بیوتکنولوژی

This study explores two big data (BD)-driven agricultural models backed by the National Institute of Food and Agriculture (NIFA). It examines the benefits of thorough agricultural records, efficient phenotyping techniques, and teamwork in promoting agriculture, highlighting the role of technological advancements like sensors, robotics, machine learning (ML), big data analytics, remote sensing, and genomics in addressing global food and water security issues. The study's primary goals were to examine the advantages of maintaining accurate agricultural records for better breeding and agronomy, investigate strategies for efficient phenotyping and data collection in agricultural systems, then support interaction between plant breeders, agricultural scientists, and specialists in ML, remote biosensing (RBS), and BD, and to determine the financial requirements for the ongoing advancement of BD-driven agriculture models.

The AVIRIS Indian Pines database was utilized in these tests. The Indian Pines database encompasses the farming industry. The dataset consists of 16 different groups. The studies used an Intel i5 laptop with a 2.4-GHz Central Processing Unit (CPU) (four cores) and 16 gigabytes of Memory.

The integration of technology, including sensors, remote sensing, robots, and BD analytics, enhances high-throughput phenotyping and precision farming. Multidisciplinary cooperation accelerates crop breeding and management. Future financing is needed for predictive machine learning models and scalable phenotyping techniques.

BD analytics, remote sensing, and machine learning have the ability to transform agronomy and plant breeding, tackling issues related to food security. Sustained cooperation and sufficient infrastructure investment are essential for successful implementation, with specific funding required for the development of cutting-edge instruments and technologies that guarantee sustainable farming methods.

Degradation of land quality is a major concern for the United Nations Environment Programme (UNEP) since it harms human health and food security. Restoring healthy soil is a primary goal of sustainable agriculture (SA), as it is essential for crop yields and other ecosystem services. This can be overcome by engineering GMOs to produce materials or enzymes that aid in the breakdown of soil pollutants. In this work, we look at the possibility that GMOs can speed up the bioremediation process and increase efficiency in the removal of toxic contaminants from soil.

The study occurred in the Nilokheri part of the Karnal area in Haryana, India. During the field trip, data was collected by interviewing 150 residents in 32 communities of the Nilokheri division in the Karnal area. Information collected included cultivation methods, farming history, varieties utilized, mean yield, thickness, location of irrigation water, and the amount and kind of fertilizers used.

The research suggests a long-term solution to soil health problems by integrating GMOs, rhizobacteria, and other soil treatments into a single strategy. Within a decade, this integrated approach may have restored soil fertility and reduced pollution impacts, guaranteeing that agricultural land will be sustainable in the long run. Genetically modified organisms (GMOs) have the potential to quicken the land's natural recovery process, which in turn increases the land's production and resistance to persistent environmental threats.

Finally, this study shows that genetically modified organisms (GMOs) have the potential to improve soil health through better bioremediation, which would lead to more sustainable agriculture. Nevertheless, it also stresses the need to evaluate the advantages of GMOs against their possible ecological dangers and to support a careful, regulated strategy for their use in environmental contexts.

Modern problems including rising food demand, limited resources, and environmental degradation can be effectively addressed through the revolutionary practice of smart agriculture (SA). Meeting global demand while reducing environmental effect is a challenge for traditional farming practices. By enhancing agricultural methods, increasing crop yields, and decreasing resource consumption, the combination of Biotechnology (BT) with SA provides a revolutionary solution.

Smart Agriculture systems' incorporation of data analytics and Deep Neural Networks (DNN) has increased the optimization potential of agriculture even further. In order to improve crop management, decrease waste, and increase overall farm production, farmers can use data-informed decisions made possible by DNN algorithms to get practical insights into crop health, growth trends, and ideal farming practices.

A Real-Time Crop Monitoring and Management (R-CMM) system integrating DNN, Internet of Things (IoT), and Biotechnology (BT) is proposed in this research as an application of Smart Agriculture. By collecting biological signals from the environment using tiny, renewable, and non-invasive sensors, IoBT provides real-time data on plant health, soil conditions, and climate parameters. With this, automated administration of crop systems and continuous monitoring from a distance are both made possible, cutting down on personnel expenses and increasing overall efficiency.

Indoor crop plantation management relies on a number of critical characteristics, including temperature, humidity, soil moisture, and light intensity, all of which the R-CMM system uses to keep checks on. The platform’s use of DNN algorithms allows for more effective and accurate farming by predicting when crops may experience stress, optimizing the allocation of resources, and detecting early indications of disease or pest infestations.

Cotton is a prominent fiber that commands the worldwide industrial and agricultural sectors. Cotton is a fundamental material used in the creation of textiles. Diagnosing the diseases on cotton plants' leaves soon is essential to prevent them and enhance productivity. Tracking cotton leaf illnesses and assessing plant health is challenging for farmers who rely solely on their subjective expertise and knowledge. Moreover, Artificial neural networks have been proposed to alleviate limitation of traditional methods and can be used to handle nonlinear and complex data, even when the data is imprecise and noisy. Agricultural data can be too large and complex to handle through visual analysis or statistical correlations. This has encouraged the use of machine intelligence or artificial intelligence The objective of this study was to diagnose diseases and improve the cultivation of cotton using Artificial Intelligence (AI) methods.

The study findings indicate that the current automated detection approaches for cotton crop illnesses are still in their early stages of development with biotechnology and Artificial Intelligence (AI). This review acknowledges the need to develop automated, cost-effective, dependable, precise, and swift diagnostic tools for detecting cotton leaf diseases to enhance output and quality.

This paper analyzes the several computational techniques used at different phases of plant-pathogen structures, including image preparation, segmentation, extracting features and selecting, and categorization. The study identified valid future paths and areas for additional exploration. There is a need for innovative, fully automated computer-assisted methods to identify and categorize various illnesses in cotton crops.

With the help of weather data from the agricultural Internet of Things (IoT) method's, it is possible to plan for changes in the weather. This is an excellent way to prepare and keep track of the production of green agriculture. Thus, the aim of this study was to make weather information forecasting more accurate in the Precision Agriculture (PA) system.

It is difficult to accurately predict future trends as the data is complicated and requires simple linear links. The evolution of communication technology and the increasing number of interconnected things have had a profound impact on the agricultural sector. Advances in AI, and deep learning in particular, have facilitated faster and more accurate data processing in this modern digital era. A new data analytics technology called deep learning has the potential to make farming more efficient, eco-friendly, and predictable. In this study Deep Learning (DL) predictions with a two-level decomposition structure and Biotechnology (BT) were used to make the prediction of weather information in the Precision Agriculture (PA) system more accurate. First, the weather data was decomposed into four parts. Then, the Gated Recurrent Unit (GRU) systems were created as sub-predictors for each part.

First, the weather data was decomposed into four parts. Then, the Gated Recurrent Unit (GRU) systems were created as sub-predictors for each part. The predictions for the medium and long-term future were made by combining the results from the GRUs. Using weather data from the BT-based IoT systems, it was confirmed that the tests work with the suggested structure.

The proposed prediction method can predict the temperature and humidity correctly and meets the PA standards. It can assist farmers in the management of their agricultural operations. It is possible to provide an initial prediction and assessment of extreme weather conditions in agriculture to minimize risks and maximize profits.

With the help of weather data from the agricultural Internet of Things (IoT) method's, it is possible to plan for changes in the weather. This is an excellent way to prepare and keep track of the production of green agriculture. Thus, the aim of this study was to make weather information forecasting more accurate in the Precision Agriculture (PA) system.

It is difficult to accurately predict future trends as the data is complicated and requires simple linear links. The evolution of communication technology and the increasing number of interconnected things have had a profound impact on the agricultural sector. Advances in AI, and deep learning in particular, have facilitated faster and more accurate data processing in this modern digital era. A new data analytics technology called deep learning has the potential to make farming more efficient, eco-friendly, and predictable. In this study Deep Learning (DL) predictions with a two-level decomposition structure and Biotechnology (BT) were used to make the prediction of weather information in the Precision Agriculture (PA) system more accurate. First, the weather data was decomposed into four parts. Then, the Gated Recurrent Unit (GRU) systems were created as sub-predictors for each part.

First, the weather data was decomposed into four parts. Then, the Gated Recurrent Unit (GRU) systems were created as sub-predictors for each part. The predictions for the medium and long-term future were made by combining the results from the GRUs. Using weather data from the BT-based IoT systems, it was confirmed that the tests work with the suggested structure.

The proposed prediction method can predict the temperature and humidity correctly and meets the PA standards. It can assist farmers in the management of their agricultural operations. It is possible to provide an initial prediction and assessment of extreme weather conditions in agriculture to minimize risks and maximize profits.

Converting data into digital form has led to a massive influx of data in almost every industry that relies on data-driven operations. The digital data processing has significantly increased the volume of information being processed. The emergence of electronic agriculture management has profoundly impacted Information and Communication Technology (ICT), resulting in advantages for farmers and customers and driving the adoption of technological solutions in rural areas. This study emphasizes the promise of ICT technologies in conventional agriculture and the obstacles to their employment in farming operations.

This study emphasizes the promise of ICT technologies in conventional agriculture and the obstacles to their employment in farming operations. The research provides thorough information on automation, Internet of Things (IoT) gadgets, and challenges related to Machine Learning (ML). Drones are being contemplated for crop monitoring and production optimization in Precision Agriculture (PA) and Smart Farming (SF). The new era of conventional agriculture is represented by precision agriculture. The development of several contemporary technologies, like the internet of things, has made this possible. When relevant, this article emphasizes global and advanced agricultural systems and platforms that utilize IoT technology.

The effectiveness of such techniques in plant disease detection is proven by their ability to achieve exceptional levels of accuracy. This is particularly true when they rely on extensive open-source databases and pre-trained algorithms. Future investigation uncovered that the size of the plant imagery utilized for modeling and the circumstances under which the photos were gathered could significantly affect the accuracy.
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The Food Supply Chain (FSC) internationalization has caused significant problems for the food system. These problems include fraud, security, dependability, and quality, all caused by a lack of fair data. Globalization makes dealing with these problems more complex and complicated, which makes FSC more successful. Blockchain (BC) has shown that it can change FSC by using its strengths. Much research is still being done on using BC in FSC, but it is still in its early stages. This work takes a close look at all the previous research that has been done on BC and Biotechnology (BT) integrated FSCs (BT-FSC).

A review using well-known sources was part of the research. Publications from 2018 to 2024 were included in the study. Fifty pieces were looked at to find the things that make BC possible and its pros and cons. Based on the facts in this study, a theoretical framework was created to help with the adoption of BC in the BT-FSC.

The poll found that the main things that stop people from using BC are its scalability, interaction, higher prices, inadequate knowledge, and laws. It adds to what is already known by giving helpful information about how BC is used in the BT-FSC. It provides other businesses with evidence-based advice on how to make plans for adopting BC.