Description:Field of the Invention
This invention relates to IoT & drone Technology based Health Monitoring System for Apple orchard.
Background of the Invention
In past eras, traditional ways of protecting apple orchard are very difficult and depended on extensively on manual inspection and historical data, which made it labor-intensive, time-consuming, and prone to errors. This can lead to inconsistent assessments of fruit ripeness and quality, suboptimal harvest timing, and reduced yield. In traditional systems, there may be gaps in understanding fruit growth conditions due to limited data collecting, making it challenging to precisely anticipate the ideal harvest time. Changing weather patterns and climate variability further complicate this process, impacting fruit growth and maturation. Farmers must also meet market demands for high-quality produce, which requires precise timing to ensure fruits are picked at their peak ripeness. Furthermore, maximizing yield production of fruit quality is a difficult task. Optimizing yield and maintaining sustainability require strong resources for the utilization of water and nutrients. A technologically driven agricultural revolution is necessary to solve the problems permanently at an appropriate price with the least possible negative environmental impact.
In our proposed invention, we designed a comprehensive, real-time monitoring system that integrates IoT sensors to continuously monitor key environmental and physiological parameters such as soil moisture, temperature, humidity, leaf wetness, and tree health indicators. To automate the image gathering of apple orchard various sensors and cameras used with Drone technology. WSNs is used for connecting spatially distributed autonomous sensors that monitor and record environmental and physiological conditions. These sensors communicate wirelessly, transmitting data to a central system for analysis, and finally transfer the data to the trained Convolutional Neural Network (CNN) model to detect apple diseases. This novel method significantly enhances the responsiveness and efficiency of the orchard health monitoring system.
AU2021107439 Agriculture is crucial for many countries because their economies are heavily based on farming. Some countries rely on agriculture for a large part of their GDP, but they still use manual methods to monitor crops, which can be labour-intensive and inefficient. In contrast, developing countries are increasingly using advanced technologies to boost crop yields and use resources more efficiently.
This invention proposes an integrated approach that combines IoT, machine learning, and drone technology to monitor crop health. By using these technologies together, we can gather diverse types of data that differ in what they measure and when they measure it. The spatial resolution, or the level of detail in the data, also varies between these technologies. The proposed approach aims to optimize how these different sensing technologies are integrated and put into practice.
This homegrown, technology-based agricultural solution can provide valuable insights into crop health by extracting additional information from the diverse data set. It reduces the effort needed to survey crops, which is especially useful for large agricultural areas.
RESEARCH GAP: In proposed system by using Drone technology, we capturing the high-resolution images of orchard and identifying the health of the apple by AI model, and sprinkle the pesticides over the affected area.
AU2021100538A Agriculture plays a vital role in the economies of many countries. Some nations rely heavily on farming for their GDP, but they often use manual methods to monitor crops, which can be labor-intensive and inefficient. On the other hand, developing countries are starting to use advanced technologies to boost crop yields and make better use of resources.
This new invention proposes an integrated approach to monitor crop health using IoT, machine learning, and drone technology. By combining these technologies, we can gather various types of data that differ in what they measure and when they measure it. The level of detail in the data also varies between these technologies. The proposed system aims to optimize how these different sensing technologies are integrated and put into practice.
This homegrown, technology-based agricultural solution provides valuable insights into crop health by extracting more information from the diverse data set. It reduces the effort needed to survey crops, which is especially useful for large agricultural areas.
RESEARCH GAP: In this Proposed system we captured the large area by drone with advance technology. Aerial drones fitted with edge devices capturing the high-resolution images of apple orchards, then transmits them to a central server where the Advanced CNN Training model is implemented for assessments of fruit ripeness and quality.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed. This invention relates to IoT & drone Technology based Health Monitoring System for Apple orchard.

SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
Early diagnosis and accurate identification of apple diseases can help to control the spread of infection and ensure the healthy growth of the apple industry. Convolutional Neural Networks (CNN) are among the best AI algorithms for detecting pests, diseases, and nutrient deficiencies early. Therefore, using drones (Unmanned Aerial Vehicles or Unmanned Aerial Systems) equipped with intelligent visual systems could be an efficient and cost-effective way for farmers to spot fruit diseases in various agricultural field.
Drones are effectively monitoring large apple orchards, helping maintain the growth of apples by collecting and delivering real-time images from the time the orchards emerge until harvest.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: APPLE ORCHARD WITH ADVANCED TECHNOLOGIES SUCH AS DRONE IOT WSN SERVER AND GATEWAYS
FIGURE-2 DRONE EQUIPPED WITH DIFFERENT DEVICES
FIGURE 3: RESULT
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Early diagnosis and accurate identification of apple diseases can help to control the spread of infection and ensure the healthy growth of the apple industry. Convolutional Neural Networks (CNN) are among the best AI algorithms for detecting pests, diseases, and nutrient deficiencies early. Therefore, using drones (Unmanned Aerial Vehicles or Unmanned Aerial Systems) equipped with intelligent visual systems could be an efficient and cost-effective way for farmers to spot fruit diseases in various agricultural field.
Drones are effectively monitoring large apple orchards, helping maintain the growth of apples by collecting and delivering real-time images from the time the orchards emerge until harvest.
Drones are very essential for predicting health monitoring system by providing aerial imaging and remote sensing capabilities. The drones used in this system have advanced edge computer capabilities, cameras, and sensors, such as:
High-Resolution Cameras:
Through high-resolution camera, we capture detailed aerial images of apple in the orchard, enabling visual inspection of tree health and fruit condition.
Multispectral and Thermal Sensors: Through these sensors can detect changes in temperature, moisture content, and plant health that are invisible to the unaided eye.
Edge Computing Hardware Real-time image analysis is achieved through onboard processing units that can be executed on convolutional neural network (CNN) models for the result.
Data Transmission (WSN)
The data collected by the IoT sensors and drones is transmitted wirelessly to a central server for further processing and analysis. Communication protocols such as, Wi-Fi, LoRa, or Zigbee to ensure reliable data transmission.
Centralised Server
In this stage, CNN models and other machine-learning algorithms are implemented to detect diseases and assess the health of the orchards before being processed for data analysis. Data has been collected from IoT sensors and drones for further studies.
Interface Hardware:
The interface hardware acts as a central point for communication between the various components of the health monitoring system, making it a crucial element. The essential components of interface hardware are as follows:
Sensor Integration:
The interface hardware connects to various types of sensors throughout the orchard, such as temperature and humidity, leaf wetness, soil moisture, and pest detection. After collecting all the data from the sensors, it transmits to the central processing unit.
Drone Communication:
The interface hardware enables communication between the drones and the ground-based sensors. Drones are fitted with multispectral sensors and high-resolution cameras fly over the orchard to collect data and high-quality images. This data is received by the interface hardware, which integrates with ground-based sensor data for further analysis.
Data Processing and Transmission:
The interface hardware includes a data processing unit that preprocesses the collected data, filtering out noise and ensuring data integrity. This processed data is then transmitted to a cloud-based server or local data storage for further analysis using machine learning algorithms and predictive models.
User Interface:
The interface hardware offers a user-friendly interface for agricultural experts and orchard managers. Through this interface, users may view historical data and analytical reports, monitor real-time data, and receive warnings and messages about potential issues. The interface can be accessed through Laptops, tablets, or cell phones.
Power Management:
The interface hardware has power management functions, such as solar power and battery backup, to ensure uninterrupted. This ensures that the system remains operational even in remote areas with limited access to electricity.
Trained CNN Models: There are few common apple diseases, such as apple scab, fire blight, and powdery mildew that can be easily identified by pre-trained models
The drones are already programmed to follow predefined flight patterns, ensuring that the entire orchard is covered during each flight. This systematic approach allows for consistent and comprehensive monitoring.
Sprinkle Mechanism: A precision application system for delivering pesticides directly to the affected areas, ensuring efficient and targeted pest control.
Work flow of Proposed Model:
Images Capturing using Drones: The deep learning CNN algorithm runs through taking inputs from camera it takes the image and converts it into the frames.
Images Storage: A large area is monitored by Drones embedded with edge devices capturing high resolution images We used a real system system to capture real-time images stores into the server.
Images Preprocessing: The initial step in this method is to extract frames from the edge devices that were obtained. Image processing classification has been done with the use of deep learning neural models. The Convolution Neural Network is well-known algorithm for its efficiency in image recognition and classification.
In recent years, advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have revolutionized the field of image-based disease detection in fruit health. A Convolutional Neural Network (CNN) takes an input image, assigns weights to different objects and features, and uses this information for object recognition. CNN is a classification algorithm that needs minimal processing. After being trained for a period of time, CNN can automatically learn features on its own.
Frames Extraction: This is the most essential phase in the process, where we extract each frame to monitor the quality of the fruit. If any disease appears on the fruit's appearance, Appropriate medication can be spread over the monitored area by drones for better growth.
The convolutional neural network identifies the various faces captured by the edge assisted vision device (Camera + RTC). Results are sent to the cloud server's database for sending response and for future reference.
About CNN Model for Apple disease detection
The improved apple disease detection model uses the EfficientNetB0 architecture, known for its efficient scaling and high performance, and is tuned to achieve high accuracy in distinguishing healthy apples from scab-affected apples. Initially, EfficientNetB0 layers are frozen to use pre-trained weights, and custom layers such as GlobalAveragePooling2D, ReLU-enabled dense layer, and Dropout layer are added for better generalization. The model is trained using data augmentation, early stopping and learning rate reduction techniques. For fine-tuning, some EfficientNetB0 layers are unfrozen and retrained with a lower learning rate. The results, visualized using accuracy/loss plots, confusion matrices and classification reports, show a significant improvement in accuracy with a target of 92% or more, demonstrating the model's robust performance in apple scab disease detection.
ADVANTAGES OF THE INVENTION
1. Through this monitoring system, we can capture high-resolution multispectral and thermal imaging, that help to improve both the yield and quality of apples.
2. No Human intervention needed.
3. Through this monitoring operating system we minimize the loss of good quality yield of fruits and secures data integrity, and supports sustainable and efficient apple orchard management.
4. All the images of apple orchard fragment are validated with the help of CNN training model
, Claims:1. A system for early diagnosis and accurate identification of apple diseases using drones, comprising:
a. At least one drone equipped with high-resolution cameras and multispectral and thermal sensors for capturing detailed aerial images and detecting changes in temperature, moisture content, and plant health;
b. Edge computing hardware onboard the drone for real-time image analysis using Convolutional Neural Network (CNN) models;
c. Wireless data transmission modules for transmitting collected data from the drone to a central server, wherein the communication protocols include Wi-Fi, LoRa, or Zigbee;
d. A centralized server configured to execute CNN models and other machine-learning algorithms for detecting apple diseases and assessing the health of apple orchards;
e. Interface hardware for integrating data from various sensors and drones, including:
f. Trained CNN models specifically for identifying common apple diseases such as apple scab, fire blight, and powdery mildew;
g. A precision application mechanism for delivering pesticides directly to affected areas based on disease detection.
2. The system as claimed in claim 1, wherein the CNN models are implemented using the EfficientNetB0 architecture, which is pre-trained and fine-tuned with custom layers for improved accuracy in apple disease detection.
3. The system as claimed in claim 1, wherein the drones are programmed to follow predefined flight patterns to ensure comprehensive coverage of the apple orchard.
4. The system as claimed in claim 1, wherein the image capturing process includes: Capturing real-time high-resolution images of the apple orchard using the drones; Storing captured images on a server; Preprocessing the images to extract frames for classification; and Utilizing CNN algorithms to classify and identify potential apple diseases based on the preprocessed images.
5. The system as claimed in claim 1, wherein the centralized server further includes:
a. A data analysis module for analyzing and visualizing the collected data, including accuracy/loss plots, confusion matrices, and classification reports;
b. A feedback mechanism for updating CNN models based on new data and improved detection accuracy.
6. The system as claimed in claim 1, wherein the interface hardware enables:
a. Integration and synchronization of data from various sensors and drones;
b. Real-time monitoring and management of apple orchard health through a user-friendly interface accessible via laptops, tablets, or smartphones;
c. Continuous operational capabilities through efficient power management.
7. A method for diagnosing apple diseases using the system as claimed in claim 1, comprising:
a. Deploying drones equipped with high-resolution cameras and multispectral sensors over the apple orchard;
b. Capturing and transmitting images and sensor data to a central server;
c. Processing and analyzing the data using CNN models on the centralized server;
d. Identifying and classifying apple diseases;
e. Providing actionable insights and recommendations for pest control and orchard management through a user interface.
8. The method as claimed in claim 7, wherein the CNN models are trained and fine-tuned to achieve an accuracy target of 92% or more in detecting apple diseases.