INTEGRATING BIOTIC AND ABIOTIC STRESS DETECTION IN CROPS THROUGH IOT AND CLOUD COMPUTING

Abstract

The production and quality of crops grown in modem agriculture are affected by biotic and abiotic stressors. We present an approach to the problem of agricultural stress detection by combining the Internet of Things (IoT) with cloud computing. To efficiently monitor and control biotic and abiotic stress in crops, the system merges IoT technologies with cloud computing. It has several sensors that can detect things like electrical conductivity (EC), pH, light intensity, soil moisture, temperature, and humidity. An Arduino MKR Wi-Fi 1010 controller handles the data processing and algorithm execution for these sensors. A Dragino LoRa Shield for Arduino connects to the Arduino board, allowing sensor data to be sent over long distances using the LoRaWAN protocol. The communication module sends data to a central server or cloud platform, which receives it via a LoRaWAN gateway. Here, more analysis and visualization are performed on the data. Dashboards on the web or mobile apps make the processed data available to fanners and agronomists. They can take prompt remedial activities, like modifying irrigation, adding nutrients, or executing insect control -measures, since they get real-time warnings and practical information about possible stress situations in crops. To detect stress, techniques in the cloud examine data streams. Farmers can intervene more quickly with early warnings, which lead to better crop management. To improve decision-making and resource allocation, the suggested method provides large-scale real-time monitoring. A more comprehensive view of crop health may be obtained by combining biotic and abiotic stress detection, which in turn allows for more targeted management approaches.

Specification

Field of Invention
With a focus on precision agriculture and crop management, this system's inventive domain is located at the interface of cloud computing, the Internet of Things .(IoT), and agriculture. By collecting data in real-time, analysing it, and providing decision-support, the system takes on the problem of monitoring and regulating biotic (such as pests and diseases) and abiotic (such as soil moisture and temperature) stress factors in crops. Agricultural crop conditions may be remotely and automatically monitored using this system, due to the integration of many sensors with IoT devices communication modules and microcontrollers. Due to farmers may recognize stress situations earlier and take prompt action to reduce crop losses and maximize yields. Using cloud computing platforms also improves the system's accessibility, scalability, and analytical capabilities. Secure data transmission to central servers or cloud platforms allows for sophisticated processing, visualization, and decision-making using data . acquired from remote sensor nodes. This gives agronomists and farmers access to practical advice and insights via user interfaces, letting them make better choices to increase crop sustainability and production. Using the IoT and cloud computing to solve the complicated problems encountered by contemporary agricultural systems, the technology helps to develop precision agriculture methods.
Background of Invention
Conventional approaches to identifying and controlling crop stresses frequently depend on manual intervention and recurring inspections, which can be labour-intensive, timeconsuming, and ineffective in adapting to changing environmental conditions. The invention of cloud computing was motivated by the need to address major issues in contemporary agriculture. In agricultural fields, the integration of IoT devices, such as sensors that track temperature, humidity, soil moisture, and pest/disease activity, provides real-time data collection capabilities. These devices enable accurate and timely detection of biotic stresses (e.g., pest infestations, disease outbreaks) and abiotic stresses (e.g., drought, heat stress) by continuously updating data on crop health and environmental parameters. Cloud computing is essential because it offers a scalable platform for data processing, analysis, and storage. It makes it easier to combine data from several Internet of Things sensors, which opens new possibilities for advanced analytics and predictive modeling. Farmers can implement targeted interventions and optimize resource allocation by using the actionable insights generated by machine learning algorithms deployed in the cloud to enable automated decision-making processes. Through the proactive management of stress factors affecting crop health and yield, the system hopes to maximize agricultural productivity, reduce crop losses, and advance sustainable farming practices.
Object of Invention
• Arduino MKR Wi-Fi 1010
• Soil Moisture Sensor
• ^Temperature and Humidity Sensor
• Light Intensity Sensor
• pH Sensor
• Electrical Conductivity (EC) Sensor
• Leaf Wetness Sensor
• LoRa Shield
Summary of Invention
Through the integration of cloud computing and the Internet of Things (IoT), the precision agriculture system described in this invention offers a comprehensive solution for monitoring and managing biotic and abiotic stress factors in crops. It is a revolutionary step forward in modem farming practices. The fundamental goal of the system is to provide farmers with up-to-the-minute information on their crops' health and environmental circumstances so that they may make better choices about how to maximize output while minimizing resource use and promoting sustainability. Soil moisture, temperature, humidity, light intensity, pH levels, electrical conductivity, and leaf wetness are just a few of the crucial data points captured by a network of sensors that are strategically placed throughout agricultural fields. The sensor array is a collection of interconnected sensors that analyses agricultural data to deduce the intricate relationships between crops and their surroundings. This knowledge is crucial for efficient stress management.
The system's brain, an Arduino MKR Wi-Fi 1010 controller, works in tandem with the array of sensors without a hitch. This controller takes in information from all the sensors, compiles it, and runs complex algorithms to identify biotic and abiotic stress in crops. The Arduino board's integrated Wi-Fi allows it to talk to a LoRaWAN communication module, which in turn allows sensors to send data wirelessly to a base station or gateway across long distances. The LoRaWAN gateway is the brains of the operation; it takes in data sent out from the field and sends it on to a cloud service for processing. Using sophisticated analytics and the scalability offered by cloud computing, the platform can provide farmers with real-time actionable insights and suggestions. Through an intuitive online or mobile interface, farmers can access these insights, giving them the data they need to make quick choices that maximize crop health and output.
There are several advantages to the precision agricultural method. With the system's real-time crop condition monitoring and early stress factor detection capabilities, farmers may optimize yields while minimizing crop losses. In addition to lowering waste and environmental impact, the system helps optimize resources by enabling pest control, precise irrigation, and fertilizing. Also, the system is accessible and scalable, so it may be used in a variety of agricultural settings, from tiny family farms to huge commercial enterprises. Even farmers with limited resources may take use of its capabilities due to its cost-effective design, which makes use of inexpensive IoT devices and cloud computing resources.
Detailed Description of Invention
Equipped with a 32-bit low power ARM microprocessor and a built-in Wi-Fi module, the Arduino MKR Wi-Fi 1010 is a powerful microcontroller board. Processing sensor data, running control algorithms, and interacting with the communication module are all within its computational and connection capabilities. With its perfect combination of connection, power efficiency, and performance, the Arduino MKR Wi-Fi 1010 is tailor-made for Internet of Things (IoT) applications. Easy programming and integration with a wide range of sensors and peripherals are guaranteed by its interoperability with the Arduino ecosystem.
To avoid watering too much or too little, this sensor precisely detects soil moisture levels. Its capacitive technology guarantees dependability and longevity in agricultural settings, which are essential for sustaining robust crop development.
To comprehend how environmental factors impact crop health, accurate temperature and humidity data are crucial, and the DHT22 sensor delivers just that. It is highly preferred for agricultural applications because to its low cost, high precision, and ability to be measured with a single instrument.
To help determine the best light conditions for photosynthesis and agricultural development, BH1750 provides accurate lux measurement. Maximizing solar exposure is critical for increasing yield potential, and its direct monitoring capabilities makes it easier to evaluate and adapt planting techniques for this purpose.
The optimal availability of nutrients to crops depends on precise measurements of soil pH, which .this sensor makes possible. Its simple analog output and compatibility with microcontrollers make it easy to integrate into Internet of Things systems for managing soil acidity in real-time.
To evaluating and controlling crop salinity stress, this analog electrical conductivity meter (K=l) sensor monitors soil salt levels directly. It can be easily integrated into Internet of Things (IoT) systems to monitor and change soil conditions in real-time since it is compatible with microcontrollers and has a simple analog output.
Predicting and controlling plant diseases relies on reliable measurements of leaf surface moistufe, which PhytoSensor provides. Effective disease management tactics in agriculture are supported by the useful data provided by its direct measuring capabilities, which guides fungicide anolication and aids in disease risk assessment.
To facilitate long-range communication with the LoRaWAN protocol, the Dragino LoRa Shield offers an extension board that is compatible with Arduino. The Arduino MKR Wi-Fi 1010 is just one of several Arduino boards that may be used with its LoRa transceiver module. Agricultural settings in distant areas may benefit from the Dragino LoRa Shield's dependable long-range communication capabilities. Operating in the license-free ISM bands, it provides exceptional throughput even when dealing with dense foliage or other barriers.
Detailed Description of Drawings
(1) Figure (i) shows the Block Diagram
(2) Figure (ii) shows the Arduino MKR Wi-Fi 1010
Equipped with a 32-bit low power ARM microprocessor and a built-in Wi-Fi module, the Arduino MKR Wi-Fi 1010 is a powerful microcontroller board. Processing sensor data, running control algorithms, and interacting with the communication module are all within its computational and connection capabilities. With its perfect combination of connection, power efficiency, and performance, the Arduino MKR Wi-Fi 1010 is tailor-made for Internet of Things (IoT) applications. Easy programming and integration with a wide range of sensors and peripherals are guaranteed by its interoperability with the Arduino ecosystem.
(3) Figure (iii) shows the Soil Moisture Sensor
To avoid watering too much or too little, this sensor precisely detects soil moisture levels. Its capacitive technology guarantees dependability and longevity in agricultural settings, which are essential for sustaining robust crop development.
(4) Figure (iv) shows the Temperature and Humidity Sensor
To comprehend how environmental factors impact crop health, accurate temperature and humidity data are crucial, and the DHT22 sensor delivers just that. It is highly preferred for agricultural applications because to its low cost, high precision, and ability to be measured with a single instrument.
(5) Figure (v) shows the Light Intensity Sensor
To help determine the best light conditions for photosynthesis and agricultural development, BH1750 provides accurate lux measurement. Maximizing solar exposure is critical for increasing yield potential, and its direct monitoring capabilities makes it easier to evaluate and adapt planting techniques for this purpose.
(6) Figure (vi) shows the pH Sensor
The optimal availability of nutrients to crops depends on precise measurements of soil pH, which this sensor makes possible. Its simple analog, output and compatibility with microcontrollers make it easy to integrate into Internet of Things systems for managing soil acidity in real-time.
(7) Figure (vii) shows the Electrical Conductivity (EC) Sensor
To evaluating and controlling crop salinity stress, this analog electrical conductivity meter (K=l) sensor monitors soil salt levels directly. It can be easily integrated into Internet of Things (IoT) systems to monitor and change soil conditions in real-time since it is compatible with microcontrollers and has a simple analog output.
(8) Figure (viii) shows the Leaf Wetness Sensor
Predicting and controlling plant diseases relies on reliable measurements of leaf surface moisture, which PhytoSensor provides. Effective disease management tactics in agriculture are supported by the useful data provided by its direct measuring capabilities, which guides fungicide application and aids in disease risk assessment.
(9) Figure (ix) shows the LoRa Shield
To facilitate long-range communication with the LoRaWAN protocol, the Dragino LoRa Shield offers an extension board that is compatible with Arduino. The Arduino MKR Wi-Fi 1010 is just one of several Arduino boards that may be used with its LoRa transceiver module. Agricultural settings in distant areas may benefit from the Dragino LoRa. Shield's dependable long-range communication capabilities. Operating in the license-free ISM bands, it provides exceptional throughput even when dealing with dense foliage or other barriers.
Different Embodiment of Invention
a. Using a variety of IoT sensors to monitor temperature, humidity, soil moisture, and pest detection in real-time by placing them across fields, this technique gathers data on crop health and environmental conditions.
b. Combining data streams from various sensors, integrating different types of data through data fusion, and carrying out advanced analysis with machine learning algorithms are all possible with cloud computing.
c. Using historical and current data, predictive models are created to forecast biotic (such as pest outbreaks) and abiotic (such as drought conditions) stress events. This allows for the development of proactive management strategies.
d. Putting in place decision support systems that offer farmers practical insights and suggest the best courses of action, such as precisely timing irrigation, applying pesticides, or taking precautions to protect crops.
e. The system is designed to be both scalable and adaptable to different agricultural settings and crop types. This ensures that the system will function reliably in a range of environmental conditions and farming practices.
Application of Invention
i. Early Pest and Disease Detection: By means of ongoing surveillance, it is possible to identify pest infestations and disease outbreaks early on, allowing for prompt intervention to reduce crop damage.
ii. Optimized Irrigation Management: Farmers can save water and optimize irrigation schedules by using precise data on soil moisture levels and meteorological conditions.
iii. Enhanced Nutrient Management: This approach makes use of real-time data insights to customize fertilizer application strategies by tracking crop health indicators and soil nutrient levels.
iv. Climate Resilience: By offering predictive analytics on temperature swings, humidity levels, and other environmental factors affecting crop growth, this technology assists farmers in adapting to changing climate conditions.
v. Precision Agriculture: Promotes precision farming methods by providing crop management recommendations tailored to the specific site, leading to increased yield, quality, and resource efficiency.
vi. Decision Support Tools: Provides fanners with tools to help them make informed decisions about sustainable agricultural practices by integrating machine learning models and data analytics.
We Claim
The invention of Integrating Biotic and Abiotic Stress Detection in Crops through IoT and Cloud Computing comprises of:
1. The system aims to raise crop yield and quality by facilitating early detection and proactive management of biotic and abiotic stresses.
2. Through prompt intervention and wise resource allocation, minimize losses brought on by pests, illnesses, and unfavourable environmental conditions.
3. Encourage sustainable farming methods by minimizing the use of pesticides, increasing nutrient and water efficiency, and improving general environmental stewardship.
4. Give fanners access to tools for decision support and actionable insights derived from predictive modeling and real-time data analytics.
5. Promote accurate and site-specific farming methods that result in cost savings and effective resource management.
6. With flexibility and scalability in mind, this system can be applied widely and effectively in a range of crop types and farming conditions.

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