Description:Field of the Invention
The present invention pertains to controlling thrust in selection of crop according to climatic conditions and temperature, by taking the pH value of the soil and consideration of types of soil.
Background of the Invention
Agriculture plays a major role in Indian economics. Proper crop selection plays a vital role in agriculture sector.It is a important decision that should be taken by a farmer before starting a crop. Due to taking of a wrong decisions in crop selection process ,some farmers grow that product which was marketed great price at last year.By this due to loss in their crop they are commiting suicides. By taking the geo conditions,pH value of soil,fertility rate of soil into consideration we can suggest them a better crop suitable for farming.
Previously all are done with the diseases that can be attacked and precautions that are to be taken by farmer to protect their crop or they are done with the only 0ne of the crop selection factors . But in this case study we are divided crop selection process is divided into 4 different
factors .By taking consideration of those four factors we can accurately predict the suitable crop than the other previous existing systems.
four factors are:-
1> Temperatur.
2> 2>PH value.
3> Rainfall.
4> Types of Soils.
1) Temperature : One of the important factor in crop selection is Temperature. Different plants are grown in Different Temperature.For example Cactus can be grown in High Temperature . paddy, maize, soyabean etc. can be grown in rainy condition. wheat,gram,pea etc.can be grown in winter condition.
All previous existing Systems are Failed to detect the Temperature .Here we calculated the temperature by taking the highest and lowest temperature of a month . so, that the temperature lies between the calculated temperature.

2) PH value: Different crops can be grown in different PH value conditions. PH value of soil plays a vital
role in production of crops. Most of all soil contains PH value between 3.5-10 . In higher rainfall areas PH value of soil lies between 5-7. Drier areas contains soil PH value between 6.5-9. For best growth of crop PH value of soil should be 6-7. If PH value of soil is more acidic,Then we increase the PH value by adding lime based compunds to soil such as Dolomitelime and agriculture lime. If PH value of soil is more basic, Then we decrease the PH value by adding sulphur aluminium sulfate or sulfuric acid
Types of Soil,
1.ALLUVIAL SOIL:
Northern Indian delta area
Rich in nutrients
rice
Ground nut
BLACK SOIL:
In deccan platetue
Rich in aluminum. Iron lime
Cotton
Sunflowers
RED SOIL:
Plateau of chattisgarh deccan
Iron oxides
Ragi potato oilseeds
LATERITE:
Tamilnadu karnataka
Acidic

Prior Art of the Invention:
US11113649B2 is a patent that is a computer implementing process that takes the conditions of particular whole region .and that gives the crop suggestion according to that region. here the data of the whole region is collected not about a particular farm and that data gives an suggestion of crop of whole region.
US20220374811A1 is a crop suggestion patent .this patent tells about the percentage of post crop yield percentage by using the previous years condition. But it's only assumption based by taking the previous year yield .but may be conditions may change .
AU2020213293B2 is a patent that gives the nitrogen presence in field by taking the details of the field and also gives the models of the water flow and also forecasts the weather . Here the conditions of crops and weather and nitrogen presence in an area are taken in database and it suggests crop accordition to situation and condition.
US20210342956A1 is a patent that gives the post revenue of a crop . By taking a subset of plurality saved data it gives the brief explanation of crop revenues of previous years. But by taking the previous years revenues into consideration all farmers are Following to cultivate the same crop but it may gives loss.

Summary of the Invention
Farmer decision-making, agricultural yield optimisation, and climate change adaptation are all made possible by employing AI for crop selection based on climatic circumstances. It contributes to boosting agricultural output, decreasing resource waste, and promoting environmentally friendly farming methods.
The use of Artificial Intelligence (AI) approaches has changed crop selection based on environmental circumstances. For the benefit of farmers and agronomists, AI algorithms analyse huge amounts of data linked to weather patterns, soil quality, and previous agricultural practises.
Additionally, AI systems continuously adapt to new data and learn from it, leading to increased accuracy and precision over time. To provide thorough crop selection ideas, these systems can take into account additional factors including the probability of pests and diseases, crop output predictions, and consumer demand.
Brief Description of Drawings
The invention will be described in detail with reference to the exemplary embodiments shown in the figures wherein:
Figure 1: Crop selection through AI
The process of choosing crops based on climatic conditions has become more precise and effective thanks to the power of artificial intelligence (AI). Farmers and agricultural specialists can choose the crops that are ideal for particular climatic situations by using AI algorithms and machine learning methods.
Huge volumes of climate data, particularly those related to temperature, precipitation, humidity, and other relevant environmental parameters, can be analysed using AI. A variety of sources, including weather stations, satellites, and ground sensors, collect this data, which is then input into AI models for analysis. To find patterns and relationships between various climate variables and crop performance, these models learn from historical data.
Detailed Description of the Invention
A key component of agricultural planning is choosing the right crops based on the local climate. In this process, artificial intelligence (AI) can be a great help to farmers and agronomists. Using AI to choose crops depending on climatic variables is described in depth here:
Data gathering: In order for AI systems to make wise decisions, they need a lot of data. Climate information is gathered and collated from a variety of sources, including meteorological stations, satellites, and weather forecasts. Considered are past weather patterns related to temperature, precipitation, humidity, and solar radiation.
AI uses machine learning algorithms to analyse the gathered data and identify patterns and correlations between crop performance and meteorological variables. Models can be trained using labelled historical data and supervised learning approaches, such as decision trees, random forests, or support vector machines, to link certain crops with related climatic conditions.
Engineering and feature selection: AI algorithms choose the dataset's most pertinent characteristics or variables that have a big impact on crop selection. These characteristics could consist of temperature ranges, rainfall patterns, or pH levels of the soil. Additionally, by merging already-existing variables, new features can be created that offer more insightful data.
Models for agricultural recommendations: After training, AI models can recommend crops depending on input from the current or future climatic circumstances. In order to recommend appropriate crops for a specific site and time period, the models take into account the discovered connections between crop performance and climate. The advice can be altered to take into account variables like soil type, readily available resources, and market demand.
Real-time monitoring: AI systems can use real-time data streams to continuously monitor the weather. The models may offer up-to-date advice and modify crop suggestions in response to shifting weather patterns by interacting with IoT devices and weather sensors.
Integrated into decision support systems that are available to farmers, agronomists, and policymakers are the AI-powered crop selection recommendations. With the use of these systems' user-friendly interfaces, stakeholders may input their location and get crop recommendations that are customised to their particular climatic circumstances.
AI systems can be created with the ability to continuously learn and advance over time. The algorithms can modify and improve their suggestions by taking into account fresh data and farmer feedback, which improves the accuracy of crop choices.
Equivalents
There are many benefits to using artificial intelligence (AI) to pick crops depending on climatic situations. The technique makes use of AI algorithms to analyse huge amounts of climatic data and offer useful data for selecting the best crops. AI can recommend the best crops for a location by taking into account a lot conditions like temperature, rainfall patterns, humidity levels, and soil qualities.
The custom of consulting area farmers or extension services can be seen as an alternative to this AI- powered crop selecting method. Such experts are highly knowledgeable about the local climate and how it affects crop growth. They offer suggestions based on their knowledge of the local weather patterns and experience.
4 Claims & 1 Figure , Claims:The scope of the invention is defined by the following claims:
Claims:
1. The crop selection using artificial intelligence comprises the following procedure,
A) AI algorithms are capable of analysing enormous datasets that include information on the environment, soil characteristics, previous crop performance, and other pertinent elements. By taking into account these factors, AI can assist in identifying the crops that are most suited for particular climatic circumstances, maximising yield potential.
B) Conventional farming practises face difficulties from climate change and extreme weather occurrences. AI can help in the identification of crops that are more suited to adjusting to climate change, such as drought-tolerant or heat-resistant kinds.
C) AI-powered tools can monitor environmental variables in real-time, such as satellite imagery and remote sensing. AI can make suggestions for crop selection based on the actual and anticipated climatic conditions of a specific place by merging this data with crop models and machine learning algorithms.
2. As per claim 1, the usage of AI to select crops most effectively can help cut back on the use of water, pesticides, and fertilisers. Farmers can reduce environmental damage and enhance agricultural sustainability by matching crop selections to local climatic circumstances.
3. As per claim 1, by analysing enormous volumes of complex data, AI can help farmers come to educated judgements. AI algorithms can suggest crops that are both profitable and risk- free by taking into account variables like temperature, precipitation, soil properties, and market demand.
4. As per claim 1, AI systems are capable of producing individualised crop recommendations based on specific farm factors including soil types, topography, and resource availability. Farmers may maximise productivity and profitability by selecting crops that are best suited to the unique circumstances of their farms.