AI IN AGRICULTURAL BIOTECHNOLOGY
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Introduction
Artificial intelligence is the field dedicated to creating machines capable of emulating human thought processes, enabling them to perform tasks typically associated with intelligence.
Biotechnology is a branch of science which combines science and technology to make a better world and improve the quality of living of an individual. Many technologies have been developed recently. This includes Genetic Engineering, Recombination, Cloning, PCR (Polymerized Chain Reaction), etc. Biotechnology is widely used in medical industries, food industries, agricultural field, aquaculture, GMO and many more.
AI in Biotechnology
AI's significance in biotechnology is increasingly prominent. The biotech sector is transforming its operations by leveraging AI and ML technologies for enhanced efficiency, speed, and precision. Whether in pharmaceuticals, healthcare, animal farming, or agriculture, the synergy of AI and biotechnology is paving the way for significant progress and groundbreaking innovations. Artificial intelligence plays a crucial role in the biotechnology field by accelerating drug discovery, providing advanced analytics, precise medical diagnostics, gene editing, personalized medicine development, and other contributions to benefit humanity. In this discussion, we will explore the significance of AI in the biotech sector.
AI in Agricultural Biotechnology
AI technology in agriculture offers a viable solution to enhance food security amidst changing climate conditions. By pinpointing resilient crop varieties that can adapt to environmental stressors like drought, it becomes possible to sustain optimal crop yields despite abiotic challenges that can significantly hinder productivity. For instance, high temperatures can cause a 6% reduction in wheat yields per degree Celsius, with Rubisco activity impeding photosynthesis above 35°C. Addressing the stress physiology induced by water and nutrient constraints in crops can be efficiently tackled through cutting-edge remote sensing techniques. Leveraging AI alongside affordable multi-channel sensors and remote sensing technologies for extensive data collection necessitates robust infrastructure to ensure data security.
These technologies can also be used to discover new crop characteristics that are more resource-efficient and resilient under fluctuating climate conditions. Phenotyping has emerged as a crucial area of study in plant sciences over the past decade. Advancements in sensors and "big data" technologies have propelled phenotyping as a primary focus in breeding to effectively address the evolving demands of global change, including escalating challenges from both abiotic and biotic pressures. Combining data from image-based phenotyping with details on stress responses at a molecular level, including genomic variations, gene and protein expression, as well as metabolite biosynthesis due to stressors and their intensities, can facilitate the development of hormesis management strategies. This integrated approach could yield significant benefits by harmonizing diverse data sources.
The rising potential of utilizing big data has heightened the importance of artificial intelligence in agricultural fields. The availability of data expands rapidly with the adoption of technologies already prevalent in agriculture, ranging from proximal to remote sensing methods like image-based phenotyping in controlled environments, unmanned aircraft systems (UAS) for field-level operations, and satellite-assisted remote sensing on a global scale. To leverage this information effectively, advancements in computer vision algorithms are crucial, providing unparalleled opportunities to enhance our understanding of agricultural systems.
Molecular breeding refers to the use of molecular biology technologies, namely genetic alteration of DNA, to improve animal or plant properties. The tools include molecular marker-assisted or genomic selection, as well as gene editing or genetic engineering. Plant tissue culture is an effective commercial plant propagation technique.
Applications of AI in Agricultural Biotechnology:
●????? quick plant production regardless of season,
●????? development of heat, drought, and salinity tolerant types,
●????? disease resistant plants,
●????? conservation of endangered species, and
●????? genetic transformation, among other applications.
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These applications attempt to increase crop performance in agriculture and include a variety of approaches such as tissues, organs, and in-vitro plant regeneration, aseptic cell growth, molecular genetics, genome analysis, gene transfer, and recombinant technologies. The advantages of using these technologies in agriculture include the preservation of desired genetics, consistent plant development, genetic improvement for better plant efficiency, and year-round production regardless of season.
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Tissue Culture With AI
The combination of tissue culture with AI and other optimization algorithms has been demonstrated as a sector of technology for improving manufacturing efficiency. Plant tissue culture, which is based on "totipotent," a stem cell's ability to differentiate into a variety of cell types, serves as the foundation for "micropropagation”. Plants are cultivated in vessels that contain culture material derived from various explants. This in vitro culture includes nutrients and growth regulators. The word "micropropagation" refers to the smaller growth of plants in vitro than in vivo. In vitro culture is one of the most important technologies for propagation and breeding of various crop species, permitting varied approaches such as shoot multiplication or the generation of plants from cells and tissues by the formation of somatic embryos or adventitious shoots. The specific nutrient requirements of different plant cells and tissues vary by plant species, therefore improving culture media is a time-consuming procedure that necessitates many media formulations. In this scenario, AI models are extremely useful in overcoming the challenge of complex interactions with many in vitro culture variables, which cannot be solved using an impractical number of treatments and classical statistics. These AI models can simulate and forecast plant tissue development and growth in vitro under a variety of situations, allowing for media optimization with a realistic number of treatments. The ability of AI models to deal with various in vitro systems and the results of the culturing have turned them into a widespread strategy used by plant tissue scientists.
Languages Used in Agriculture:
●????? Java
●????? Python
●????? PHP
●????? JavaScript
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Types of AI used in Agriculture
Automated farm machinery like
1.??? Smart Irrigation
2.??? Fertilized systems
3.??? IoT-powered agricultural drones
4.??? Smart spraying
5.??? Vertical farming software
6.??? AI-based greenhouse robots
7.??? Driverless tractors and many more.
Example of AI in Agriculture in India
Farmers in India are combating climate change, disease, and financial difficulties, with AI-driven projects such as AI4AI providing innovative solutions. The "Saagu Baagu" project under AI4AI has increased yields and revenues for 7,000 Chilly farmers in Telangana, doubling their earnings using aggrotech and data management. Following its success, 'Saagu Baagu' is set to reach 500,000 farmers across five value chains, highlighting AI's enormous potential in agriculture.
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Drought Resistance in Rice Paddies
Scientists hope to utilize AI and the CRISPR-CAS9 gene scissors to create climate-resistant super cultures that can produce bigger yields with fewer resources. They accomplish this by changing the plant's genes using a technique known as genome editing.? Rice provides an ideal platform for exploration. Rice, a traditionally thirsty plant that grows submerged in water, has been severely impacted by catastrophic drought from Italy to China and Pakistan. A new kind known as IR64 could assist. It grows largely in Asia and parts of Africa but is sold all around the world. The plant's genes have been modified to make it more drought resistant. In some weeks, it requires up to 40% less water than before.
Returning to the ground, artificial intelligence can be quite useful in monitoring soil health. On the one hand, computer vision can easily define soil organic matter and texture at wide spatial scales, giving valuable information for improving existing soil maps. Identifying soil health metrics is typically a time-consuming and costly process. As a result, identifying essential soil variables underlying soil health functions for larger-scale monitoring is critical to developing successful monitoring schemes. Laboratory data and low-cost handheld equipment can be utilized to train algorithms to assess soil health or prospective areas for improvement.
Digitalization in agriculture can increase data collecting and recording of soil health condition, as well as the future use of regenerative agricultural practices. Soil properties and functions such as organic matter, pore volume, aggregate stability, water holding capacity, microbial activity, and nutrient (mostly nitrogen) availability have a favorable impact on both the environment and crop productivity. These soil functions are heavily influenced by soil management practices (e.g., tillage, mechanical weed control) and crop rotations. Crops' positive benefits on soils, such as delivering nutrients and organic matter, are sometimes complex and overlooked due to a lack of quick responses.
AI can help discover important drivers of ecosystem processes and how to govern them through methods that can be deployed in land use, specifically agricultural systems. The loss of key taxa is one source of concern, but AI can also identify the complex interplay of the food-web belowground, as environmental disturbances not only reduce the abundance of some organisms, but also have consequences for others, as food webs contain multiple interactions. Only biodiverse ecosystems provide a store of genetic resources for crops, livestock, and soil biota, which are required for nutritious diversity and an important deterrent to health, i.e., through micronutrient availability. Many cultures rely on natural goods gathered from ecosystems not just for medical treatment, but also for cultural purposes.
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