Description:FIELD OF INVENTION
Agriculture forms the major part of our Indian economy. In the current world, agriculture and irrigation are the essential and foremost sectors. It is a mandatory need to apply information and communication technology in our agricultural industries to aid agriculturalists and farmers to improve vice all stages of crop cultivation and post-harvest.
BACKGROUND OF INVENTION
Internet of Things (IoT) have supported researchers to implement this automation in agriculture to support farmers. ML allows farmers to improve yield make use of effective land utilization, the fruitfulness of the soil, level of water, mineral insufficiencies control pest, trim development and horticulture. Application of remote sensors like temperature, humidity, soil moisture, water level sensors and pH value will provide an idea to on active farming, which will show accuracy as well as practical agriculture to deal with challenges in the field. This advancement could empower agricultural management systems to handle farm data in an orchestrated manner and increase the agribusiness by formulating effective strategies.
SUMMARY
This work highlights contribute to an overview of the modern technologies deployed to agriculture and suggests an outline of the current and potential applications, and discusses the challenges and possible solutions and implementations. Besides, it elucidates the problems, specific potential solutions, and future directions for the agriculture sector using Machine Learning and the Internet of things.

DETAILED DESCRIPTION OF INVENTION
Monitoring systems based on artificial intelligence (AI) and wireless sensors are in high demand and give exact data extraction and analysis. The main objective of this work is to detect the most appropriate plant development parameters. This work has the concept of reducing the hazards in agriculture and promoting intelligent farming. Advancement in agriculture is not new, but the AI-based wireless sensor will push intelligent agriculture to a new standard. The research goal of this work is to improve the prediction state using image processing-based machine learning techniques. Artificial intelligence (AI) technologies have predicted the behavior of nonlinear systems and have contributed to controlling variables to improve system-operating conditions. A recent analysis highlights the emergence of artificial intelligence as part of solutions for enhanced farm productivity.
Use of WSN in Agriculture
Use of WSN in Agriculture Spatial-temporal climatic, hydrographic, pressure, movement, the wetness of the soil, eco-psychological plants, plagues, and the reporting to the farmer of optimal alternatives are possible using wireless sensor networks. It would be a tremendous boon for him to have such knowledge routinely. Automatic control equipment can be used to control irrigation, fertilization, and pest control to address adverse situations which confront farmers. Maintenance of irrigation is also one of the most crucial precise farming chores. The small parasitoid wasp Microplitis croceipes finds caterpillars attacking cotton crops by placing onto a complicated organic cocktail, which is issued when assaulted from the plant. Sensors capable of detecting this cocktail might result, with much-targeted pesticide treatments or warp introductions, in the early detection and mitigation of this attack.
Diverse elements, including soil type and temperature, differ substantially in precision farming (PA) from area to area; any irrigation system therefore must be flexible to suit these differences. Irrigation regulators are frequently costly to manage precious water resources. They are not efficient at all. Moreover, WSNs are still under improvement; for instance, they are sometimes inaccurate, delicate, and hungry for power and can easily lose contact in a hostile environment, especially in agriculture. Cultivation field surveillance is critical for agricultural effectiveness in reducing resource waste and increasing yields in activities such as irrigation and fertilization because it allows farmers to access and decide upon sound information on climate factors, soil, and plant situations and changes in plant life. Although agricultural field monitoring generally involves manpower, one-off agro weather stations, and wired sensor network systems, the high density and flexible deployment of instruments for collecting data in real time is necessary for this issue, immersed in precise farming. WSNs have been developed to provide low-cost, flexible, easy-to-use, and high-precision benefits in real time for agricultural monitoring. We highlight the applications for agriculture and farming that can be used with WSNs.
Irrigation management system
Agricultural production demands a better irrigation system to maximize water use in agriculture. Another cause for the need for an improved system is the frightening decrease in the groundwater level. This setting has a cost-effective and water-efficient method of micro-irrigation. However, depending on environmental and soil knowledge, micro irrigation efficiency may be further increased. WSNs are used as the organizing mechanism in this respect.
Farming system monitoring
Several upgraded technologies and equipment are presently being employed in agriculture. In this respect, the enhanced method for managing this equipment makes operation generally easier and allows automation for famine. Furthermore, remote surveillance devices aid better management of large-scale farms. Moreover, the system quality can be enhanced by providing extra data such as satellite photos and weather forecasts.
Pest and disease control
Increased quality of crops and minimized agricultural expense are helped by controlled utilization of pesticides and fertilizers. However, we must monitor the likelihood and presence of pests in crops to control the use of pesticides. We require information about the environment, such as temperature, moisture, and wind speed, for this purpose. A WSN can observe these occurrences independently and can anticipate them in a field of interest.
Controlled use of fertilizers
The growth of plants and their quality depends directly on fertilizer application. However, it is demanding work to optimally feed fertilizers in good fields. Monitoring of the variation in land nutrition such as nitrogen (N), phosphorous (P), potassium (K), and pH can be carried through the application of fertilizers for agriculture. The balance of soil nutrition can therefore be sustained, as well as the quality of the crop.
Ground water quality monitoring
The growing use of fertilizers and pesticides reduces groundwater quality. Control of water quality by placing sensor nodes is enhanced by wireless technology.
Remote control and diagnosis
Farm equipment like pumps, lighting, heaters, and valves in machines also can be remotely controlled and diagnosed using the Internet of Things
The system uses a temperature sensor, a humidity sensor, an optical sensor, a ground moisture sensor, a soil pH sensor, and a camera module for data collection. The field characteristics are monitored using LCD monitors and mobile applications. The sprayed chemical in the plants is controlled via the solenoid valve in Figure 1. The first image is taken from the camera and detected and displayed in the app by image infection picked, when farmers take the appropriate steps after disease identification, i.e., by using an app to spray pesticides or fertilizers to convert ON/OFF into the water [22]. The ON/OFF external devices are controlled by the relay driver. With the assistance of a sensor, farmers may also control the soil and water level in a tank. For soil condition and water level and pesticide tank measurement, four different kinds of sensors are used. These sensors comprise a sensor [18], a humidity sensor, a sensor of water, and a humidity sensor. All of these sensors have a Raspberry Pi interface. For moving the whole system, motor drivers and DC motors are used. The movable system monitors the status of the ground in different locations.

Figure 1: Block diagram for artificial intelligence-based wireless sensor for monitoring and controlling agriculture parameters.
To provide data and transport the data between devices, this IoT cloud plays an essential function. The storage is kept independently for every analysis, such as sensor output, item recognition, illnesses of plants, and predictive big data analysis. Moreover, farmers can gain knowledge through Internet services from agro experts about smarter agriculture and future forecasting. Services are designed to provide insights on crop planting, control of pesticides, and land management. In the agricultural sector, the conventional farmer may use these services to prepare himself. The server is powered by IoT devices and unbelievably easy to operate. This section comprises several sensor types, cameras, display units, microscopic controllers, and network components, like routers and switches. The sensors’ characteristics are conditioned according to the predicted duties performed by actuators. The central processing unit’s main focus is on the transmission of information between components utilized to process IoT systems.

Figure 2: Flowchart for monitoring farms
It will examine which sort of crop has been sown after the detection of the soil. It checks the health of the crop and the soil based on crops. If soil fertilization and cultivation are not troublesome, then these operations are carried out. If the plant and soil are troublesome, the problem is resolved and the farmer is encouraged to take the required measures for the crop, and chemical is sprayed on the infected plants and monitored for a few days. If the problem ends, the issue will be checked again. If no difficulty is present and the illness of the crop is cleared, the robot will not function till the next disease is detected.
DETAILED DESCRIPTION OF DIAGRAM
Figure 1: Block diagram for artificial intelligence-based wireless sensor for monitoring and controlling agriculture parameters.
Figure 2: Flowchart for monitoring farms , Claims:1. ML and IOT-based Probabilistic Method in Applied Mathematics for Agricultural Tracking Farming Systems claims a method comprising
a. Receiving, by a processor of a device, data, the data including first data and second data,
b. The first data being received from a plurality of sensor devices located on one or more farms, and
c. The second data being received from one or more devices located external to the one or more farms;
d. Creating, by the processor and using the data, a model;
e. Receiving, by the processor, sensor data,
f. The sensor data relating to a particular farm of the one or more farms;
g. Identifying, by the processor, an alert, associated with the particular farm, based on the sensor data and using the model;
h. Determining, by the processor and using the model, a recommended course of action to address the alert;
i. Providing, by the processor and to a user device associated with the particular farm, the recommended course of action;
j. Receiving, by the processor and based on providing the recommended course of action, an instruction from the user device;
k. Identifying, by the processor and based on the instruction, a network address for an irrigation system; and
l. Causing, by the processor and based on identifying the network address, the irrigation system to perform an action.

2. The method of claim 1,
a. Where, when determining the recommended course of action, the method includes:
b. Determining a plurality of recommended courses of action to address the alert,
c. Determining, for each recommended course of action of the plurality of recommended courses of action, a financial impact of performing the recommended course of action or not performing the recommended course of action, and
d. Ranking the plurality of recommended courses of action, based on determining the financial impact for each recommended course of action, to create a ranked list, and where, when providing the recommended course of action, the method includes:
e. Providing the ranked list to the user device.
3. The method of claim 1, where, when creating the model, the method includes:
a. Creating a plurality of models for a farm of the one or more farms,
b. The plurality of models including:
c. A first model that is associated with a first portion of the particular farm, and
d. A second model that is different than the first model and that is associated with a second portion of the particular farm,
e. The first portion and the second portion corresponding to different plots of the particular farm or different crops of the particular farm.