Description:BACKGROUND
Field of Invention
[001] Embodiments of the present invention generally relate to a rice plant disease prediction system and particularly to a system for real-time rice plant disease detection using an edge Artificial Intelligence.
Description of Related Art
[002] Rice holds a central role in global agriculture, with millions of smallholder farmers depending on its cultivation for livelihood and food security. Diseases such as blast, bacterial leaf blight, and brown spot frequently affect rice crops and cause substantial losses when diagnosis and response do not occur swiftly. Farmers in many regions depend on visual inspections to detect such diseases, which often produce unreliable results and require expert knowledge that remains unavailable in remote locations.
[003] Technology-based solutions have emerged to assist in agricultural monitoring. These include cloud-based applications, mobile AI platforms, and sensor-driven networks. While these tools improve detection accuracy and deliver predictive insights, they often depend on high-speed internet and expensive hardware, which limits their adoption in rural settings. Additionally, the accuracy of these systems varies when environmental diversity, multiple disease stages, and rice variants complicate recognition.
[004] Recent advancements in compact computing systems and localized data processing have offered new directions for agricultural innovation. However, limitations in device performance, energy availability, and compatibility with field conditions continue to hinder widespread deployment. Current systems lack robustness under harsh weather, and integration with user-friendly interfaces remains incomplete.
[005] There is thus a need for an improved and advanced system for real-time rice plant disease detection using an edge Artificial Intelligence that can administer the aforementioned limitations in a more efficient manner.
SUMMARY
[006] Embodiments in accordance with the present invention provide a system for real-time rice plant disease detection using an edge Artificial Intelligence. The system comprising an image acquisition unit adapted to receive images of rice leaves from an electronic device. The electronic device is installed in a preset pattern across a farmland. The system further comprising a data acquisition unit adapted to receive environmental data from adetection unit. The detection unit is adapted to install in the preset pattern across the farmland. The system further comprising a communication unit adapted to establish a communication link between the electronic device, the image acquisition unit, and the data acquisition unit. The system further comprising a processing unit in communication with the communication unit. The processing unit is configured to receive the images of the rice leaves and the environmental data; pre-process the received images of the rice leaves for carrying out a visual sanitization; identify irregularities in the rice leaves from the pre-process images of the rice leaves. The irregularities are selected from spots, discoloration, unusual patterns, or a combination thereof; create a matrix of the identified irregularities with the received environmental data by executing an on-device Artificial Intelligence (AI) model; compare the matrix with a dataset comprising pretrained images of the rice leaves along with a sample set of environmental data; and identify a disease corresponding to the irregularities and the environmental data in the matrix; and transmit data related to the identified disease to the electronic device.
[007] Embodiments in accordance with the present invention further provide a method for real-time rice plant disease detection using an edge Artificial Intelligence. The method comprising steps of receiving images of rice leaves and environmental data from an image acquisition unit and a data acquisition unit respectively; pre-processing the received images of the rice leaves for carrying out a visual sanitization; identifying irregularities in the rice leaves from the pre-process images of the rice leaves. The irregularities are selected from spots, discoloration, unusual patterns, or a combination thereof; creating a matrix of the identified irregularities with the received environmental data by executing an on-device Artificial Intelligence (AI) model; comparing the matrix with a dataset comprising pretrained images of the rice leaves along with a sample set of environmental data; identifying a disease corresponding to the irregularities and the environmental data in the matrix; and transmitting data related to the identified disease to an electronic device.
[008] Embodiments of the present invention may provide a number of advantages depending on their particular configuration. First, embodiments of the present application may provide a system for real-time rice plant disease detection using an edge Artificial Intelligence.
[009] Next, embodiments of the present application may provide a system for rice plant disease detection that provides immediate disease detection directly in the field without delays, allowing farmers to take timely action and minimize crop loss.
[0010] Next, embodiments of the present application may provide a system for rice plant disease detection that performs all processing locally on the device, making it suitable for remote or rural areas where internet access is unreliable or unavailable.
[0011] Next, embodiments of the present application may provide a system for rice plant disease detection that uses affordable hardware such as Raspberry Pi or smartphones ensures that the solution remains accessible to small-scale farmers and developing regions.
[0012] Next, embodiments of the present application may provide a system for rice plant disease detection that delivers clear, easy-to-understand outputs and guidance without requiring technical knowledge, enabling widespread adoption among farmers.
[0013] Next, embodiments of the present application may provide a system for rice plant disease detection that detects diseases accurately and early, additionally promoting targeted pesticide use, reducing chemical overuse, and supporting sustainable agriculture.
[0014] These and other advantages will be apparent from the present application of the embodiments described herein.
[0015] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The above and still further features and advantages of embodiments of the present invention will become apparent upon consideration of the following detailed description of embodiments thereof, especially when taken in conjunction with the accompanying drawings, and wherein:
[0017] FIG. 1 illustrates a schematic block diagram of a system for real-time rice plant disease detection using an edge Artificial Intelligence, according to an embodiment of the present invention;
[0018] FIG. 2 illustrates a block diagram of a processing unit, according to an embodiment of the present invention; and
[0019] FIG. 3 depicts a flowchart of a method for real-time rice plant disease detection using an edge Artificial Intelligence, according to an embodiment of the present invention.
[0020] The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word "may" is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including but not limited to. To facilitate understanding, like reference numerals have been used, where possible, to designate like elements common to the figures. Optional portions of the figures may be illustrated using dashed or dotted lines, unless the context of usage indicates otherwise.
DETAILED DESCRIPTION
[0021] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the scope of the invention as defined in the claims.
[0022] In any embodiment described herein, the open-ended terms "comprising", "comprises”, and the like (which are synonymous with "including", "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of", “consists essentially of", and the like or the respective closed phrases "consisting of", "consists of”, the like.
[0023] As used herein, the singular forms “a”, “an”, and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.
[0024] FIG. 1 illustrates a schematic block diagram of a system 100 for real-time rice plant disease detection using an edge Artificial Intelligence, according to an embodiment of the present invention. In an embodiment of the present invention, the system 100 may be adapted to detect a presence of disease in received images of rice leaves. Moreover, the system 100 may classify and evaluate a stage of the detected disease in the received images of the rice leaves. Furthermore, the system 100 may train an artificially computable model for adaptive learning and disease progression prediction. Further, the training may be driven by real-time updates based on emerging disease trends. The system 100 may utilize advanced feature extraction techniques to analyze progressive changes in rice leaves in correlation with emerging diseases and/or infestations that happened in past, according to an embodiment of the present invention.
[0025] According to the embodiments of the present invention, the system 100 may incorporate non-limiting hardware components to enhance the processing speed and efficiency such as the system 100 may comprise an electronic device 102, a computer application 104, an image acquisition unit 106, a detection unit 108, a data acquisition unit 110, a communication unit 112, a processing unit 114, an on-device Artificial Intelligence (AI) model 116, and a dataset 118. In an embodiment of the present invention, the hardware components of the system 100 may be integrated with computer-executable instructions for overcoming the challenges and the limitations of the existing systems.
[0026] In an embodiment of the present invention, the electronic device 102 may be adapted to capture and upload the images of the rice leaves to the system 100. The electronic device 102 may be installed in a preset pattern across a farmland, in an embodiment of the present invention. In another embodiment of the present invention, the electronic device 102 may be a handheld device that may be carried portably and be operated manually by a user.
[0027] The images of the rice leaves may be captured under various conditions such as, but not limited to different angles, disproportionate lighting, several rotations, and so forth to ensure diversity. The electronic device 102 may be, but not limited to, a camera, a laptop, a mobile, a drone, a flood illuminator, an infrared emitter, a Raspberry Pi, a smartphone, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the electronic device 102, including known, related art, and/or later developed technologies.
[0028] The electronic device 102 may comprise the computer application 104 adapted to display the categorization of the images of the rice leaves conducted by the system 100. The categorization of the images of the rice leaves conducted by the system 100 may be healthy or diseased. Further, the computer application 104 may be adapted to display an identified disease in the images of the rice leaves. The computer application 104 may further be adapted to display recommended remedies and actions that may be carried out for combating the identified disease.
[0029] The computer application 104 may be, but not limited to, a web application, a standalone application, an Unstructured Supplementary Service Data (USSD) application, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the computer application 104, including known, related art, and/or later developed technologies.
[0030] In an embodiment of the present invention, the image acquisition unit 106 may be adapted to receive the images of the rice leaves from the electronic device 102.
[0031] In an embodiment of the present invention, the detection unit 108 may be adapted to measure and capture environmental data. The detection unit 108 may be installed in the preset pattern across the farmland. The detection unit 108 may encapsulate sensors such as, but not limited to, a temperature sensor, a humidity sensor, a soil moisture sensor, a luminosity sensor, and so forth. Embodiments of the present invention are intended to include or otherwise cover any sensors, including known, related art, and/or later developed technologies, that may be encapsulated in detection unit 108.
[0032] In an embodiment of the present invention, the data acquisition unit 110 may be adapted to capture and upload the environmental data to the system 100.
[0033] In an embodiment of the present invention, the communication unit 112 may be adapted to establish a communication link between the electronic device 102, the image acquisition unit 106, and the data acquisition unit 110.
[0034] The communication unit 112 may be, but not limited to a wired communication network, a wireless communication network, and so forth. In a preferred embodiment of the present invention, the communication unit 112 may be adapted to provide an Internet of Things (IoT) enabled communication channel. Embodiments of the present invention are intended to include or otherwise cover any type of the communication unit 112, including known, related art, and/or later developed technologies.
[0035] The wired communication network may be enabled by means such as, but not limited to, a twisted pair cable, a co-axial cable, an Ethernet cable, a modem, a router, a switch, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the means that may enable the wired communication network, including known, related art, and/or later developed technologies.
[0036] The wireless communication network may be enabled by means such as, but not limited to, a Wi-Fi communication module, a Bluetooth communication module, a millimeter waves communication module, an Ultra-High Frequency (UHF) communication module, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the means that may enable the wireless communication network, including known, related art, and/or later developed technologies.
[0037] In an embodiment of the present invention, the processing unit 114 may be in communication with the communication unit 112. The processing unit 114 may further be configured to execute computer-executable instructions to generate an output relating to the system 100. According to embodiments of the present invention, the processing unit 114 may be, but not limited to, a Programmable Logic Control (PLC) unit, a microprocessor, a development board, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the processing unit 114 including known, related art, and/or later developed technologies. In an embodiment of the present invention, the processing unit 114 may further be explained in conjunction with FIG. 2.
[0038] FIG. 2 illustrates a block diagram of the processing unit 114 of the system 100, according to an embodiment of the present invention. The processing unit 114 may comprise the computer-executable instructions in form of programming modules such as a data receiving module 200, a data preprocessing module 202, a data identification module 204, a data comparison module 206, and a data transmission module 208.
[0039] In an embodiment of the present invention, the data receiving module 200 may be configured to receive the images of the rice leaves from the electronic device 102. In an embodiment of the present invention, the data receiving module 200 may be configured to receive the environmental data from the detection unit 108. The data receiving module 200 may be configured to transmit the received images of the rice leaves and the environmental data to the data preprocessing module 202.
[0040] The data preprocessing module 202 may be activated upon receipt of the images of the rice leaves and the environmental data from the data receiving module 200. In an embodiment of the present invention, the data preprocessing module 202 may be configured to pre-process the images of the rice leaves. The preprocessing of the received images of the rice leaves may carry out a visual sanitization of the received images of the rice leaves. The preprocessing of the received images of the rice leaves may be carried out by resizing input images to a fixed dimension, normalizing pixel values, applying data augmentation, applying data normalization, flipping, rotating, brightness adjustment, contrast enhancement, noise reduction, and so forth. Embodiments of the present invention are intended to include or otherwise cover any means for preprocessing of the received images of the rice leaves, including known, related art, and/or later developed technologies. The data preprocessing module 202 may be configured to transmit the pre-processed images of the rice leaves to the data identification module 204.
[0041] The data identification module 204 may be activated upon receipt of the pre-processed images of the rice leaves. In an embodiment of the present invention, the data identification module 204 may be configured to identify irregularities in the rice leaves from the pre-process images of the rice leaves. The irregularities may be, but not limited to, spots, discoloration, unusual patterns, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the irregularities, including known, related art, and/or later developed technologies.
[0042] In an embodiment of the present invention, the data identification module 204 may be configured to create a matrix of the identified irregularities with the received environmental data by executing the on-device Artificial Intelligence (AI) model 116. The on-device Artificial Intelligence (AI) model 116 may be an edge-enabled Artificial Intelligence (AI) model. The enablement of edge may allow the on-device Artificial Intelligence (AI) model 116 to operate locally without requiring a mandatory internet connectivity. The enablement of the edge may further allow the on-device Artificial Intelligence (AI) model 116 to store the dataset 118 locally on a storage unit (not shown). The localization of the on-device Artificial Intelligence (AI) model 116 and the dataset 118 may shave off connectivity timings and latency of the system 100. Hence, making the system 100 fast and agile. Further, the on-device Artificial Intelligence (AI) model 116 may be trained for consumption of low power and efficient operation.
[0043] Upon creation of the matrix, the data identification module 204 may be configured to transmit the matrix to the data comparison module 206.
[0044] The data comparison module 206 may be activated upon receipt of the matrix from the data identification module 204. In an embodiment of the present invention, the data comparison module 206 may be configured to compare the matrix with the dataset 118. The dataset 118 may comprise pretrained images of the rice leaves along with a sample set of environmental data. Further, the data comparison module 206 may be configured to conduct a matchmaking of the matrix with the dataset 118. Upon matchmaking, the data comparison module 206 may be configured to identify the disease corresponding to the irregularities and the environmental data in the matrix.
[0045] Upon identification of the disease, the data comparison module 206 may be configured to transmit data related to the identified disease to the data transmission module 208.
[0046] The data transmission module 208 may be activated upon receipt of the identified disease from the data comparison module 206. In an embodiment of the present invention, the data transmission module 208 may be configured to transmit the data related to the identified disease to the electronic device 102. The data related to the identified disease may be, but not limited to, a name of the disease, a cause of the disease, an impact of the disease, a preventive measure for the disease, a suitable insecticide and/or pesticide for treatment of the disease, a period of recovery, and so forth. Embodiments of the present invention are intended to include or otherwise cover any data related to the identified disease, including known, related art, and/or later developed technologies. Further, the data transmission module 208 may be configured to transmit the recommended remedies and actions for combating the identified disease to the electronic device 102.
[0047] FIG. 3 depicts a flowchart of a method 300 for the real-time rice plant disease detection using the edge Artificial Intelligence, according to an embodiment of the present invention.
[0048] At step 302, the system 100 may receive the images of the rice leaves and the environmental data.
[0049] At step 304, the system 100 may pre-process the received images of the rice leaves for carrying out a visual sanitization.
[0050] At step 306, the system 100 may identify irregularities in the rice leaves from the pre-process images of the rice leaves.
[0051] At step 308, the system 100 may create the matrix of the identified irregularities with the received environmental data by executing the on-device Artificial Intelligence (AI) model 116.
[0052] At step 310, the system 100 may compare the matrix with the dataset 118 comprising the pretrained images of the rice leaves along with the sample set of environmental data.
[0053] At step 312, the system 100 may identify the disease corresponding to the irregularities and the environmental data in the matrix.
[0054] At step 314, the system 100 may transmit the data related to the identified disease to the electronic device 102.
[0055] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it is to be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0056] This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined in the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements within substantial differences from the literal languages of the claims. , Claims:CLAIMS
I/We Claim:
1. A system (100) for real-time rice plant disease detection using an edge Artificial Intelligence, the system (100) comprising:
an image acquisition unit (106) adapted to receive images of rice leaves from an electronic device (102), wherein the electronic device (102) is installed in a preset pattern across a farmland;
a data acquisition unit (110) adapted to receive environmental data from a detection unit (108) that is adapted to install in the preset pattern across the farmland;
a communication unit (112) adapted to establish a communication link between the electronic device (102), the image acquisition unit (106), and the data acquisition unit (110); and
a processing unit (114) in communication with the communication unit (112), characterized in that the processing unit (114) is configured to:
receive the images of the rice leaves and the environmental data;
pre-process the received images of the rice leaves for carrying out a visual sanitization;
identify irregularities in the rice leaves from the pre-process images of the rice leaves, wherein the irregularities are selected from spots, discoloration, unusual patterns, or a combination thereof;
create a matrix of the identified irregularities with the received environmental data by executing an on-device Artificial Intelligence (AI) model (116);
compare the matrix with a dataset (118) comprising pretrained images of the rice leaves along with a sample set of environmental data;
identify a disease corresponding to the irregularities and the environmental data in the matrix; and
transmit data related to the identified disease to the electronic device (102).
2. The system (100) as claimed in claim 1, wherein the electronic device (102) is selected from a smartphone, a camera, a drone, or a combination thereof.
3. The system (100) as claimed in claim 1, wherein the detection unit (108) encapsulates sensors selected from a temperature sensor, a humidity sensor, a soil moisture sensor, a luminosity sensor, or a combination thereof.
4. The system (100) as claimed in claim 1, wherein the identified diseases are selected from a bacterial rice leaf blight, a brown spot, a blast spot, a terminal blast, a blister, a blight infestation, a hips spot, a fungal infection, or a combination thereof.
5. The system (100) as claimed in claim 1, wherein the preprocessing of the received images of the rice leaves is carried out by resizing input images to a fixed dimension, normalizing pixel values, applying data augmentation, applying data normalization, flipping, rotating, brightness adjustment, contrast enhancement, noise reduction, or a combination thereof.
6. The system (100) as claimed in claim 1, wherein the electronic device (102) comprises a computer application (104) adapted to display the identified disease along with recommended remedies and actions.
7. The system (100) as claimed in claim 1, wherein the communication unit (112) is adapted to provide an Internet of Things (IoT) enabled communication channel.
8. A method (300) for real-time rice plant disease detection using an edge Artificial Intelligence, the method (300) characterized by steps of:
receiving images of rice leaves and environmental data from an image acquisition unit (106) and a data acquisition unit (110) respectively
pre-processing the received images of the rice leaves for carrying out a visual sanitization;
identifying irregularities in the rice leaves from the pre-process images of the rice leaves, wherein the irregularities are selected from spots, discoloration, unusual patterns, or a combination thereof;
creating a matrix of the identified irregularities with the received environmental data by executing an on-device Artificial Intelligence (AI) model (116);
comparing the matrix with a dataset (118) comprising pretrained images of the rice leaves along with a sample set of environmental data;
identifying a disease corresponding to the irregularities and the environmental data in the matrix; and
transmitting data related to the identified disease to an electronic device (102).
9. The method (300) as claimed in claim 8, comprising a step of displaying the identified disease along with recommended remedies and actions on the electronic device (102).
10. The method (300) as claimed in claim 8, wherein the detection unit (108) encapsulates sensors selected from a temperature sensor, a humidity sensor, a soil moisture sensor, a luminosity sensor, or a combination thereof.
Date: April 17, 2025
Place: Noida

Nainsi Rastogi
Patent Agent (IN/PA-2372)
Agent for the Applicant