Claims:We claim:
1) A system for improving agricultural productivity comprising of;
a. Creating seasonal forecasting models;
b. Carrying out farm monitoring on registration by the farmer in the internal database of the website;
c. Utilizing drones for flying on fields;
d. Capturing the images of the entire farm through the cameras using artificial intelligence;
e. Analyzing the images for identifying problem areas through computer network.;
f. Collecting all the data of soil sampling using sensors; and
g. Detecting the weeds and deciding the type of herbicide application.
2. The system as claimed in claim 1, wherein the said seasonal forecasting models predict upcoming weather patterns months ahead to assist decisions of farmers.
3.The system as claimed in claim 1, wherein the said bots can harvest crops at a higher volume and faster pace to identify and eliminate weeds, and to reduce costs for farms.
4. The system as claimed in claim 1, wherein the said system carries out weather forecasting, disease or pest identification, image recognition, disease and pest movement, machine maintenance and break-down prediction, field accessibility or harvest advisory type estimations, irrigation and water management, nutrient use and fertility recommendations.
, Description:FIELD OF THE INVENTION:
The present invention relates to harvest operations in precision agriculture. The present invention more specifically relates to a system of applying, field-level weather simulation and prediction to agricultural models to generate a series of harvest advisory outputs in a tool for supporting farm operations management.
BACKGROUND OF THE INVENTION:
Harvest operations for a variety of agricultural commodities are substantially influenced by environmental factors, such as the weather. While some weather conditions, such as precipitation, may create an obvious deterrent to harvest operations, more benign daily weather characteristics also play subtle yet significant roles. For example, many commodities require that the harvested product be at or below a product-dependent moisture threshold before they can be stably stored at ambient temperatures (at least without taking specific steps to keep the product stable, such as the maintenance of a constant airflow through the product). On the other extreme, delaying harvest for too long can result in the crop becoming overly dry, potentially exposing seeds to damage during the threshing process, or removing permissible water weight from the product. Such an occurrence of delayed harvest may result in lower crop revenue, since payments are often based on mass. Similarly, crop temperature thresholds may be a major consideration for long-term storage of some crops, for example tuberous crops such as potatoes and sugar beets, where cold conditions are advantageous.
The harvest operation itself is also often sensitive to plant, product, and soil moisture and temperature levels. For instance, green plants, or even deceased plants with a heightened moisture level, often create difficulty for harvest operations that are based on the use of a threshing action to separate the seed (or other product) from the parent plant or stalk. This can result in both yield loss due to un-threshed seeds passing through and out of the harvester, and seed damage due to the repeated or harsh threshing action that may be required. Harvesting crops at a higher moisture content than is typical for long-term storage has short-term advantages, for example for livestock feed, however, yield loss may occur due to spoilage or mold if the grain moisture is too high while stored, or damage may occur to storage structures if excessive moisture seeps from the agricultural product. Additionally, yield or grain nutrient loss can result from overly dry plants or plant parts (such as a corn cob or corn husk), for example, when harvesting corn for silage, earlage, snaplage, for high-moisture corn grain, or for other types of livestock feed. Frozen or excessively wet soils can also inhibit harvest operations for various crops, depending upon the harvest mechanism for the particular crop. Each of the product, plant, and soil moisture and temperature therefore impact both the timing and viability of harvest operations, and all of these qualities are highly influenced by complex interactions between plant and soil characteristics and environmental conditions.
So there is a need for a system for improving crop management practices. The present invention helps to produce, harvest and sell essential crops and checking defective crops and improving the potential for healthy crop production.
OBJECTS OF THE INVENTION:
An object of the present invention is to provide efficient ways to produce, harvest and sell essential crops and checking defective crops and improving the potential for healthy crop production.
Another object of the present invention is to provide a system for agro-based businesses to run more efficiently and improve crop management practices.
Yet another object of the present invention is to make adjustments for weather forecasting and disease or pest identification.
Other objects and benefits of the present invention will be more apparent from the following description, which is not intended to bind the scope of the present invention.
SUMMARY OF THE INVENTION:
Accordingly, a system for improving crop management practices is disclosed. A system for improving agricultural productivity comprising of; creating seasonal forecasting models; carrying out farm monitoring on registration by the farmer; utilizing drones for flying on fields; capturing the images of the entire farm through the cameras using artificial intelligence; analyzing the images for identifying problem areas; collecting all the data of soil sampling using sensors and detecting the weeds and deciding the type of herbicide application.
DESCRIPTION OF THE DRAWINGS:
Fig 1 is the system for improving agricultural productivity based on artificial intelligence is disclosed.
DETAILED DESCRIPTION OF THE INVENTION WITH RESPECT TO DRAWINGS:
The present invention is a system for improving agricultural productivity based on artificial intelligence. AI-powered solutions improve quality & ensure faster go-to-market for crops. Many factors such as climate change, population growth & food security have propelled the industry into more innovative approaches to protect and improve crop yield. The present invention offers data sources such as temperature, precipitation, wind speed, and solar radiation, along with comparisons to historic values. The present invention improves their processes and provide with more efficient ways to produce, harvest and sell essential crops.
In one embodiment, Artificial intelligence helps to analyze farm data. With the help of artificial intelligence, farmers analyze a variety of things such as weather conditions, temperature, water usage or soil conditions collected from their farm. The present invention help farmers optimize planning to generate more yields by determining crop choices, the best hybrid seed choices and resource utilization. AI systems helps to improve harvest quality and accuracy. The present invention helps to detect diseases in plants, pests, and poor plant nutrition on farms. AI sensors can detect and target weeds and then decide which herbicides to apply within the right buffer zone. This helps to prevent over application of herbicides and excessive toxins that find their way in our food.
In another embodiment, the present invention helps to create seasonal forecasting models to improve agricultural accuracy and increase productivity. These models are able to predict upcoming weather patterns months ahead to assist decisions of farmers. Seasonal forecasting is particularly valuable for small farms.
In another embodiment, the farm monitoring is carried out using artificial intelligence. Computer vision and deep learning algorithms process data captured from drones flying over their fields. From drones, AI enabled cameras can capture images of the entire farm and analyze the images to identify problem areas and potential improvements.
In another embodiment, traditionally farms have needed many workers to harvest crops and keep farms productive. The present invention provides AI agriculture bots which overcomes the shortage of workers. These bots augment the human labor workforce and are used in various forms. These bots can harvest crops at a higher volume and faster pace than human laborers to identify and eliminate weeds, and to reduce costs for farms by having a round the clock labor force.
The present invention recommends chatbots for assistance to the farmers. Chatbots help answer a variety of questions and provide advice and recommendations on specific farm problems. The use of AI will allow farms of all sizes to operate and function keeping our world fed and are able to run farms more efficiently to produce the fundamental staples of our dietary lifestyles.
In another embodiment, farmers are using sensors and soil sampling to collect data and this data is stored on-farm management systems that allow for better processing & analysis. Machine learning provides clients with a sense of their soil’s strengths and weaknesses. The present invention helps preventing defective crops and optimizing the potential for healthy crop production.
Artificial intelligence helps farmers to get more from the land using resources more sustainably. For eg: The farmer can save corn from grasshoppers by using AI to detect a swarm in an unexpected parcel of his field, A robotic lens zooms in on the yellow flower of a tomato seedling, Images of the plant flow into an artificial intelligence algorithm predicts how long it will take for the blossom to become a ripe tomato ready for picking & packing. the technology is also being applied in one way or another in applications such as Automated machine adjustments (combine, planter downforce, etc.), Weather forecasting, Disease or pest identification, image recognition, Disease and pest movement, Machine maintenance and break-down prediction, Field accessibility or harvest advisory type estimations, Irrigation and water management, Nutrient use and fertility recommendations, Autonomous machines or robots. While AI is still fairly recent technology, it has already made its way to mobile technology. Phone cameras can recognize the image they are taking and adjust the camera settings accordingly. AI systems have the potential to solve most challenges like climate change, infestation of pests and weeds and reduced yields.
In another embodiment, a system for improving agricultural productivity comprising of;
Creating seasonal forecasting models;
Carrying out farm monitoring on registration by the farmer in the internal database of the website;
Utilizing drones for flying on fields;
Capturing the images of the entire farm through the cameras using artificial intelligence;
Analyzing the images for identifying problem areas through computer network.;
Collecting all the data of soil sampling using sensors; and
Detecting the weeds and deciding the type of herbicide application.