Claims:WE CLAIM:

1. Farm Management Systems (FMS) can assist farmers with a variety of collected information, by managing and controlling various tracking devices and sensors.

2. The IoT/AI SSA platform Cloud offers real-time information to farmers to facilitate decision-making and reduce operational costs, while at the same time enhancing productivity.

3. IOT for (1) searching; (2) tracking; (3) monitoring; (4) control; (5) managing; (6) evaluating; and (7) operations within a supply chain.

4. It is crucial to use automatic Radio Frequency Identification (RFID) efficient traceability systems to store and access data on electronic data chips in a more rapid and accurate manner.

Dated this 27th day of April 2021.

RUBINA AFZAL
AGENT FOR THE APPLICANTS
IN/PA No. 3586

, Description:

FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
The Patent Rules, 2003
COMPLETE SPECIFICATION
[See Section 10 and Rule 13]

1. TITLE OF THE INVENTION

“PREDICTIVE ANALYTICS BASED ON MACHINE LEARNING MODELS CAPABLE OF TRACKING VARIOUS IMPACTS ON CROP HARVEST”

2. APPLICANT

a) Dr. T. P. LATCHOUMI,
b) a national of India,
c) of SRM INSTITUTE OF SCIENCE AND TECHNOLOGY, RAMAPURAM, CHENNAI – 600 089, TAMILNADU, INDIA;

a) P JHANSI LAKSHMI,
b) a national of India,
c) of VIGNANS FOUNDATION FOR SCIENCE TECHNOLOGY AND RESEARCH, VADLAMUDI-522213, GUNTUR, ANDHRA PRADESH, INDIA;

a) Dr. T.L. YOOKESH,
b) a national of India,
c) of VIGNANS FOUNDATION FOR SCIENCE TECHNOLOGY AND RESEARCH, VADLAMUDI-522213, GUNTUR, ANDHRA PRADESH, INDIA;

a) Dr. E. D. BOOBALAN,
b) a national of India,
c) of ZAKAPPS SOFTWARE PRIVATE LIMITED, SENTHIL TOWER, ASHOK NAGAR, CHENNAI – 600083, TAMILNADU, INDIA;

a) Dr. E. BOOPATHI KUMAR,
b) a national of India,
c) of BHARATHIAR UNIVERSITY, MARUDHAMALAI, COIMBATORE – 641046, TAMILNADU, INDIA.;

a) Dr. K. SATHYA NARAYANA SHARMA,
b) a national of India,
c) of LOYOLA COLLEGE, CHENNAI 600 034, TAMILNADU, INDIA;

a) Dr. P. MANIKANDAN,
b) a national of India,
c) of LOYOLA COLLEGE, CHENNAI 600 034, TAMILNADU, INDIA;

a) K. S. KEERTHIKA,
b) a national of India,
c) of VIGNANS FOUNDATION FOR SCIENCE TECHNOLOGY AND RESEARCH, VADLAMUDI-522213, GUNTUR, ANDHRA PRADESH, INDIA;

a) Dr. BALAMURUGAN KARNAN,
b) a national of India,
c) of VIGNANS FOUNDATION FOR SCIENCE TECHNOLOGY AND RESEARCH, VADLAMUDI-522213, GUNTUR, ANDHRA PRADESH, INDIA;

a) M. SURESH,
b) a national of India,
c) of MANAKULA VINAYAGAR INSTITUTE OF TECHNOLOGY, KALLITHERTHALKUPPAM MADAGADIPET, PONDICHERRY, INDIA;

a) A. CHINNAMAHAMMAD BHASHA,
b) a national of India,
c) of VIGNANS FOUNDATION FOR SCIENCE TECHNOLOGY AND RESEARCH, VADLAMUDI-522213, GUNTUR, ANDHRA PRADESH, INDIA.

3. PREAMBLE TO THE DESCRIPTION

The following specification particularly describes the invention and the manner in which it is to be performed.

TECHNICAL FIELD OF THE INVENTION:

This platform would prove a valuable medium to facilitate data flow and sharing among SSA domains. Many researchers have developed IoT architectures, but their efforts have tended to target specific areas of IoT/AI, i.e. sensors or weather monitoring systems. This current concept is proposing a holistic IoT/AI platform to cover all areas within a SSA environment, performing the following tasks: (a) manage and govern data flow between SSA domains; (b) facilitate the integration of the different components of SSA architecture; (c) tackle interoperability issues caused by the utilization of different tools and software; (d) provide easy-to-use interfaces for interaction.

BACKGROUND OF THE INVENTION:

Agriculture forms a critical activity vital to the survival of humanity for approximately many thousands of years. This relationship has resulted in the advancement of agricultural activities, initially through the time-consuming methods of traditional agriculture. The current recent rapid increase in in the global population (predicted to rise to 8.9 billion by 2050) has now led to an urgent need to balance demand and supply through the use of new technologies to increase food production. This development places pressure on natural resources, with agriculture now consuming 70% of the world?s fresh water supply for the purposes of irrigation. Limited resources and the impact of climate change will therefore lead to considerable challenges in producing sufficient high quality food to support the population. Smart Agricultural is a global initiative to preserve resources and maintain sustainable agriculture. Recently, researchers have adopted the Internet of Things (IoT), with a number of studies emphasizing the adoption and implementation of IoT in agriculture, farming, and irrigation The use of IoT and AI technologies has the potential to result in a positive transformation of traditional agriculture, including: (a) improved use of data collected from smart agriculture sensors; (b) managing and governing the internal processes within the smart agriculture environment (including the management of the harvesting and storage of crops); (c) waste reduction and cost saving; (d) increasing business efficiency by means of automating traditional processes; and (e) improving the quality and volume of products. A major challenge is to provide farmers with the required information in a rapid manner. AI therefore has significant potential to address the urgent challenges faced by traditional agriculture. There has, over previous decades, been considerable research and application of AI, including in: (a) smart agriculture; (b) robotics; (c) agricultural optimization management; (d) automation; (e) agricultural expert systems; (f) agricultural knowledge-based systems; and (g) decision support systems.

OBJECTIVE OF THE INVENTION:

Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows.

a) IOT is primarily used in agriculture for management of agricultural products within gathered real-time data, alongside: (1) searching; (2) tracking; (3) monitoring; (4) control; (5) managing; (6) evaluating; and (7) operations within a supply chain.
b) Using of BDA in agriculture focusses on management of the supply chain of agricultural products, in order to enhance decision-making and minimize the cost of production cost.
c) Mobile computing is crucial to use automatic Radio Frequency Identification (RFID) efficient traceability systems to store and access data on electronic data chips in a more rapid and accurate manner.
d) AI technologies are now emerging to assist and improve efficiency and tackle many of the challenges facing the agricultural industry, including soil health, crop yield and herbicide-resistance.

SUMMARY OF THE INVENTION:

This concept has established the importance of employing recent and advanced computing technologies in the agricultural sector, in particularly AI and IoT. Agriculture is considered central to the survival of human beings. Supporting the current practices of traditional agriculture with recent IoT/AI technologies can improve the performance, quality and volume of production. This study has reviewed the existing IoT/AI technologies discussed within the main research journals in the area of agricultural. Furthermore, it categorized the main domains of smart, sustainable agriculture, i.e. human resources; crops; weather; soil; pests; fertilization; farming products; irrigation/water; livestock; machines; and fields. The major contribution of this idea concerns the AI/IoT technical architecture for SSA, leading to an emphasis on the research and development of a unified AI/IoT platform for SSA, to positively resolve issues resulting from the fragmentary nature of the agricultural process. Future work will include investigation of the process of implementing AI/IoT technologies for SSA by applying the proposed AI/IoT technical architecture in the form of the prototype of a unified platform on real test cases. This will identify the relevant strengthens and weaknesses for further improvement and enhancement.

BRIEF DESCRIPTION OF DRAWINGS:

Specific embodiments of the present invention will now be described, by way of example only, and with reference to the accompanying drawings in which:

Figure 1 shows how the SSA-IoT/AI platform would be used at the center of the SSA domain to facilitate business process and data flow and to share within a smart, sustainable agricultural environment.
Figure 2 demonstrates the interrelation and complexity of data flow between different Smart, Sustainable Agriculture domains.

DETAILED DESCRIPTION OF THE INVENTION:

1) Internet of Things (IoT): IoT is a technology aimed at connecting all intelligent objects within a single network, i.e. the Internet. It involves all kinds of computer technologies, both (a) hardware (i.e. intelligent boards and sensors) and (b) software (i.e. advanced operating systems and AI algorithms). Its primary target is the establishment of applications for devices, in order to enable the monitoring and control of a specific domain. It has been widely adopted in many areas, i.e. industrial business processes; home machines; health applications; and smart homes and cities. IoT connectivity encompasses people, machines, tools and locations, aiming to achieve different intelligent functions from data sharing and information exchange. However, it is primarily used in agriculture for management of agricultural products within gathered real-time data, alongside: (1) searching; (2) tracking; (3) monitoring; (4) control; (5) managing; (6) evaluating; and (7) operations within a supply chain.

2) Big Data Analytics (BDA): BDA refers to the large volume of data gathered from different datasets sources over a long period of time, i.e. sensor, Internet and business data. The datasets used in this technology surpass the computational and analytical capabilities of typical software applications and standard database infrastructure. Its primary task is to capture, store, analyze and search for data, as well as seeking to identify concealed patterns in the gathered data. Thus, BDA involved the utilization of: (a) tools, (i.e. classification and clustering); (b) techniques, (i.e. data mining, machine learning and statistical analysis); and (c) technologies (i.e. Hadoop and spark). These go beyond traditional data analytical approaches, being employed to extract beneficial knowledge from a considerable amount of data, in order to facilitate timely and accurate decision–making. However, the use of BDA in agriculture focusses on management of the supply chain of agricultural products, in order to enhance decision-making and minimize the cost of production cost. It is also employed for the analysis of the properties of different types of soil for classification and further enhancement. Furthermore, it is useful for the improved prediction and production of crops.

3) Cloud Computing (CC): CC has is a recent and rapidly growing phenomenon within IT. The Cloud is not restricted to a particular business domain, but has been implemented to underpin and support various software applications and platforms. It offers easy access to the Cloud provider?s high-performance and storage infrastructure over the Internet, with one of its main benefits being to conceal from users the complexity of IT infrastructure management defined CC as “a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, servers, storage, applications and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction”. The Cloud can be seen as high virtualization method for data center infrastructure distributed over a wide geographical area, linked by means of high bandwidth network cables providing a variety of virtualized services. These include entire infrastructures, as well as small software applications and different types of services, i.e. high-performance computing and large scalable storage services based on a pay-per-use model. CC can be divided into four main layers: (1) hardware; (2) infrastructure; (3) platform; and (4) application. The delivery of Cloud services can generally be divided into three different models: (1) Infrastructure-as-a-Service (IaaS); (2) Platform-as-a-Service (PaaS); and (3) Software-as-a-Service (SaaS).

4) Mobile Computing (MC): MC refers to infrastructure in which data processing and data storage take place externally to the mobile device. MC applications transfer computing power, processing and data storage from mobile devices in the Cloud. MC has had a considerable impact on modern daily life, due to the availability and low cost of purchasing and communication. It is now widely used in every field, including the agricultural sector, in which MC systems collect and send daily data to farmers, informing them of both the production status and weather conditions. It is crucial to use automatic Radio Frequency Identification (RFID) efficient traceability systems to store and access data on electronic data chips in a more rapid and accurate manner. It has been primarily applied to the logistics of industrial products, for the purposes of identification and to check delivery processes.

5) Artificial Intelligence (AI): AI has been employed in smart systems over a long period of time, being the science of creating intelligent machines to facilitate everyday life. AI covers many areas, including computer vision, data mining, deep learning, image processing and neural networks. AI technologies are now emerging to assist and improve efficiency and tackle many of the challenges facing the agricultural industry, including soil health, crop yield and herbicide-resistance.

AI Cloud applications fall into three main categories:
a) Robots: these are developed and programmed to handle fundamental agricultural tasks (i.e. harvesting crops) more rapidly and with a higher capacity than human workers. Examples of robotic applications include: (a) See and Spray (i.e. a weed control robot) and (b) Harvest CROO (i.e. a crop harvesting robot). Agricultural robots have the potential to become valuable AI applications, i.e. milking robots.

b) Monitoring Crop and Soil: this employs computer vision and deep-learning algorithms for processing captured data by sensors monitoring crop and soil health, i.e. the PEAT machine for diagnosing pests and soil defects, based on deep learning application known as Plantix that identifies potential defects and nutrient deficiencies in the soil. A further example is Trace Genomics, a machine learning based service for diagnosing soil defects and providing soil analysis services to farmers. This uses machine learning to provide farmers with a sense of both the strengths and weaknesses of their soil, with the emphasis being on the prevention of poor crops and optimizing the potential for healthy crop production. A SkySquirrel technology is an example of the use of drones and computer vision for crop analysis.

c) Predictive Analytics: This analysis captured data, based on machine learning models capable of tracking and predicting various environmental impacts on crop harvest, i.e. changes in weather. Examples of such AI technologies include (a) aWhere (i.e. prediction of weather and crop sustainability) and (b) Farmshots (i.e. monitoring of crop health and sustainability). Crop and soil monitoring technologies are important applications for addressing issues related to climate change. IoT/AI technologies (such as drone and satellite) that generate a large amount of data on a daily basis have the potential to enable agricultural production to forecast changes and detect opportunities. It is predicted that, over the coming years, IoT and AI applications will attract a considerable degree of interest from large industrial agricultural enterprises