Precision agriculture: global prospects and environmental implications.

The paper reviews the development of practices for the adjustment of soil management and in particular fertiliser inputs to rubber to cater for variations within an area based on planting material (clone), soil and its variations, tapping system, yield levels and other factors. The practice which was folly evolved by mid 1970's was termed "discriminatory fertiliser use" and seems to fit into the newly emerging terminologies of "precision agriculture" and or "site specific management". Keywords: Discriminatory fertiliser use, rubber, soil management, precision agriculture.

The classical agricultural natural process requires huge manual try and dispatch on farm subject field which can be replaced using mechanization or smart farming. Agribot is the excellent example of computerized or smart effectuation of the agricultural devices which can oeuvre in autonomous trend with live monitoring from a remote mastery locating. This manuscript contains an automaton robot to carry out various applications because automaton is a mechanical, artificial cistron and is usually an electromechanical (Mechatronics) system. By using control ascendency, we operate the whole golem. The Key mapping associated with farming is implemented by the Agribots. The main focus is addressed by the Agribots includes different farm operation along with their CAD model of individual components with design specification which is required for the balanced movement of Agribot. This work target on the design, developing and the fabrication of the robot which can dig the soil, sow the seeds with the wrack quill, gearing train mechanism, sprinkle to fertilizer, gage cutting tool to cut the green goddess. The robotic system is an electromechanical and artificial agent which is steered by a DC motor which has four wheels. The machine can be controlled remotely or with a solar panel which is used to charge DC battery or with the AC Power supply. The microcontroller is used to control and monitor the process of system motion of vehicle with the help of DC motor. This research underlines the assorted linear perspective of Agricultural Golem with the implementation of linear perspective throughout the globe on assorted parameters and functions involved. Keywords: Agribot, arduino uno, automated farming, smart farming introduction Cite this Article Patil Nagesh Umakant, Sandeep Tiwari. An Effectual Analysis of Agribot Technology and Implementation in Global Perspectives for Energy Saving. Recent Trends in Sensor Research & Technology . 2018; 5(2): 20–32p.

It is widely observed that the crop yields vary significantly within the field, primarily because of the intra-field variations in soil, landscape, water, and nutrient conditions. That variability forms the foundation for site-specific management in precision agriculture. However, thus far the studies about the within-field yield variability are mostly based on one or several sample fields, while the variability distribution for a real world farm or a larger region have received less investigations. In addition, due to the small number of sample fields, the determining factors of yield variability have not been fully examined. This study fill this research gap by collecting and analyzing high resolution yield monitor data of over a hundred fields from a large size farm in the Mississippi Delta. It quantitatively describes the extent of the within-field yield variability over a large scale area, and empirically examines the relationship between the yield variability and factors such as soil types, landscapes, weather conditions, management inputs, etc. A spatial econometric model is utilized to account for other unobserved but spatially correlated yield-impacting soil properties. This study contributes to exploring the method of analyzing GIS data from farming and turning the data into site-specific management decisions.

The relentless growth of the global population, compounded by the adversities posed by climate change, has triggered an unprecedented demand for sustenance. This burgeoning need places an imperative on the agricultural sector to adopt practices that are both sustainable and efficient. Traditional farming methods, marked by inefficiencies and environmental ramifications, prove inadequate in meeting this escalating demand. Inresponse, precision farming emerges as a transformative solution, centering on data-driven decision-making and the optimization of resources. This groundbreaking whitepaper meticulously navigates the intersection of precision farming and augmented reality (AR) technology, showcasing a paradigm shift in agriculturalpractices. AR, a revolutionary tool overlaying digital insights onto the physical world,empowers farmers with real-time information on soil conditions, crop health, and moisture Levels. Harnessing AR's capabilities, farmers gain unprecedented control over irrigation and fertilizer applications, minimizing waste while maximizing yields. The paper dives deep into the modern challenges of agriculture, articulates the substantial benefits of precision farming, and underscores AR's transformative potential in surmounting these challenges. Precision farming, elucidated as the fusion of technology to gather and analyze data on parameters like soil moisture and weather conditions, is detailed with utmost clarity. In contrast to the antiquated one-size-fits-all approach of traditional farming, precision farming, facilitated by real-time data and analytics, enables bespoke crop management. This optimization minimizes resource usage, curtails waste, and augments productivity to Unparalleled levels. The whitepaper seamlessly dissects precision farming into two indispensable components:data capture and data visualization. The Internet of Things (IoT) emerges as the linchpin for data capture, employing sensors that collect real-time data transmitted to a centralized platform for astute analysis. Concurrently, AR, the second component, emerges as the pivotal interface, empowering farmers to seamlessly visualize and interpret data for judicious decision-making. In the current corporate panorama, stalwarts like Augmenta and Grow Glide, coupled with potential contributors like Rams Creative Technologies Pvt. Ltd. and emerging entities likeInfosys Limited, stand as vanguards propelling technological advancements to bolster precision farming. However, the potential for widespread AR adoption in precision farming faces impediments. Concerns regarding the safety and health risks associated with AR hardware, coupled with formidable development costs, and a prevailing lack of awareness pose substantial challenges.

Smart agriculture integrates key information communication technologies with sensing technologies to provide effective and cost-efficient agricultural services. Smart agriculture leverages a wide range of advanced technologies, such as wireless sensor networks, Internet of Things, robotics, agricultural bots, drones, artificial intelligence, and cloud computing. The adoption of these technologies in smart agriculture enables all stakeholders in the agricultural sector to develop better managerial decisions to get more yield. We differentiate between traditional agriculture and smart agriculture based on the deployment architectures along with a focus on the various processing stages in smart agriculture. We present a comprehensive review of various types of sensors that are playing a vital role in enabling smart agriculture. We also review the integration of various sensing technologies with emerging technologies and computing infrastructures to make agriculture smarter. Finally, we discuss open research challenges that must be addressed to improve the adoption and deployment of smart agriculture in the future.

This report contains estimates of the number of new farms and the number of exits from farming in North Carolina for various periods of time between 1978 and 1992. Entrance and exit rates are based on the latest information provided by the Census of Agriculture about the total number of farms in each county as well as the number of years operators reported managing a given farm. Comparing characteristics of new farming operations with those that have existed for a longer period of time provides insights about some elements of the evolving structure of agricultural production in North Carolina. The total number of farms in North Carolina decreased more than 40 percent between 1974 and 1992. The rate of turnover in farming operations between 1987 and 1992 was not as great, however, as during the previous decade. Substantial variation in the rates of change in farm numbers and the ratios of entrants to exits among counties indicate that factors influencing the number and turnover in farming operations have not operated uniformly throughout the state. Regional differences in farm number changes and total acres of farmland indicate more consolidation of farming operations in the eastern part of North Carolina relative to other parts of the state. Much of the decrease in the number of farms in North Carolina has been concentrated among smaller farm size categories including those up to 220 acres in size. Nevertheless, nearly 60 percent of all new farms in North Carolina between 1978 and 1992 had less than 50 acres of farmland. Many new farms as well as those that have been in operation for longer periods of time appear to be part-time operations in that only a little over half of all farm operators in 1992 considered farming to be their principal occupation. An increasing proportion of farm operators, especially among those reporting farming as their principal occupation, are 65 years of age or older. This suggests increasing rates of management turnover in farming operations are likely. Considerably larger proportions of new farms, relative to farms that have been in operation for longer periods of time, are tenants. On the other hand, the largest proportion of continuing farming operations, especially among those with annual sales of farm products of $10,000 or more, were part owners. Consistent with the continuing shift in composition of agricultural production in the state; an increasing share of new and continuing farms in North Carolina are livestock oriented. Nevertheless more than 3300, or 42 percent of all farms that began operations between 1987 and 1992 had 50 percent or more of their total receipts from cash grain or other field crops.

Nanotechnology is a novel technique that has potential applications in multiple sectors and can easily transform the conventional farming system into modern agriculture in an eco-friendly, sustainable way. It is the modern farming strategy that can fulfill the higher demand for foods among the world’s population along with overcoming the inherent limitations in traditional agriculture like soil disturbance, imbalance in biodiversity, and environmental risk. Metal nanoparticles (NPs) are used as nano-pesticides, nano-fertilizers, nano-sensors, and nano-priming agents that can be synthesized through physical, chemical, or biological methods. They also help to mitigate issues such as high production costs, excessive pesticide and fertilizer requirements, soil depletion, and various biotic and abiotic challenges. Additionally, agroecosystems can achieve enhanced nutrient utilization efficiency, more efficient pest control, and less environmental impact with the application of metal NPs. Nanoencapsulation allows chemical pesticides and fertilizers to be precisely dosed by deliberately releasing their ingredients incrementally. Metal NPs, used as biosensors and seed-priming materials, can be integrated with precision agriculture to enable precise resource management. Aside from that, they can contribute to seed germination even in adverse conditions. A variety of important information from different research findings on metal NPs, their characteristics, synthesis process, and their roles in precision and sustainable agriculture are included in this article. This article also covers future perspectives for metal NPs based on these impacts. This study discusses the benefits of metal NPs on plant growth and development, as well as their effects on precision agriculture for crop production.

The Republic of Turkey occupies a unique geographical and cultural position at the crossroads of Europe and Asia. Turkey has a total land area of 78 million hectares and a population is about 70 m illion. Approximately one third of the land is arable, and 26 percent of the population lives in rural areas. Since the formation of the modern state of Turkey in 1923, th e economy has been grown rapidly. However, economic growth slowed in the mid-1990s in the wake of a fis cal and monetary crisis. There are wide disparities in income between the more prosperous, industrialized parts o f the country in the west and south-west, and the l ess developed provinces in the rest of Turkey. In 2007, gross national income was USD 5500 per capita. Turkey's economy mostly depends on agricultural sector rather than industry and services. Even Turkish industry is commonly agricultural manufacturing, so economic development of Turkey will pick up speed whenever agricultural development exists in Turkey. Agricultural production has to be raised at least double times in Turkey because of the fast growing populat ion In order to feed fast growing population of Tur key, in recent years, modern agricultural technologies, irr igation systems, environment friendly fertilizers, quality seeds, and agricultural credits has being applied in Turke y's agriculture. All those inputs need to be used b y knowledgeable farmers in order to get sufficient re sults. Turkey's farms have been obtained a family business and small scale. Day to day, youths are not intere sting in agriculture in Turkey as well as like developing co untries. Almost 8 millions employee work in agricul ture and about 60% of them are female in Turkey. In agricult ure, women have been striving the agricultural acti vities and besides their house-works. Women are drawers in agricultural development for Turkey. Whenever the women get good education, they could be affecting her hus band and children. They can also be very sensitive about their agricultural environmental production. The overall objective of this study was to examine factors influencing accessibility of women to Agric ultural Extension Services in Konya. The second purpose of the study was to investigate specific needs and int erests of women.

Precision agriculture leverages data science and technology to optimize farming practices, enhance crop yields, and promote sustainability. This review examines how data-driven approaches are revolutionizing agriculture through improved decision-making, resource efficiency, and environmental stewardship. Key technologies enabling precision agriculture are discussed, including remote sensing, IoT sensors, AI/machine learning, and farm management information systems. The paper explores applications such as variable rate technology, yield mapping, and predictive analytics. Challenges related to data management, interoperability, and adoption barriers are analyzed. The review concludes that precision agriculture, powered by data science, offers significant potential to address food security and sustainability challenges, but requires continued research and interdisciplinary collaboration to realize its full benefits.

Many approaches have been proposed over the last two decades for managing the spatial variation of soil and crops. In this chapter we discuss the importance of quantifying and managing spatial variation in crop production fields to implement site-specific crop management. We outline the challenges that soil and crop scientists have addressed since the inception of precision agriculture (PA) in terms of managing soil spatial variation, and the development of simple, stable and inexpensive techniques for quantifying and managing it with tools such as site-specific management zones. This chapter summarizes and cites the work of several scientists who have worked in the area of development and evaluation of site-specific management zones from around the world. Geostatistics is being applied increasingly in PA because of the need for accurate maps on which to base site-specific management. For soil and crop properties that require costly sampling and analysis, there are often insufficient data for geostatistical analyses and this chapter shows how management zones can provide an interim solution to more comprehensive site-specific management. Physical and chemical soil properties have been the most widely used properties for delineating management zones, however, intensive data from remote and proximal sensors are being used increasingly. The case study describes methods of delineating and evaluating management zones.

Nanotechnology is science of manipulating materials at nano-scale. Among the latest technological advancements, nanotechnology occupies a central position. It has many applications in all stages of production, processing, storing, packaging and transport of agricultural products. The reduced use of herbicides andpesticides with increased efficiency, controlled release and targeted delivery will lead to precision farming. Improvement of crops in agriculture is a continuous process. Nano-herbicides are being developed to address the problems in perennial weed management and exhausting weed seed bank. Remediation of environmental contamination of the industrial waste and agricultural chemicals like pesticides and herbicide residues are possible through metal nanoparticles. Details of possibilities and concepts of application of nanotechnology in the weed management and results obtained already in these areas are reviewed in this paper. Keywords: Nanotechnology, biosensor, nano-herbicides, nano-polymer, nano-pesticides Cite this Article Ambreesh Singh Yadav and D S Srivastava. Application of Nano-Technology in Weed Management: A review. Research & Reviews : Journal of Crop science and Technology. 2015; 4(2): 21–23p.

Internet of things (IoT) is a network in which all physical objects are connected to the internet through network devices or routers and exchange data. IoT allows objects to be controlled remotely across existing network infrastructure. It plays a major role in precision agriculture. Using the sensors we can monitor a specific crop and provide the inputs based on their needs. Sensors in fields measure the moisture content and temperature of the soil and surrounding air. Satellites and robotic drones provide farmers with real-time images of individual plants. Information from those images can be processed and integrated with sensor and other data to yield guidance for immediate and future decisions. Precision agriculture is also called site-specific crop management. The goal of precision agriculture is to ensure profitability, sustainability and protection of the environment. This study provides useful information about the IoT sensors and precision agriculture and how it will be useful to farmers to increase their production.

An approach for precision farming under pivot irrigation system using remote sensing and GIS techniques

Landowner characteristics influence his/her willingness to change landuse practices to provide more or less environmental benefits. However, most studies of agricultural/environmental polices identify landowners as homogenous. And, the primary cause of failure of many environmental and other polices is the lack of knowledge on how humans may respond to polices based on changes in their behavior (Stern, 1993). From socioeconomic theory and empirical research, landowners can be identified as individuals who make agricultural landuse decisions independently based on their objectives. Identifying possible classes of landowners, assessing how each would potentially respond to policy alternatives, and the resulting pattern of land uses in a watershed or a riparian corridor would be very useful to policy makers as they evaluated alternatives. Agricultural landscapes are important producers of ecosystem services. The mix of ecosystem services and commodity outputs of an agricultural landscape depends on the spatial pattern of land uses emerging from individual land use decisions. However, many empirical studies show that the production of ecosystem services from agricultural landscapes is declining. This is consistent with research conducted over the last few decades showing there is a narrow range of social circumstances under which landowners are willing to make investments in the present to achieve public benefits in the future through investing in natural capital resulting in public goods which are frequently produced as ecosystem services. In this study an agent-based model within a watershed planning context is used to analyze the tradeoffs involved in producing a number of ecosystem services and agricultural commodities given price and policy scenarios while assuming three different types of agents in terms of their goals. The agents represent landowners who have been divided into a number of different groups based on their goals and the size of their farm operations. The multi-agent-based model is developed using a heuristic search and optimization technique called genetic algorithm (GA) (Holland), which belongs to a broader class of evolutionary algorithms. GAs exhibit three properties (1) they start with a population of solution, (2) they explore the solution space through recombination and mutation and (3) they evaluate individual solutions based on their appropriate fitness value(s), for example given profit maximizing agents this would be gross margin. A GA is a heuristic stochastic search and optimization method, which works by mimicking the evolutionary principles and chromosomal processing in natural genetics. The three economic agents that are modeled are based on variations in their objective functions and constraints. This study will help in identifying the tradeoffs associated with various agents in the provision of ecosystem services and agricultural commodities. The agent model developed here will help policy and decision maker identify the various agents within the watershed and assess various policy options based on that information. The study will also help to understand the interaction and feedback between the agents and their environment associated with various policy initiatives. The results of the study indicate that the agent model correctly predicts the actual landuse landcover map by 75 percent. Key words: Multifunctional agriculture, Agent based modeling, Genetic Algorithm.

Sustainability in operations is a key consideration when discussing aquaculture’s current and future role in providing food and increasing coastal and rural employment and incomes, among other social benefits. An important problem from the economics point of view is how the economics point of view is how the externalities generated by aquaculture and those that the industry suffers are internalized. This paper extends the analysis reported in Martinez-Cordero and leung (2004) for a group of semi-intensive shrimp farms in Mexico. Modifications to the traditional Total Factor Productivity (TFP) and Technical Efficiency (TE) indicators are carried out in order to incorporate in the evaluation the environmental effects of aquacultural activities. In a framework of sustainable operations and development, these indicators (called environmentally-adjusted Total Factor Productivity EATFP and environmentally-adjusted Technical Efficiency EATE) allow for a better assessment of aquaculture activities, where enterprises are evaluated not only for obtaining the target product but also for how successfully farms are in generating the minimum among to undesirable outputs (wastes or pollutants). In this paper the years 1994, 1996-1998 and 2001-2003 are analyzed using an input distance function, and the environmental effects evaluated are total Nitrogen (N) and Phosphorous (P) discharges in farm’s effluents, calculated using mass balances for N and P reported for semi-intensive shrimp farms in Mexico. The results show that in all years EATE and EATFP were lower than the traditional TE and TFP scores. In the first period of evaluation (1994, 1996-1998) the TE and TFP trend is opposite to yields. As expected, years following diseases outbreaks result in a drop in all the economic indicators, but the fall is bigger in 2001 compared to 1996. In the second period (2001-2003) and despite drastic reductions in annual yields, productivity and efficiency don’t fall in the same proportion, meaning that both private and governmental efforts to assure that shrimp farming operations are carried out more efficiently, with higher productivity, are producing results, even though the fact that production continues being impacted by viral disease. For example, currently several Good Management Practices (GMPs) are already implemented or in the process of being adopted in semi-intensive farms. Two of the most important GMPs are present in the operation and management of the farms during 2001-2003: reduced water exchange rates and more controlled feeding strategies. Both have a direct effect on the discharges of shrimp farms to the environment and this study shows that EATFP and EATE properly capture these externalities. Farms that produce higher yields but also have a reduced impact on the environment (N and P discharges) achieve higher values for these economic indicators.