Study On Artificial Intelligence Applications Uses In Agriculture

Automation in the agriculture sector is popular, emerging issue in addition to significant concern worldwide. In addition to this increased claim of food, the population is increasing a lot, in addition to employment is also increasing. The traditional approach which was put in place by farmers was not sufficient to meet these requirements. Therefore, innovative automated approaches were presented. These innovative approaches met the requirements of food, in addition to also provide the possibilities of serving billions of people currently of Artificial Intelligence (AI). This article presents an assessment of the software of AI in soil management, crop management, weed disease management. A better focus is systematic to observe the glitches, develop suitable explanations for it, in addition to create the optimal results designed for it, Artificial Intelligence act a great help to report crop diseases. Thus, the review paper discusses a quick summary of the application of an artificial intelligence in agronomy, hence the existing methods for agronomy in addition to spotlighting the existing several approaches for the discovery of diseases in crop. The classification technique convolutional neural network provides better accuracy associated with traditional methods.

Introduction

By 2050, the world population is expected to reach nearly 10 billion, expanding the agricultural order by about 50% compared to 2013 in a situation of modest economic growth. Crop production currently occupies about 37.7% of the total land surface. Agriculture is important in terms of income generation and contribution to national income. It contributes substantially to the economic prosperity of industrialized countries and also plays a vital role in the economies of emerging countries. A large increase in the per capita income of the rural community has resulted from the expansion of agriculture. Consequently, it would be sensible and appropriate to emphasize the agricultural sector. Agriculture contributes to 18% of the GDP in India and employs 50% of the workers. Rural development will be boosted by agricultural development, which will eventually lead to rural transformation and structural change as by 2030, India’s population is predicted to exceed 1.6 billion people. With such a large increase in population, significant demand for agricultural products is also expected. The advancement of the service industry has resulted in a large shift of workers from primary to tertiary sectors. Moreover, lack of awareness about emerging diseases in crops is reducing crop yields. Food being the most basic need of human life, future research should focus on reviving the agricultural sector. Due to the various variety of agricultural species, a comprehensive database is required for various aspects of agriculture.

Farming can be made more efficient for farmers by employing suitable artificial intelligence techniques and datasets. These techniques can be considered as adequate implementation in the event of a future crisis. Agriculture has been elevated to a new level thanks to AI-based tools and equipment. This technology has resulted in improved crop production, as has real-time monitoring, harvesting, processing, and marketing. In the agriculture-based sector, the latest technologies of automated systems using agricultural robots and drones have made a significant contribution. Many high-tech computer-based systems have been developed to identify various important factors such as weed detection, yield detection, crop quality and a variety of other ways. Science, education, economics, agriculture, industry, security and a variety of other fields have all been influenced by AI. AI implementation requires a machine learning process. This leads us to the “Machine Learning” sub-domain of the AI field. The main purpose of machine learning is to give the machine data from past experiences and statistical data so that it can perform its assigned duty of solving a specific problem. Today’s uses include data analysis based on prior data and experiences, speech and facial recognition, weather prediction, and medical diagnosis. The domains of Big Data and Data Science have evolved to a significant extent in this way as a result of machine learning.

Machine learning is a method for creating intelligent machines that is based on mathematics

1) IoT-driven growth: IoT generates massive amounts of data in both organized and unstructured formats every day (Internet of Things). These include information such as historical weather patterns, soil reports, fresh studies, rainfall, pest infestations, and photographs captured by drones and cameras, among other things. All this data can be sensed by cognitive IoT solutions, which can then provide actionable insights to increase yield.

2) Soil testing: Proximity sensing and remote sensing are advanced technologies that represent for smart data fusion. Soil testing is an application of this high-resolution data. Although remote sensing requires the integration of sensors into aerial or satellite systems, proximity sensing requires sensors that are in direct contact with the ground at a relatively close range. This aids in soil classification based on the soil beneath the surface at a particular location.

3) Image-based insight generation: Drone-based imaging can contribute with in-depth field analysis, crop monitoring, field scanning, and other tasks. They can be used in conjunction with computer vision technologies and the Internet of Things to allow farmers to initiate prompt actions. These services provide daily weather alerts to farmers.

4) Detection of crop diseases: Under white/UV-A light, computer vision technology is used to acquire images of diverse crops. Farmers can then sort the produce into different piles before delivering it to the market. Leaf images are divided into sections for further diagnosis through image pre-processing. Such technology will identify pests more specifically.

5) Optimal mix of agricultural products: Cognitive computing advises farmers on the simplest choice of crops and seeds based on multiple parameters such as soil conditions, weather outlook, seed type, as well as infections around a certain field. The advice is further customized based on the farm’s requirement, local conditions including past achievements. However Artificial Intelligence can take into account external factors such as market trends, prices, as well as consumer expectations.

6) Crop Health Monitoring: Remote sensing technology, and hyperspectral imagery as well as 3D laser scanning, were needed to develop crop metrics across thousands of acres. This has the potential to usher in a fundamental shift in how producers monitor crops in terms of time and energy. This equipment will track crops throughout their life cycle and produce data to identify any irregularities.

Literary Inspection

Farming comes with some lot of possibilities as well as uncertainties. There are fluctuations from season to season, such as the prices of farming materials, soil degradation, crop failure, weed suffocation, pest damage, plus climate variability. Farmers must manage with these uncertainties. Although planting is vast, soil, crop, disease, as well as pests are all major contributors to agricultural production under this study. It is important to review AI systems for agriculture in terms of soil, crop, disease, as well as pest management.

Soil is now a vital component of successful agriculture as it provides the basic source of nutrients for crop growth. Soil is just the foundation of all agriculture, forestry, as well as fishing production systems. Water, minerals, and proteins are stored in the soil until they are needed for crop growth and development. Crop farming is extremely important for long-term economic development. However, it also provides food, raw materials, and jobs. Marketing, processing, distribution, and post-sales support are now considered part of agricultural production in modern times. Crop farming, as well as other primary industries, are now being prioritized in places in which real income per capita seems to be low.

Increasing crop factory production as well as productivity is seen to contribute significantly to the overall economic development of a country. They can be used in conjunction with computer vision technologies and the Internet of Things to allow farmers to initiate prompt action. Plant diseases reduce the quantity and quality of agricultural production as agriculture tries to meet the demands of a rapidly growing population. The loss of crop infestation in agriculture can be devastating. Weeds are a significant threat to all agricultural activity. Weeds reduce field as well as forest productivity, eliminating crops, suffocating pastures and even killing livestock in some circumstances. They constantly contend for water, nutrients as well as sunlight with crops, resulting in reduced agricultural quality.

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