Machine Learning In Agriculture Research Articles

The Impact of Machine Learning in Agriculture: Revolutionizing Farming Practices

Machine learning (ML) and big data analytics have emerged as transformative technologies in the agricultural sector, offering innovative solutions to enhance productivity, sustainability, and profitability. This abstract provides an overview of how ML and big data are revolutionizing traditional farming practices by leveraging advanced algorithms and data analytics. It discusses the potential of ML and big data in optimizing crop yield and quality, promoting sustainable practices, enabling precision agriculture, and addressing crop diseases through advanced predictive modelling. The abstract emphasizes the potential of ML and big data to reshape the future of agriculture, fostering resilience and prosperity for farmers and stakeholders worldwide. The application of machine learning in the field of agriculture and its correlation with weather patterns delves into various ways in which machine learning techniques can be used to analyze and predict the impact of weather conditions on agricultural productivity. The paper discusses specific machine learning algorithms and models that have been successfully applied in this domain, providing insights into the potential benefits and challenges of integrating machine learning into agricultural practices.

Intelligence Farming Using Machine Learning

Abstract: This research paper delves into the revolutionary role of machine learning in contemporary agriculture, a concept termed as "Intelligence Farming." The study encompasses the application of data in farming, including data related to weather conditions, soil quality, and crop health, and how machine learning contributes to efficient resource distribution and crop management. The paper underscores the significance of predictive yield forecasting, precision farming, and early detection of crop diseases, all facilitated by machine learning. Furthermore, it discusses the socio-economic and environmental outcomes of implementing this technology, such as enhanced productivity and sustainability. In conclusion, the paper strongly recommends the incorporation of machine learning in agricultural decision-making processes, underlining its critical role in the current era of data-driven decision-making.

Roles of Iot, big data and machine learning in precision agriculture: a systematic review

Precision agriculture utilizes information technologies to efficiently manage water and soil resources in agriculture. This technological revolution has been sparked by the big data analytics, machine learning and Internet of Things (IoT)in numerous industries. This game-changing technology has influence on houses, grids, smart cities, and health. Machine learning and data-intensive decision-making have been made possible, which has created new possibilities for efficiency and innovation in various fields. In several fields, the convergence of IoT, big data analytics, and machine learning has opened the way for cutting-edge solutions and enhanced results.The application of machine learning in the agricultural sector can increase production quantity and quality to meet increasing food demand. These advancements are transforming current agrarian approaches and generating new opportunities, despite some limitations. This paper presents a systematic appraisal of the role of IoT, big data analytics, and machine learning in precision agriculture, highlighting the potential benefits and challenges in this rapidly evolving field.

Building Trust in AI Farming Tools

AbstractPrecision agriculture tools like decision support systems increasingly use machine‐learning algorithms and other types of artificial intelligence (AI) to analyze large quantities of agricultural data and provide recommendations to producers and crop advisers. However, several barriers threaten adoption of these tools. Three papers in the recent Agronomy Journal special section, “Machine Learning in Agriculture,” explore this phenomenon and offer solutions and opportunities for building trust in these technologies.

Leveraging Machine Learning to Improve Soil Greenhouse Gas Predictions

AbstractAgricultural soils are large sources of greenhouse gas emissions, particularly the potent nitrous oxide (N2O). While N2O emissions can be predicted by traditional biogeochemical models, these forecasts contain a lot of uncertainty. New efforts, including research (https://doi.org/10.1002/agj2.21185) published in the recent Agronomy Journal special section “Machine Learning in Agriculture,” use machine learning to improve N2O flux predictions and guide management interventions.

IMPLICATIONS OF NEURAL NETWORK AS A DECISION-MAKING TOOL IN MANAGING KAZAKHSTAN’S AGRICULTURAL ECONOMY

This study investigates the application of Artificial Neural Networks (ANN) in forecasting agricultural yields in Kazakhstan, highlighting its implications for economic management and policy-making. Utilizing data from the Bureau of National Statistics of the Republic of Kazakhstan (2000-2023), the research develops two ANN models using the Neural Net Fitting library in MATLAB. The first model predicts the total gross yield of main agricultural crops, while the second forecasts the share of individual crops, including cereals, oilseeds, potatoes, vegetables, melons, and sugar beets. The models demonstrate high accuracy, with the total gross yield model achieving an R-squared value of 0.98 and the individual crop model showing an R value of 0.99375. These results indicate a strong predictive capability, essential for practical agricultural and economic planning. The study extends previous research by incorporating a comprehensive range of climatic and agrochemical data, enhancing the precision of yield predictions. The findings have significant implications for Kazakhstan's economy. Accurate yield predictions can optimize agricultural planning, contribute to food security, and inform policy decisions. The successful application of ANN models showcases the potential of AI and machine learning in agriculture, suggesting a pathway towards more efficient, sustainable farming practices and improved quality management systems.

IoT-Based Agriculture and Smart Farming: Machine Learning Applications: A Commentary

In-depth analysis of the most recent developments in IoT-based agriculture and smart farming is provided in this article with a specific focus on the integration of machine learning applications. With the advent of Internet of Things (IoT) technology, conventional agricultural methods have been transformed by the ability to gather real-time data from numerous sensors and devices. The commentary highlights the pivotal role of machine learning in harnessing the vast amounts of data generated by IoT devices. By employing sophisticated machine learning algorithms, farmers can analyse historical and real-time data to uncover valuable insights, forecast trends, and proactively manage their farms. Applications of machine learning in agriculture, such as precision agriculture, automated monitoring, and predictive maintenance, have contributed to increased efficiency, optimized resource utilization, and higher crop yields. While delving into the challenges and opportunities, the commentary emphasizes the significance of data privacy and security in IoT-based agriculture. Furthermore, it discusses the integration of machine learning at the edge, which facilitates rapid and decentralized decision-making, minimizing latency and dependence on cloud-based solutions. The potential societal and environmental impacts of adopting IoT-based smart farming techniques are also discussed, as these applications promote sustainable practices, resource conservation, and food security

Machine Learning Applications in Agriculture: Current Trends, Challenges, and Future Perspectives

Progress in agricultural productivity and sustainability hinges on strategic investments in technological research. Evolving technologies such as the Internet of Things, sensors, robotics, Artificial Intelligence, Machine Learning, Big Data, and Cloud Computing are propelling the agricultural sector towards the transformative Agriculture 4.0 paradigm. The present systematic literature review employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to explore the usage of Machine Learning in agriculture. The study investigates the foremost applications of Machine Learning, including crop, water, soil, and animal management, revealing its important role in revolutionising traditional agricultural practices. Furthermore, it assesses the substantial impacts and outcomes of Machine Learning adoption and highlights some challenges associated with its integration in agricultural systems. This review not only provides valuable insights into the current landscape of Machine Learning applications in agriculture, but it also outlines promising directions for future research and innovation in this rapidly evolving field.

Machine Learning to Predict Rice Stock for Food Security: A Brief Study

The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” and “rice”. Only journal papers were considered eligible that were published within 2020 – 2023. Approximately 49 articles from scopus and google scholar database. It was selected for full content review after the pre-screening process. The results indicated that this topic pertains to different disciplines that favor convergence research at the international level. Prediction criteria related to food security were found to be the most inputs of prediction models. Machine learning models such as Random forest result in high accuracy in predict rice stock on 98%. This study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in predict rice stock and contributing to a more systematic research on this topic.

Machine Learning Techniques in Agriculture

Food is considered as a basic need of human being which can be satisfied through farming. Agriculture not only fulfils human’s basic needs, but also considered as source of employment worldwide. Agriculture is considered as a backbone of economy and source of employment in the developing countries like in India. Indian farmer still struggles when it comes to picking up the right crop for right biological and non-biological factors. Thus, to accelerate the yield of crops, different AI techniques been proposed worldwide. Advancement in area of machine learning has helped improving gains in agriculture. Machine learning is the current technology which is benefiting farmers to minimize the losses in the farming by providing rich recommendations and insights about the crops. This paper presents an extensive of latest machine learning techniques in agriculture. Techniques of machine learning in agriculture allows more efficient and precise farming with less human manpower with quality production.

Machine Learning Techniques in Agriculture

Food is considered as a basic need of human being which can be satisfied through farming. Agriculture not only fulfils human’s basic needs, but also considered as source of employment worldwide. Agriculture is considered as a backbone of economy and source of employment in the developing countries like in India. Indian farmer still struggles when it comes to picking up the right crop for right biological and non-biological factors. Thus, to accelerate the yield of crops, different AI techniques been proposed worldwide. Advancement in area of machine learning has helped improving gains in agriculture. Machine learning is the current technology which is benefiting farmers to minimize the losses in the farming by providing rich recommendations and insights about the crops. This paper presents an extensive of latest machine learning techniques in agriculture. Techniques of machine learning in agriculture allows more efficient and precise farming with less human manpower with quality production.

Crop Prediction Model Using Machine Learning Algorithms

Machine learning applications are having a great impact on the global economy by transforming the data processing method and decision making. Agriculture is one of the fields where the impact is significant, considering the global crisis for food supply. This research investigates the potential benefits of integrating machine learning algorithms in modern agriculture. The main focus of these algorithms is to help optimize crop production and reduce waste through informed decisions regarding planting, watering, and harvesting crops. This paper includes a discussion on the current state of machine learning in agriculture, highlighting key challenges and opportunities, and presents experimental results that demonstrate the impact of changing labels on the accuracy of data analysis algorithms. The findings recommend that by analyzing wide-ranging data collected from farms, incorporating online IoT sensor data that were obtained in a real-time manner, farmers can make more informed verdicts about factors that affect crop growth. Eventually, integrating these technologies can transform modern agriculture by increasing crop yields while minimizing waste. Fifteen different algorithms have been considered to evaluate the most appropriate algorithms to use in agriculture, and a new feature combination scheme-enhanced algorithm is presented. The results show that we can achieve a classification accuracy of 99.59% using the Bayes Net algorithm and 99.46% using Naïve Bayes Classifier and Hoeffding Tree algorithms. These results will indicate an increase in production rates and reduce the effective cost for the farms, leading to more resilient infrastructure and sustainable environments. Moreover, the findings we obtained in this study can also help future farmers detect diseases early, increase crop production efficiency, and reduce prices when the world is experiencing food shortages.

Random Forest Regression in Maize Yield Prediction

Artificial Intelligence is the discipline of making computers behave without explicit programming. Machine learning is a subset of artificial Intelligence that enables machines to learn autonomously from previous data without explicit programming. The purpose of machine learning in agriculture is to increase crop yield and quality in the agricultural sector. It is driven by the emergence of big data technologies and high-performance computation, which provide new opportunities to unravel, quantify, and comprehend data-intensive agricultural operational processes. Random Forest is an ensemble technique that reduces the result's overfitting. This algorithm is primarily utilized for forecasting. It generates a forest with numerous trees. The random forest classifier predicts that the model's accuracy will increase as the number of trees in the forest increases. All through the training phase, multiple decision trees are constructed. It generates subsets of data from randomly selected training samples with replacement. Each data subset is employed to train decision trees. It utilizes multiple trees to reduce the possibility of overfitting. Maize is a staple food in Kenya and having it in sufficient amounts in the country assures the farmers' food security and economic stability. This study predicted maize yield in the Kenyan county of Uasin Gishu using the machine learning algorithm Random Forest regression. The regression model employed a mixed-methods research design, and the survey employed well-structured questionnaires containing quantitative and qualitative variables, which were directly administered to 30 clustered wards' representative farmers. The questionnaire encompassed 30 maize production-related variables from 900 randomly selected maize producers in 30 wards. The model was able to identify important variables from the dataset and predicted maize yield. The prediction evaluation used machine learning regression metrics, Root Mean Squared error-RMSE=0.52199, Mean Squared Error-MSE =0.27248, and Mean Absolute Error-MAE = 0.471722. The model predicted maize yield and indicated the contribution of each variable to the overall prediction.

Machine learning in agriculture: a review of crop management applications

Machine learning in agriculture: a review of crop management applications

Review of Sustainable Irrigation Technological Practices in Agriculture

The paper focuses on the increasing demand for water and its impact on irrigated agriculture, emphasizing the importance of effective water management. It reviews the use of soil moisture sensors, IoT, big data analytics, and machine learning in agriculture, particularly in the context of Indian agriculture. The study explores the potential of IoT technologies, such as sensors, drones, and machine learning algorithms, to optimize water usage, minimize waste, and enhance crop yields. The role of big data analytics in sustainable water irrigation management and decision support systems is highlighted. The integration of IoT and sensory systems in smart agriculture is discussed, addressing both the challenges and benefits of implementing sensory-based irrigation systems. Additionally, the paper describes an automated irrigation system developed to optimize water use for crops, utilizing a distributed wireless network of sensors and a web application. The system, powered by photovoltaic panels, demonstrated significant water savings of up to 90% compared to traditional irrigation methods in a sage crop field. The system's energy autonomy and cost-effectiveness suggest its potential utility in water-limited and geographically isolated areas.

Machine Learning Algorithms and Fundamentals as Emerging Safety Tools in Preservation of Fruits and Vegetables: A Review

Machine learning assists with food process optimization techniques by developing a model to predict the optimal solution for given input data. Machine learning includes unsupervised and supervised learning, data pre-processing, feature engineering, model selection, assessment, and optimization methods. Various problems with food processing optimization could be resolved using these techniques. Machine learning is increasingly being used in the food industry to improve production efficiency, reduce waste, and create personalized customer experiences. Machine learning may be used to improve ingredient utilization and save costs, automate operations such as packing and labeling, and even forecast consumer preferences to develop personalized products. Machine learning is also being used to identify food safety hazards before they reach the consumer, such as contaminants or spoiled food. The usage of machine learning in the food sector is predicted to rise in the near future as more businesses understand the potential of this technology to enhance customer experience and boost productivity. Machine learning may be utilized to enhance nano-technological operations and fruit and vegetable preservation. Machine learning algorithms may find trends regarding various factors that impact the quality of the product being preserved by examining data from prior tests. Furthermore, machine learning may be utilized to determine optimal parameter combinations that result in maximal produce preservation. The review discusses the relevance of machine learning in ready-to-eat foods and its use as a safety tool for preservation were highlighted. The application of machine learning in agriculture, food packaging, food processing, and food safety is reviewed. The working principle and methodology, as well as the principles of machine learning, were discussed.

Automation and Digitization in the Seed Sector: Offline to Online

As automation has become an integral part of today's world, the Internet of things (IoT) and Machine learning (ML) offers a platform for tracking and controlling different environments remotely with the help of sensors, controllers and the internet. Today IoT and ML are taking over control of agriculture industries and playing an important role in smart farming. The literature highlights the potential use of IoT and ML in agriculture and even in seed sector which is feasible for surveillance and monitoring from sowing to harvesting and similar packaging operations. In this review, we have highlighted the use of IoT applications with remote sensors such as temperature, humidity, soil moisture, water level sensors and pH value providing an idea of automation in seed sowing, seedling development and yield, which displays precision as well as practical utility to cope with difficulties in the field. It also addresses the digitalization in seed distribution and market systems. Some notable prospects by ML in the seed industry viz., seed tracking and smart storage system has also been stated.

A REVIEW ON USE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN AGRICULTURE

The implementation of artificial intelligence (AI) and machine learning (ML) techniques in the field of agriculture has gained significant attention in recent years. This report explores the various applications of AI and ML in agriculture, focusing on their benefits, challenges, and future prospects. By analyzing existing research and case studies, this report aims to provide insights into the potential of AI and ML technologies to revolutionize farming practices and improve crop yield, resource management, and sustainability in the agricultural sector.

Stress phenotyping in plants using artificial intelligence and machine learning

The global population is rapidly increasing and is expected to exceed 9 billion by 2050, resulting in significant challenges for agriculture due to factors such as industrialization, reduced farmland, and biotic and abiotic stresses. To address these challenges and ensure future sustainability, the agriculture system needs to become more productive, efficient, and resilient. Artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools to transform the agricultural sector. Agricultural productivity is greatly influenced by biotic and abiotic stresses, and developing climate-smart crops through conventional breeding techniques is time-consuming and challenging. Plant phenotyping, which involves measuring specific plant features related to function, is crucial in breeding for target traits. However, traditional phenotyping methods are laborious, error-prone, and less accurate, particularly under stress conditions. To overcome these limitations, researchers have focused on developing high-throughput phenotyping technologies. State-of-the-art imaging techniques, such as light detection and ranging (LIDAR), remote sensing, and RGB imaging, combined with autonomous carriers like unmanned aerial vehicles (UAVs) and ground robots, enable real-time and high-throughput phenotyping of morphological, physiological, and stress-related traits. ML tools can compartmentalize big data, identify related traits, classify them, quantify their expression, and predict their function within the plant system. AI and ML offer multidisciplinary approaches for analyzing big data accumulated over time, leading to the discovery of patterns and systematic data of interest, such as stress phenotypes. Using these technologies, researchers worldwide can expedite agricultural research and develop climate-smart crops. The future of AI and ML in agriculture is promising, as they can lead to new scientific discoveries and help overcome the challenges of limited resources in food production.

A Review on Machine Learning Techniques Used in Rice Crop Management

Abstract: Rice is a major food crop for a significant population of the world and increase in its yield is a priority to ensure nutrition for many countries. Thus it is important to protect the crop from the various diseases that affect the crop. With the advent of machine learning, a number of methods have been proposed to identify diseases after the occurrence as well as at an early stage. Diagnose of these predators before occurrence can be much more beneficial. Journal papers that used rice as a major crop were considered eligible. In this review, an attempt is made to understand the preferred machine learning methods and algorithms that can be employed to detect various diseases and pest as well as predict crop yield of the rice crop. It is expected to be beneficial to all who wish to use machine learning in agriculture that can lead to constructive research in this area.