Intelligent Crop Recommendation System using Machine Learning

Producers in industrialized countries have been inundated by ideas and information about precision agriculture (PA) and how new site-specific management (SSM) technologies will revolutionize their farm operations. Conjuring up 'Star Wars' imagery, farmers and their computerized machinery communicate with satellites while speeding up and down the information highway. The farm press has hailed the advent of these technologies as a win-win situation, with higher farm profits and improved environmental quality. Certainly the potential is there for greater economic returns and better environmental stewardship. But what exactly is precision agriculture, who is applying it, and where? Is the technology only relevant for developed countries and are there implications for markets? What is the likelihood that environmental benefits will be realized? This paper addresses these questions by drawing on literature, data and expert opinion.

The implementation of image recognition technology can significantly enhance the levels of automation and intelligence in smart agriculture. However, most researchers focused on its applications in medical imaging, industry, and transportation, while fewer focused on smart agriculture. Based on this, this study aims to contribute to the comprehensive understanding of the application of image recognition technology in smart agriculture by investigating the scientific literature related to this technology in the last few years. We discussed and analyzed the applications of plant disease and pest detection, crop species identification, crop yield prediction, and quality assessment. Then, we made a brief introduction to its applications in soil testing and nutrient management, as well as in agricultural machinery operation quality assessment and agricultural product grading. At last, the challenges and the emerging trends of image recognition technology were summarized. The results indicated that the models used in image recognition technology face challenges such as limited generalization, real-time processing, and insufficient dataset diversity. Transfer learning and green Artificial Intelligence (AI) offer promising solutions to these issues by reducing the reliance on large datasets and minimizing computational resource consumption. Advanced technologies like transformers further enhance the adaptability and accuracy of image recognition in smart agriculture. This comprehensive review provides valuable information on the current state of image recognition technology in smart agriculture and prospective future opportunities.

Agriculture and farming are the most important and basic industries that are very important to humanity and generate a considerable portion of any nation's GDP. For good agricultural and farming management, technological advancements and support are required. Smart agriculture (or) farming is a set of approaches that uses a variety of current information and communication technology to improve the production and quality of agricultural products with minimum human involvement and at a lower cost. Smart farming is mostly based on IoT technology, since there is a need to continually monitor numerous aspects in the agricultural field, such as water level, light, soil characteristics, plant development, and so on. Machine learning algorithms are used in smart farming to increase production and reduce the risk of crop damage. Data analytics has been shown through extensive study to improve the accuracy and predictability of smart agricultural systems. Data analytics is utilised in agricultural fields to make decisions and recommend acceptable crops for production. This study provides a comprehensive overview of the different methods and structures utilised in smart farming. It also provides a thorough analysis of different designs and recommends appropriate answers to today's smart farming problems.

The advancement of technology has led to the introduction of smart technologies in various sectors, including farming, forming what is now known as smart farming. Smart farming involves using smart technologies to perform monitoring and automation tasks traditionally performed by humans. However, despite these advancements, farmers still need help adopting smart farming techniques in crop farming. Therefore, this chapter presents a framework designed to guide the implementation of smart technologies in crop farming activities. This framework employs the concept of artificial intelligence of things (IoT), which utilizes artificial intelligence (AI) for decision-making and data analysis based on data from internet of things (IoT) devices. The framework is structured around five modules: the sensor module, the artificial intelligence module, the automation module, the network module, and the processing module. These modules enable effective monitoring of factors essential for optimal crop growth and performing automation based on monitoring activities.

The role of emerging technologies has gained huge attention in recent years. With the advancements in sensor technologies, internet of things (IoT), and remote sensing, large amounts of data are being generated in agriculture. However, making sense of this data and extracting valuable information offers major/ important challenges. Data visualization techniques offer a solution by providing intuitive and interactive ways to represent complex agricultural data. Furthermore, big data analytics techniques enable the extraction of valuable information and patterns from large datasets, contributing to informed decision-making in smart agriculture. This chapter discusses the importance/benefits of data visualization and big data analytics in smart agriculture. It discusses various data sources in agriculture, including sensor technologies, IoT, and remote sensing. This work also provides an overview of the tools and technologies available for data visualization in smart agriculture, including data visualization software, etc., in detail.

Precision agriculture is about viewing and treating the agricultural process as a system and incorporating information available from all its parts to improve its performance. In order to do so, new enabling processes, tools, and technologies had to be developed to enable the observation and measurement of important variables, facilitate the study and assessment of these variables to extract relevant information and knowledge, and use this knowledge to control the agricultural process and its inputs/outputs. This book is a compilation of contributions, breakthroughs, and impactful research done by leading female researchers and scholars from various fields and from around the world toward making precision agriculture a reality. These researchers are creating new technological advances that are revolutionizing agriculture and providing innovative solutions to some of today’s most challenging global food problems, paving the way for a smarter, more precise, more efficient, and more profitable agriculture for the twenty-first century. This is the only known book focused on advances in precision agriculture for both land and livestock, led by women researchers and scholars, hence providing a unique woman’s perspective in a field primarily dominated by men. This chapter presents a holistic overview of the field, highlighting relevant technologies, decision-making strategies, practices, applications, economics, opportunities, and challenges for both land and livestock applications.

The use of contemporary technologies in agriculture has transformed conventional farming practices, resulting in considerable increases in productivity, resource efficiency, and sustainability. This analysis looks at the uses of significant technology advancements such precision agriculture, smart irrigation systems, genetically modified organisms (GMOs), automation, robotics, and data analytics. These technologies have the potential to alter agriculture by increasing crop yields, optimizing resource utilization, and minimizing environmental impact. However, their broad use presents significant issues, notably for agricultural laborers who suffer job displacement and the need to up skill. While precision agriculture and automation can lessen the need for human labor, they require a workforce that is skilled at operating and maintaining sophisticated machinery and software. While GMOs increase yields and resilience, they also pose ethical and economic problems that may have an impact on labour relations. Furthermore, the growing reliance on data analytics and artificial intelligence (AI) in decision-making processes may result in a digital gap, marginalising individuals who lack access to or knowledge of these technologies. This paper gives a thorough examination of the advantages and disadvantages of modern agricultural technologies, with a special emphasis on the implications for workers. It emphasises the importance of policies that help displaced workers’ retraining and education, as well as the equitable sharing of technical breakthroughs to guarantee that all agricultural stakeholders benefit from these innovations. The findings underscore the significance of balancing technological advancement with the social and economic well-being of agricultural communities in order to ensure the sector’s long-term and inclusive growth. The study highlights key innovations, such as precision agriculture, automation, and biotechnology, that have revolutionized agricultural practices. While these technologies enhance productivity and sustainability, they also pose challenges, including the displacement of labor and the need for workforce reskilling. The discussion addresses the potential benefits, such as increased efficiency and reduced environmental impact, alongside the challenges of equitable access and socio-economic disparities. Practical solutions, including training programs and policies for inclusive technology adoption, are proposed to mitigate adverse effects and ensure a balanced transition for agricultural communities.

Precision agriculture technologies (PATs) are reshaping modern crop production by promoting efficient and sustainable farming practices. Core components of precision agriculture include remote sensing, GPS-enabled machinery, variable rate technology (VRT), and Internet of Things (IoT)-based systems. Remote sensing tools and unmanned aerial vehicles provide high-resolution data that support accurate assessment of crop health, soil variability, and pest incidence. GPS-guided equipment improves the precision of field operations such as sowing, fertilizer application, and harvesting, leading to reduced input losses and higher operational efficiency. VRT allows site-specific application of water, fertilizers, and pesticides according to crop demand and real-time field conditions, thereby minimizing excessive input use, nutrient runoff, and greenhouse gas emissions. The adoption of precision agriculture significantly enhances environmental sustainability by conserving water, lowering chemical inputs, and improving soil health. By reducing the ecological footprint of farming while increasing yields and profitability, PATs offer a viable solution for meeting the growing global food demand and ensuring long-term agricultural and environmental sustainability. Precision agriculture integrates advanced technologies to enhance farm productivity, efficiency, and sustainability. Precision farming, defined by the strategic application of data analytics and advanced technologies, has emerged as a transformative approach for achieving sustainable agriculture. precision agriculture provides an effective pathway for building sustainable and resilient agricultural systems by improving resource-use efficiency, while simultaneously supporting food security and environmental sustainability.

The agriculture 4.0 revolution is an opportunity for farmers to meet the challenges in food production. It has become necessary to adopt a set of agricultural practices based on advanced technologies following the agriculture 4.0 revolution. This latter enables the creation of added value by combining innovative technologies: precision agriculture, information and communication technology, robotics, and Big Data. As an enterprise, a connected farm is also highly sensitive to strategic changes like organizational changes, changes in objectives, modified variety, new business objects, processes, etc. To strategically control its information system, we propose a metamodeling approach based on the ISO/IS 19440 enterprise meta-model, where we added some new constructs relating to new advanced digital technologies for Smart and Connected agriculture.

To support the current population growth, modern agriculture must increase food production while reducing the use of water and other resources required for crop cultivation. Precision agriculture (PA) aims to achieve these via a variety of methods that include site-specific plant selection, variable rate irrigation and fertilisation, as well as site-specific pesticide and herbicide application. To determine the plant performance and health that drive such precision PA practices, PA solutions currently collect and analyse data from cameras and multispectral sensors. Technological advancements in the Internet of Things (IoT) and in the development of Unmanned Aerial Vehicles (UAV) in recent years have provided potential solutions for automating image acquisition and analysis that can advance such PA practices. This paper proposes 1) devising plant models from RGB and multi-spectral data, 2) using such models to guide the above PA practices. More specifically, the paper explores monitoring plants at different health and life cycle stages from fully green to completely dry and capturing related RGB and multi-spectral data in a controlled environment. These data are then analysed to create a model for each plant variety, which we refer to as the plant profile, that captures the combined colour and light reflectance of the plant over its life cycle and related health stages. The paper proposes using such plant variety profiles to determine the performance and health of the plants across entire crops. Finally, the paper discusses how UAVs and IoT can be used to automatically capture and analyse the images and multi-spectral data for advancing PA.

Two mail surveys were carried out in Denmark and the Eastern Corn Belt, USA in 2002. Questionnaires were sent to 580 farmers who had used precision agriculture (PA) and 198 responses were received. The surveys focused on the current status of use of PA in both countries, including: PA practices, equipment and software, Internet and e-mail use, information sources for PA, satisfaction level from service providers, data handling, interpretation, storage and ownership, value of data for decision making, changes in management practices, desired information and services, and the next planned step in the practice of PA. The survey results showed more similarities in practicing PA between the two countries than differences. Time requirement and high cost of data handling were cited as the main problems. Survey respondents found soil maps to be more valuable than yield maps in management decisions. About 80% of the respondents would like to store the PA data themselves. The majority of the respondents indicated that they have changed their management practices due to PA, but not substantially. Some 90 of the respondents used the Internet and e-mail for agricultural purposes, but only a small number for PA websites.

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.

Advancements in technology have significantly transformed the agriculture sector, with the Internet of Things (IoT) emerging as a key player in enhancing productivity and efficiency. By integrating IoT technologies into smart farming practices, it is possible to monitor and control critical environmental factors such as light, temperature, humidity, and soil conditions in real-time. This approach not only automates irrigation systems, reducing the need for human intervention but also optimizes resource use, lowers operational costs, and improves overall farm management. Smart farming facilitates sustainable agricultural practices by maintaining soil quality, conserving water, enhancing land biodiversity, and promoting a healthier environment. As the global demand for food rises due to population growth, IoT-driven smart farming offers a viable solution for increasing farm productivity and profitability while supporting sustainable agricultural development.

Precision agriculture (PA) is an approach that uses information technology (IT) to ensure agricultural production and increases profitability. The Internet of Things (IoT) has important applications and the potential to be used in agriculture. For instance, it can manage soil, water, and weather information for the farmers. Moreover, it can be used to increase the productivity of crops, farmers looking for real-time data, and strive to ensure enough agricultural inputs to meet the crop requirements while taking advantage of the data already present on the farm. The proposed study will enable farmers to obtain necessary information about their land and, hence, decide how to manage the limited water resources, soil nutrients, and weather forecasting on their farms. Artificial intelligence and machine learning techniques will be used to learn from the historical data and show any pattern that might increase the farm’s efficiency and productivity. To ensure the benefit of smart farming, it is of utmost importance to commit field-specific research, demonstrate trials, and bring in governmental policies.The study refers to the challenges of using IoT in smart farms, such as strong internet connection without interruption and provide fast transmission speeds. However, Uganda has already taken advantage of IoT technology, applied it widely, and has rapidly strengthened the capability. This system presents obvious benefits to agriculture and can reduce costs and increase the yield up to 50% because Smart Farms is the leading solution for increasing agricultural production.

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.