AI AND IOT-BASED SYSTEM FOR POST-HARVEST DISEASE CONTROL IN MAJOR AGRICULTURAL CROPS

Abstract

The present invention discloses an AI and IoT-based system for post-harvest disease control in agricultural crops. The system integrates advanced IoT sensors, including temperature, humidity, gas, and light sensors, with AI algorithms to monitor environmental factors and detect early signs of disease in stored crops. The AI system processes sensor data to predict potential disease outbreaks and triggers automated corrective actions, such as adjusting environmental conditions or activating antimicrobial systems. A user interface enables remote monitoring and control, offering real-time alerts and access to historical data. This invention provides an autonomous, efficient, and scalable solution for minimizing crop losses due to post-harvest diseases, reducing labor costs, and ensuring higher crop quality and preservation. The system is adaptable to various agricultural environments, from small-scale farms to large industrial storage facilities.

Specification

Description:[015] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered 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 spirit, and scope of the present disclosure as defined by the appended claims.
[016] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.
[017] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.
[018] Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[019] The word "exemplary" and/or "demonstrative" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" and/or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements.
[020] Reference throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[021] In an embodiment of the invention and referring to Figures 1, the present invention relates to an AI and IoT-based system for post-harvest disease control in major agricultural crops. This invention leverages cutting-edge technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and novel hardware components to provide an integrated solution for disease detection and control in crops after harvest. The proposed system not only identifies diseases but also provides preventive measures using smart control mechanisms, making it an innovative solution in the agricultural sector.
[022] Post-harvest diseases are a significant problem in agriculture, causing substantial losses in crop yield and quality. Current disease control methods largely rely on manual intervention, which is time-consuming and inefficient. Furthermore, existing systems lack real-time monitoring capabilities, automated intervention, and precise detection methods for post-harvest diseases. This invention aims to address these gaps by integrating IoT sensors, AI algorithms, and novel hardware components to create an autonomous, accurate, and efficient disease control system.
[023] The AI and IoT-based system for post-harvest disease control includes several core components: IoT sensors, a central processing unit (CPU), communication modules, AI-based disease detection algorithms, a feedback control system, and a user interface (UI). Each of these components plays a crucial role in the effective functioning of the system.
[024] The IoT sensors are strategically placed in storage areas or transportation environments to monitor environmental factors such as humidity, temperature, air quality, and light levels, which are critical parameters in determining the likelihood of post-harvest diseases. These sensors are connected to a central processing unit (CPU), which collects and processes the data for disease prediction using AI algorithms.
[025] Temperature and Humidity Sensors: The system uses highly sensitive temperature and humidity sensors (e.g., DHT11 or DHT22) to monitor the storage conditions of crops. The precise measurement of temperature and humidity is crucial since these factors directly affect the proliferation of post-harvest diseases such as molds, fungi, and bacteria. These sensors feed real-time data to the CPU, where it is analyzed by the AI model.
[026] Gas Sensors: To detect the presence of gases released by diseased crops, such as ethylene and carbon dioxide, the system incorporates gas sensors like MQ-7 or CCS811. These sensors play a critical role in identifying early signs of crop decay, which may not be visible yet but can significantly affect the quality of harvested produce. The data collected from these sensors is transmitted to the AI system for analysis.
[027] Image Processing Camera: A high-resolution camera (e.g., Raspberry Pi camera or industrial-grade cameras) is used for image processing and visual recognition of post-harvest diseases. The camera captures images of the crops and uses AI algorithms (such as Convolutional Neural Networks, or CNNs) to detect visible signs of diseases like molds or spots on fruits or vegetables. The image data is processed locally or sent to the central processor for further analysis.
[028] Light Sensors: Light conditions in the storage area are monitored by light sensors (such as the BH1750 sensor), which are used to detect any abnormal lighting that may promote mold growth or spoilage. These sensors are critical in environments like warehouses where artificial lighting might be improperly regulated.
[029] The central processing unit (CPU) is the brain of the system, where all incoming sensor data is processed. The CPU can be a Raspberry Pi, an Arduino-based platform, or an industrial-grade microprocessor depending on the complexity of the deployment.
[030] The CPU is connected to the various sensors via a communication module such as Wi-Fi, Zigbee, or LoRaWAN. These communication protocols ensure that sensor data is transmitted in real-time to the processing unit without any loss or delay. In the case of a multi-farm or industrial setup, long-range IoT communication protocols such as LoRaWAN can be used to connect remote sensors in large storage facilities or agricultural sites.
[031] Once data is collected, the system uses AI-based algorithms to process and analyze the input data. Machine learning models such as Support Vector Machines (SVM), Random Forest, and deep learning models like CNN are trained using a large dataset of crop images, environmental conditions, and disease reports.
[032] The AI model continuously learns from incoming data, improving its accuracy in detecting diseases over time. For example, if the system detects increased humidity and temperature levels, the AI will analyze the probability of diseases such as mold or fungi based on historical data. Once the likelihood exceeds a predefined threshold, the system can issue warnings or activate preventive measures, such as controlling humidity or air circulation.
[033] The automated control system is integrated with the AI model to perform corrective actions based on the predictions made by the AI. If a potential disease is detected, the system can automatically adjust environmental parameters like temperature and humidity to prevent the further spread of disease.
[034] For instance, if the AI model predicts a high probability of fungal growth, it can activate the ventilation system, adjust the humidity levels, or even trigger an antimicrobial spray system to mitigate the risk. This feedback loop ensures that the environment is continuously monitored and adjusted in real-time, minimizing human intervention.
[035] The system can also be integrated with robotic actuators or drones that can be deployed for tasks such as spraying pesticides or fungicides in response to disease predictions, making the system fully autonomous.
[036] A user interface (UI) allows farmers or agricultural managers to remotely monitor the system's performance. The UI can be an Android or web-based application that displays real-time data from the sensors, alerts about potential disease threats, and control options for adjusting environmental settings manually or automatically.
[037] The UI also allows users to access historical data, such as trends in humidity and temperature, to analyze long-term patterns and make informed decisions about crop storage and handling.
[038] The system incorporates a reliable power supply setup, typically using a combination of renewable energy sources such as solar panels and backup batteries. This ensures that the system remains operational in remote areas with limited access to grid power.
[039] The system is designed with seamless integration of its hardware and software components. The IoT sensors (temperature, humidity, gas, light, and cameras) collect environmental data and send it to the central processor via wireless communication protocols like Zigbee or Wi-Fi. The processor is responsible for analyzing the sensor data using machine learning models, and based on predefined thresholds, the system can trigger automated interventions.
[040] For instance, if the temperature exceeds a certain threshold, the AI algorithm will predict the likelihood of mold formation and trigger the ventilation system to reduce humidity. Similarly, the image processing unit will analyze visual data to detect any physical signs of decay or disease and alert the user.
[041] The novelty of this invention lies in the integration of AI with IoT for real-time, autonomous disease control in post-harvest environments. The use of advanced sensors (including gas sensors and high-resolution cameras) coupled with AI-driven disease detection provides an unprecedented level of accuracy and efficiency. The system's ability to automate responses to disease threats ensures that crops are protected with minimal human intervention, reducing labor costs and preventing large-scale crop losses.
[042] Tables below illustrate the efficacy of the system in preventing post-harvest diseases by comparing it with traditional methods.

[043] The above table highlights the superior efficiency, accuracy, and effectiveness of the AI and IoT-based system.
[044] The AI and IoT-based system for post-harvest disease control represents a significant advancement in agricultural technology. By integrating advanced sensors, AI, and automated control systems, this invention provides an intelligent solution for disease detection and prevention, offering improved efficiency, reduced labor, and enhanced crop preservation. This system is scalable, adaptable, and can be deployed across different agricultural environments, from small-scale farms to large industrial warehouses.
[045] The innovative combination of hardware and software components, together with the real-time, autonomous functionality, makes this system a highly valuable tool for ensuring the quality and sustainability of agricultural produce in post-harvest conditions. , Claims:1. An AI and IoT-based system for post-harvest disease control in major agricultural crops, comprising:
a) IoT sensors for monitoring environmental parameters such as temperature, humidity, air quality, light, and gas concentrations in storage or transportation environments;
b) a central processing unit (CPU) for processing sensor data and predicting disease risks;
c) AI algorithms for analyzing sensor data and detecting potential post-harvest diseases;
d) a feedback control system for adjusting environmental parameters based on the predictions of the AI algorithms.
2. The system as claimed in claim 1, wherein the IoT sensors include temperature and humidity sensors, gas sensors, image processing cameras, and light sensors, each providing real-time data to the CPU for disease prediction.
3. The system as claimed in claim 1, wherein the AI algorithms include machine learning models selected from the group consisting of Support Vector Machines (SVM), Random Forest, and Convolutional Neural Networks (CNN), and are trained using a dataset of crop images, environmental conditions, and historical disease reports.
4. The system as claimed in claim 1, wherein the feedback control system is configured to automatically adjust environmental conditions, including temperature, humidity, and ventilation, in response to predicted disease risks based on AI analysis.
5. The system as claimed in claim 1, further includes a user interface (UI) that enables remote monitoring and control of the system, including access to real-time sensor data and alerts for potential disease threats.
6. The system as claimed in claim 5, wherein the UI allows users to manually or automatically adjust environmental settings and access historical data for trend analysis and decision-making.
7. The system as claimed in claim 1, wherein the system is powered by a combination of renewable energy sources, including solar panels, and backup batteries to ensure continuous operation, particularly in remote areas.
8. The system as claimed in claim 1, wherein the central processing unit is connected to the IoT sensors via wireless communication protocols selected from the group consisting of Wi-Fi, Zigbee, and LoRaWAN, to enable real-time data transmission.
9. The system as claimed in claim 1, further comprising robotic actuators or drones for autonomously carrying out disease control measures, including the spraying of pesticides or fungicides based on AI predictions.
10. A method of controlling post-harvest diseases in agricultural crops using the system of claim 1, comprising the steps of:
i. monitoring environmental conditions in storage or transportation areas using IoT sensors;
ii. analyzing the sensor data with AI algorithms to predict the likelihood of disease;
iii. adjusting environmental parameters in real-time based on AI predictions to prevent the occurrence or spread of post-harvest diseases.

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