state of crops and productivity; technologies of differentiated application of seeds, fertilizers and pesticides, differentiated pre-sowing soil treatment; technology of parallel driving and automatic control over the work and movement of technical equipment. Based on the results of the analysis, such directions of development and practical use of precision farming tech-nologies in Belarus have been identified, such as the implementation of domestic industrial developments; development of domestic software and hardware; expanding scientific research on precision farming technologies; generalization and dis-semination of scientific and production experience in the introduction of precision farming; development of government measures to support commodity producers, including farms, mastering precision farming technologies. The scientific novelty lies in the study of the problems of introducing precision farming technologies in Belarus, tak-ing into account world experience, as well as in developing priority areas for the further development of precision farming in Belarus. The practical significance lies in the need to introduce precision farming technologies in agricultural organizations of Belarus. It is advisable to continue the research in the direction of developing a methodology for assessing the degree of intro-duction of precision farming technologies, as well as a comprehensive analysis of the results of the practical use of these technologies at micro- and mesoscale levels. Keywords: agriculture, efficiency, sustainability, innovative technologies, precision farming, world experience, de-velopment directions in the Republic of Belarus.

‘Precision Farming’ or ‘Precision Agriculture’ aims at increasing productivity, decreasing production costs and minimizing the environmental impact of farming. In this context, the present study has been undertaken to understand the impact of precision farming on resource-poor regions and underprivileged farmers. Specifically, the study has looked into productivity, income, employment, and adoption behaviour of technology in agriculture. The study, conducted in the Dharmapuri district, has collected data on precision and non-precision farmings through the interview schedule during the year 2007. Sources of the productivity difference between the precision and conventional farmings have been identified by decomposing the productivity change. Financial impact of adoption has been studied through a two-stage econometric model. The first stage of the model consists of an adoption decision model that describes the factors which influence the likelihood of adopting precision farming. Results of first stage have provided input for the second stage of the model, which has been used to estimate the impact of precision farming on farm financial performance. The study has revealed that adoption of precision farming has led to 80 per cent increase in yield in tomato and 34 per cent in brinjal production. Increase in gross margin has been found as 165 and 67 per cent, respectively in tomato and brinjal farming. The contribution of technology for higher yield in precision farming has been 33.71 per cent and 20.48 per cent, respectively in tomato and brinjal production. The elasticity of 0.39 for the adoption in tomato and 0.28 in brinjal has indicated that as the probability of adoption increases by 10 per cent, net return increases by 39 per cent and 28 per cent in tomato and brinjal cultivation. Lack of finance and credit facilities have been identified as the major constrains in non-adoption of precision farming. The study has suggested that providing of subsidies for water-soluble fertilizers and pump-sets will increase adoption of precision farming.

Purpose Effectively transferring knowledge of digital innovations, such as precision agriculture, is critical for farmers to make adoption decisions to meet current and future farming challenges. Stronger knowledge flows from Extension systems to stakeholders benefit adoption of precision agriculture. Methods This study used a three-round Delphi technique with a panel consisting of 15 precision agriculture experts who are academic extension faculty at land-grant universities in the Southeastern United States to identify the key knowledge, skills, and abilities needed by Extension agents to facilitate effective precision agriculture interactions. Results A total of 13 key competencies were identified. The findings revealed that the majority category identified in the 13 key competencies was the skill component, followed by the knowledge component, indicating the need to expand Extension agents' skills and knowledge related to precision agriculture. Conclusion The top key competency of basic precision agriculture awareness underscores the importance of ensuring Extension agents are aware of precision agriculture applications in their region. While all items were considered relatively important to the panelists based on the rate data, sustainability awareness, and equipment operation skill competencies were ranked as a lower priority, indicating Extension agents do not necessarily need to understand how to use the precision agriculture technology or manage the data but should function as change agents who introduce farmers to precision agriculture and connect them with relevant experts. The findings inform future tailored professional development programs and educational resources for equipping Extension agents with the needed competencies and guide future assessment of precision agriculture competencies among Extension agents.

With the progress and application of information technology in agriculture, precision farming (PF) has been increasingly gaining attentions in worldwide. PF technology is a kind of agricultural management system on the basis of the variability of spatial and temporal of crop yield of small section and circumstance factor in the field. To measure timely and efficiently the spatial and temporal variant information that influences crop production is the key step for implementing PF. The current status of the acquisition information techniques of soil variability, the distribution of crop yield, diseases and insect pests and crop growth condition was analyzed, which showed that most of research focused on the acquisition and processing techniques of soil variability, most of the acquisition technique didn't realize the quick and real-time collection, and the single functional instrument was used. The development and research on the yield monitor system, the instrument for collecting timely soil parameter variability and integrating multifunctional information collection system will be the direction carried out in the future in China.

The study introduced foreign and domestic situation of wireless sensor network technology in precision agriculture in detail. Applications of wireless sensor network are universal in foreign precision agriculture, however in its beginning stage in domestic agriculture. The function of domestic system based on wireless sensor network is usually positioning measurement and processing of agriculture elements, which does not meet final requirement of precision agriculture. The study designed a universal wireless sensor network for M2M combined intelligent communication technology and big agriculture machinery. The key technologies of wireless sensor network for M2M include development plan of ISA SP100.11a, spectrum technology based on DSSS, network technology based on net routing and low power radio frequency design, which can meet the real-time character, reliability, robustness and low energy consumption requirement of wireless communication in precision fruits. Moreover, the study presented several typical applications in precision fruits (including farming machine, water-saving irrigation machine and picking machine). With development of precision fruits in breadth and depth, integration application of wireless sensor network would have a widen prospect in the future.

Precision agriculture (PA) technologies, such as GPS-guided tractors and unmanned aerial vehicles (UAVs), have increased farming efficiency, allowing precise input application and reducing waste. Precision agriculture enhances crop yield and quality, balancing economic and environmental interests. Simultaneously, hay production in agriculture, crucial for livestock during scarce pasture periods, benefits from these advancements. Its harvesting process, aimed at preserving nutritional value and preventing spoilage, enables large-scale storage for consistent livestock feeding. Additionally, hay production, adaptable to various climatic conditions and incorporating diverse grass and legume species, enhances soil health and aligns with sustainable agriculture principles. Previous research on using proximal and remote sensing for hay yield estimation has shown promise but faces several limitations. These include low accuracy in mixed species or low-density crops and in heterogeneous field conditions. Challenges also arise in selecting optimal data collection dates and dealing with ambient conditions like wind and dirt, which affect the accuracy of both proximal and remote sensing technologies. These limitations highlight the need for improved methods in hay yield estimation. This study aims to develop and evaluate hay yield estimation systems using remote and proximal sensing as an improvement over the conventional bale weight method. The focus is on enhancing spatial resolution for site-specific management, involving the use of LiDAR, ultrasonic sensors, and UAV-based remote sensing. It includes detailed analysis, data processing methodologies, predictive model evaluation, and key variable identification. The study is structured into chapters, each addressing a specific aspect of yield estimation, including preliminary remote sensing tests, machine learning applications using UAV imagery, and comparative analysis of proximal sensing technologies. All studies were conducted in a 35-ha field near Centralia, Missouri, involving the collection of ground-truth biomass data in a hay field having a mixture of timothy grass and red clover, proximal sensing data by ground based ultrasonic and LiDAR, and UAV-based remote sensing data. Ground-truth data was collected in 1-m2 quadrats and multiple descriptive statistics such as median, mean and quantile 90 percent were extracted for calibration and validation for all studies. The first study utilized various multispectral image vegetation indices (VIs) and regression models for biomass estimation. It evaluated the use of multispectral imaging systems, along with data collection and processing approaches. A multiple linear regression model was developed, using independent variables selected from candidate VIs and texture features, to predict hay biomass. Model performance was evaluated using measures like R2 and RMSE, and models including VIs and texture features provided R2 values from 0.31 to 0.68. Significant findings included correlations between VIs and dry mass and the effectiveness of texture features in improving model accuracy. While standard VIs showed substantial correlation with biomass, challenges like variable image resolution impacted accuracy. The second study used UAV-based remote sensing systems and machine learning models to estimate and map hay yield, employing a red-green-blue (RGB) camera for image acquisition. A random forest (RF) model was applied to estimate yield, after selecting predictive variables from VIs, texture features, color features, moisture content, and proportion of grass through recursive feature elimination (RFE). The highest accuracy model achieved R2 = 0.59, RMSE = 331 g/m^2, and MAE = 277 g/m^2 for wet hay mass estimation. This research provided insights into the selection of appropriate analysis methods for hay estimation using UAV imagery and showed the prospects for future research to apply these models to whole-field imagery for creating hay yield maps. Finally, the last study focused on comparing the effectiveness of ultrasonic and LiDAR sensors for estimating hay yield. The use of these sensors to measure plant height when mounted on a boom in front of a hay mower was evaluated, correlating these measurements with hay biomass. Data from the ultrasonic sensor, collected over a 3 m range and analyzed with machine learning models, provided the most accurate biomass estimates, with R2 values ranging from 0.31 to 0.52. Decision tree models were the most effective for use with data collected by these sensors. Results indicated that higher sensor data collection frequencies might improve prediction results.

Precision agriculture technologies (PATs) have revolutionized agriculture production and provide many benefits to farmers. Among these benefits is the ability to conduct experiments using PATs and collaborate with researchers in a process known as on‐farm precision experimentation (OFPE). OFPE is a citizen‐science approach that fosters relationships and knowledge‐sharing to address challenges of mutual interest. While the literature on precision agriculture is extensive, little research has addressed farmers’ willingness to conduct OFPE with researchers. Interviews with 11 Illinois farmers revealed high adoption rates of PATs. Interest in collaborating with researchers to conduct OFPE was mixed, with farmers identifying clear channels of communication as the most important factor in their decision to collaborate. While additional study is needed among the larger farming community, these initial insights may contribute to the larger effort to encourage greater collaboration between researchers and farmers to find real‐world solutions to agronomic, economic, and environmental challenges.Core Ideas Precision agriculture technologies (PATs) allow farmers to engage in on‐farm precision experimentation (OFPE). OFPE fosters farmer and researcher collaboration to help address agronomic challenges. This study provides initial insight into farmers’ perceptions of and interest in OFPE.

Smart agriculture is increasingly recognized as a digital transformative to achieving stage of food security and environmental sustainability, particularly through the digital technologies such as IoT, Ai, and remote sensing. However, this research and adoption remain concentrated in high income countries, with limited exploration of youth engagement in Indonesia. This study combines scientometric mapping of global innovative agriculture research with empirical field insights from 90 youth respondents in Indonesia to assess the alignment between academic trends and real-world adoption. Scientometric analysis revealed growing interest in precision agriculture and sustainability technologies but a lack of focus on youth and community-based innovation. Field findings indicated high awareness among Indonesian youth of the benefits of smart agriculture, especially in terms of productivity, environmental, and market access. The result shows that the need for inclusive strategies integrated youth perspective, build local capacity, and participate in rural development. This study contributes to academic and policy discussions on inclusive agricultural innovation ecosystems in developing regions.

To strengthen the agriculture sector, it is crucial to combine the efforts of industrialization (field mechanization and fertilizer production), technology (genome editing and manipulation), and the information sector (for the application of current technologies in precision agriculture). The challenge of modern sustainable agriculture is increasing agricultural output while using the least amount of resources and capital expenditure possible and considering the variables contributing to environmental damage. Different environmental factors adversely affect medicinal plant populations, leading to the extinction of these valuable medicinal species. These difficulties drew the attention of the international scientific community to farm sustainability and energy efficiency studies that put forth the idea of precision agriculture (site-specific crop management) in medicinal plants. It is a systems-based method that monitors and responds to changes in intra- and inter-field conditions for environmentally friendly and optimum crop output. Farming systems have significantly benefited from the visualization and morphological analysis of agricultural areas (both open fields and greenhouse experiments) using remote sensing technology, geographic information systems (GIS), crop scouting, variable rate technology (VRT), and Global Positioning System (GPS). These technologies form the backbone of the fourth agricultural technological revolution, Agriculture 4.0. This review concisely summarizes these innovative technologies' current use and potential future advancements in medicinal plants. The review is intended for researchers, professionals in medicinal plant cultivation, herbal medicine research, crop science, and related fields.

Against the backdrop of growing demand for rapid soil testing technologies in precision agriculture, this study proposes a detection method based on pyrolysis-electronic nose and machine olfaction signal analysis to achieve precise measurement of key soil nutrients. An electronic nose system comprising 10 metal oxide semiconductor gas sensors was constructed to collect response signals from 112 black soil samples undergoing pyrolysis at 400 °C. By extracting time-domain and frequency-domain features from sensor responses, an initial dataset of 180 features was constructed. A novel feature fusion method combining Pearson correlation coefficients (PCC) with recursive feature elimination cross-validation (RFECV) was proposed to optimize the feature space, enhance representational power, and select key sensitive features. In predicting soil organic matter (SOM), total nitrogen (TN), available potassium (AK), and available phosphorus (AP) content, we compared support vector machines (SVM), support vector machine-random forest models (SVM-RF), and particle swarm optimization-enhanced support vector machine-random forest models (PSO-SVM-RF). Results indicate that PSO-SVM-RF demonstrated optimal performance across all nutrient predictions, achieving a coefficient of determination (R2) of 0.94 for SOM and TN, with a performance-to-bias ratio (RPD) exceeding 3.8. For AK and AP, R2 improved to 0.78 and 0.74, respectively. Compared to the SVM model, the root mean square error (RMSE) decreased by 25.4% and 21.6% for AK and AP, respectively, with RPD values approaching the practical threshold of 2.0. This study validated the feasibility and application potential of combining electronic nose technology with a time-frequency domain feature fusion strategy for precise quantitative analysis of soil nutrients, providing a new approach for soil fertility assessment in precision agriculture.