AU2021100096A4 - Ai-based crop recommendation system for smart farming towards agriculture 5.0 - Google Patents

Abstract

AI-BASED CROP RECOMMENDATION SYSTEM FOR SMART FARMING TOWARDS AGRICULTURE 5.0 ABSTRACT: According to a report of the Food and Agriculture Organization, the world population is projected to increase another two billion by 2050, while the arable region is expected to only grow by 5%. To increase productivity in agriculture, clever and effective agricultural techniques are therefore required. The determination of land adequacy for agriculture is a basic instrument for the growth of agriculture. Agriculture is using many emerging technologies and inventions as an approach to the gathering and distribution of agricultural knowledge. The rapid growth of wireless sensor networks has led to the creation of Internet of Things (IoT) of low cost and compact sensor devices, which are a feasible method for automating and making decisions in agriculture. This study proposes an expert framework for the evaluation of agricultural land suitability, by combining sensor networks with artificial intelligence systems such as neural networks and multi-component perceptron. This innovation would help farmers determine the farmland for agriculture according to four classes of judgment, i.e. more acceptable, suitable, reasonably appropriate and unsuitable. This measurement is based on the data from the different sensor instruments used for machine training. The results achieved by MLP with four hidden layers are seen in contrast to the other current model, to be successful for the multiclass classification system. This learned model is used to evaluate potential evaluations and to identify the land after each harvest. IIPage Al-BASED CROP RECOMMENDATION SYSTEM FOR SMART FARMING TOWARDS AGRICULTURE 5.0 Diagram: Sensors Software AJ data set Impie ents Figure 1: Flow network for Al based crop management system

Description

Al-BASED CROP RECOMMENDATION SYSTEM FOR SMART FARMING TOWARDS AGRICULTURE 5.0

Diagram:

Sensors

Software

AJ data set

Impie ents

Figure 1: Flow network for Al based crop management system

AI-BASED CROP RECOMMENDATION SYSTEM FOR SMART FARMING TOWARDS AGRICULTURE 5.0

Description

Field Of Inventions: The innovation encompasses crop diseases in an intelligent technology area and in

particular a kind of artificial intelligence for agriculture, which includes an

observational process, a smart system and a computer-readable media.

Background of the invention:

Agricultural diseases and pests are a significant factor in reducing agricultural

output and profits. Moreover, because of diseases and pests of crops, they are of

more significance characteristics and scope of their incidence and intensity also

affect national economies, especially agricultural production into high losses.

Agricultural disease arises in time and follows a Preventive Approach in order to

reduce large-scale underproduction and economic failure in agriculture. Prior art

refers primarily to the identification of artificial disease.

This crop disease Detection approach is undesirable for the detection of diseases in

the early stages of plant disease, and is intended for the prophylactic therapy

II P a g e measures. Profit Manual intelligence detects the automated identification of crop conditions, effectively addresses the issue.

In Agriculture 5.0, farms adhere with the ideals of precision farming and use

machinery requiring unmanned operations and self-support for decisions.

Agriculture 5.0 thus requires the use of robotics and some kinds of AI. Tradition

has allowed farms to harvest crops and keep farms active, mostly seasonally.

However, culture has changed from a farm society with vast numbers of farm

employees to people who live in cities now; as a consequence, farmers face the

problem of a lack of manpower. Agricultural robots integrating Al features are a

response to this lack of staff.

Agricultural robots increase the number of human workers and can harvest crops in

higher amount, more rapidly than human agricultural workers, as per a

CN107392091A. While robots are not as good as people in certain instances,

agriculture develops robotic systems to support farmers with repetitive tasks to

drive agricultural systems into the modem paradigm of farming 5.0.

According to 20180262571 in some countries, the development of robotics in

agriculture greatly improved production and the cost of running agriculture has

been reduced. as already mentioned, agricultural robotic applications are growing

21 P a g e exponentially, which offers smart agricultural solutions to cope with labor shortages and a long-term decline in profitability; however, there are major restrictions like most inventions In today's early stages to deal with.

For most farmers, especially those on small farms, these technologies are still too

costly because economic scales make small individual farms less productive.

However, the cost of innovations is decreasing over time, and agricultural robots

will certainly be seen as an option to increase productivity in the future.

The concept of agricultural robotics was introduced to overcome these problems

and satisfy the rising demand for high yields. Robotic innovations are giving a

boost to the global agriculture and crop production market, as according to the

Verified Market Intelligence report, agricultural robots will be capable of

completing field tasks with greater efficiency as compared to the farmers.

Advanced equipment for sensing Farming will help solve the challenge; it provides

accurate soil details, crop status, and environmental requirements for precise use of

phytosanitary goods reduced herbicide uses, better water use and increased crop

production and productivity and quality.

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Objective of the Inventions:

• Obtain neural network model by deep learning system to the neutral net

Model is trained, and obtains the disease recognition network for different

crops.

• According to the disease recognition network of the crop specie selection

perform detection to be detected

• Obtains plant disease prevention result

Summary of the Invention:

The present innovation idea reveals that consistent farm awareness

contributes to outstanding decision-making. Agricultural management systems can

handle farm data to orchestrate performance addressing for each farm custom

solutions. This assistance in digital technologies for farmers combines powers with

artificial intelligence and robotics in order to introduce an urgent agricultural 5.0

concept.

Deep training must be provided to the users most of the advantages of

agriculture 5.0, ideally young farmers interested in learning and applying modern

technology to farming as well as the provision of future generational renewals. It

appears to be the right time for a modern, sustainable farm, capable of

demonstrating the full power of data-based management to meet the food

41Page production challenges of the 21st Century. Development to farming 5.0 for the next decade is on the agenda of most important farm equipment manufacturers and therefore the manufacturers of road equipment will play a key role if farm robots become the next, smarter generation of the crop.

Detailed Description of the Invention:

Raw measurements of key crop parameters should be processed effectively

so that numbers or images become valuable information unambiguously. Field

based crop management Precision agriculture has already evolved when it came to

light 30 years ago, but certainly the current digital information era has been

transformed.

Except in those areas traditionally the technology has not yet come, field

control is a visual production inspection plants to obtain a diagnosis by farmers and

to take steps to treat farmers to their crops.

This approach draws on knowledge in the field and the details that farmers

understand. The related farmers should also follow the cooperative's advice

Engineers or technicians working in the business of which they belong. In

farms where sophisticated technologies are available Field scheduling has been

applied, based on the operating process shown in Figure 1.

The platform refers to the physical means with which information is acquired,

being the sensors the specific elements through which objective data are obtained.

1P a g e

Data includes the information directly retrieved from the parameters measured

from the crop, soil, or ambient. Retrieving the data from the sensors can be done in

multiple ways, from inserting a pen drive in a USB port to get the files to retrieving

data from software applications synchronized to the Internet.

The nexus between the data and the decision stage involves filtering routines

and Al algorithms for getting only the right data and helping the grower make

correct decisions. Finally, actuation refers to the physical execution of an action

commanded by the decision system, and is typically carried out by advanced

equipment that can receive orders from a computerized control unit. As each action

takes place over the crop, the cycle starts and closes at crop level; the response of

the crop is then registered by specialized sensors and the loop continues

systematically until harvesting times ,which marks the end of the crop life cycle.

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Claims (5)

1. A-BASED CROP RECOMMENDATION SYSTEM FOR SMART FARMING TOWARDS AGRICULTURE 5.0

   CLAIMS:

   .. An artificial intelligence type of agriculture renders object detection process,

   defined by:

   Get picture trends and create a database of body diseases;

   Get neural model network;

   The model neural network is conditioned by a profound learning system and is

   equipped with the disease recognition network for a variety of crops;

   Get the screenshot of the crop chart to be found;;

   Get the species of crops for detection;

   Detection shall be done in conjunction with the disease identification network of

   the crop species selection;
2. 2. The Claim 1 computer in which one or more processors are located:

   IIPage identifying a multiplication of products based on data from the image, each product, the plurality of commodities, the quantity of pixels associated with the data from the imagery, the sum of pixels reaching the threshold and

   When one or more processors are to: determine the true value of the product

   quantity by means of the plurality of commodities.
3. 3. The processing of pyramids decomposition, the area sub-graph of the crop map

   image to be detected is also identified as a processing method as stated in claim 2.

   This is a method which involves carrying out the image to the crop map image to

   be detected.
4. 4. The device of claim 1, where the one or more processors are further to: cause an

   unmanned robotic vehicle to perform the action in association with the emergence

   area.
5. 5. The method of claim 4, further comprising:

   Causing a farming device to replant the emergence area based on the emergence

   value.

   21 P a g e

   AI-BASED CROP RECOMMENDATION SYSTEM FOR SMART FARMING TOWARDS AGRICULTURE 5.0

   Diagram: 2021100096

   Figure 1: Flow network for AI based crop management system