IOT AND MACHINE LEARNING FOR SMART AGRICULTURE: IMPROVING CROP YIELD THROUGH AUTOMATED IRRIGATION

Abstract

IOT AND MACHINE LEARNING FOR SMART AGRICULTURE: IMPROVING CROP YIELD THROUGH AUTOMATED IRRIGATION In smart agriculture, IoT (Internet of Things) and machine learning play critical roles in enhancing crop yield through automated irrigation. IoT sensors continuously monitor field 5 conditions, such as soil moisture, temperature, and humidity, providing precise real-time data that enables optimal water management. This data feeds into automated irrigation systems, which adjust water distribution based on crop needs and environmental factors, preventing under- or over-irrigation. Machine learning models further analyze historical and current data to predict future irrigation requirements, adapting schedules to crop type, growth stage, and 10 weather conditions. Together, these technologies create a responsive, data-driven irrigation process that improves crop health, conserves water, reduces labor, and promotes sustainable farming practices. The integration of IoT and machine learning into agriculture thus holds substantial potential for increasing crop yield and enhancing resource efficiency in modern farming. 15 FIG.1

Specification

Description:IOT AND MACHINE LEARNING FOR SMART AGRICULTURE: IMPROVING CROP YIELD THROUGH AUTOMATED IRRIGATION
Technical Field
[0001]
The embodiments herein generally relate to a method for IOT and machine 5 learning for smart agriculture: improving crop yield through automated irrigation.
Description of the Related Art
[0002]
The integration of the Internet of Things (IoT) and machine learning in smart agriculture offers transformative solutions for improving crop yield, especially 10 through automated irrigation. IoT-enabled devices, such as soil moisture sensors, temperature sensors, and weather stations, are installed across fields. These sensors gather real-time data on soil moisture, temperature, humidity, light intensity, and other environmental factors. This data provides a continuous, accurate snapshot of field conditions, enabling farmers to understand the precise needs of their crops at any given 15 moment.
[0003]
Automated irrigation systems, connected to IoT sensors, respond to field data and irrigate crops based on real-time soil moisture and weather conditions. For instance, if soil moisture drops below a critical level, the system activates irrigation, ensuring plants receive just the right amount of water. These systems can also be 20 programmed to consider forecasts, allowing for preemptive adjustments, such as reducing water usage if rain is expected. Machine learning models analyze historical and real-time data to predict future irrigation needs, identify optimal watering schedules, and adjust to crop growth stages.
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[0004]
These models improve over time, learning from each irrigation cycle to maximize water efficiency and avoid under- or over-irrigation, which can harm plant health and reduce yield. By providing plants with optimal water levels, IoT and machine learning reduce water waste, decrease labor requirements, and create healthier growth conditions, directly improving crop yields. The combination of precise irrigation and 5 predictive analytics enables sustainable resource management, reducing the environmental impact of agriculture.
[0005]
Until recently, using the words AI and agriculture in the same sentence may have seemed like a strange combination. After all, agriculture has been the backbone of human civilization for millennia, providing sustenance as well as contributing to 10 economic development, while even the most primitive AI only emerged several decades ago. Nevertheless, innovative ideas are being introduced in every industry, and agriculture is no exception. In recent years, the world has witnessed rapid advancements in agricultural technology, revolutionizing farming practices. These innovations are becoming increasingly essential as global challenges such as climate change, population 15 growth together with resource scarcity threaten the sustainability of our food system. Introducing AI solves many challenges and helps to diminish many disadvantages of traditional farming.
[0006]
The modern world is all about data. Organizations in the agricultural sector use data to obtain meticulous insights into every detail of the farming process, from 20 understanding each acre of a field to monitoring the entire produce supply chain to gaining deep inputs on yield generation process. AI-powered predictive analytics is already paving the way for agribusinesses. Farmers can gather, and then process more data in less
4
time with AI. Additionally, AI can analyze market demand, forecast prices as well as
determine optimal times for sowing and harvesting.
SUMMARY
[0007]
In smart agriculture, IoT and machine learning work together to improve crop yields through automated irrigation. IoT sensors monitor real-time field conditions 5 like soil moisture, temperature, and humidity. Based on this data, automated irrigation systems deliver precise amounts of water to crops only when needed, preventing water waste and plant stress. Machine learning models analyze past and current data to predict ideal watering schedules, adapting to variables like crop type, growth stage, and weather. Together, these technologies enhance water efficiency, optimize crop growth, and support 10 sustainable farming practices.
[0008]
AI is also transforming livestock management. Drones and cameras with computer vision technology monitor cattle health remotely. These systems detect unusual behavior, identify distressed animals, and predict birthing times. This ensures better animal welfare and increases productivity in livestock farming. 15
[0009]
These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and 20 modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.
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BRIEF DESCRIPTION OF THE DRAWINGS
[0010]
The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:
[0011]
FIG. 1 illustrates a method for IOT and machine learning for smart agriculture: improving crop yield through automated irrigation according to an 5 embodiment herein; and
[0012]
FIG. 2 illustrates a method proposed for the improvement of agriculture productivity by using artificial intelligence and blockchain technology according to an embodiment herein.
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DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0013]
The embodiments herein and the various features and advantageous details thereof are explained more Smart farming marks a significant shift in agriculture. It merges precision agriculture with advanced technologies to enhance crop yields and reduce resource consumption. This approach is reshaping the agricultural sector, addressing the 15 pressing need for increased global food production. Smart farming's foundation is built on data-driven insights. Farmers employ sophisticated sensors and IoT devices to gather real-time data on crops, soil, and weather. This information is then processed by AI algorithms, offering crucial insights for decision-making. AI plays a pivotal role in smart farming. These systems predict the best planting times, identify crop diseases early, and automate irrigation based on 20 soil moisture. This leads to more efficient and sustainable farming, significantly cutting down on waste and environmental harm.
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[0014]
FIG. 1 illustrates a method for IOT and machine learning for smart agriculture: improving crop yield through automated irrigation according to an embodiment herein. In some embodiment, Smart farming technologies are transforming agriculture, leading to significant cost reductions and efficiency gains. By adopting these innovations, farmers can implement cost-effective farming methods and boost productivity. Precision agriculture is a 5 cornerstone of smart farming. It enables farmers to cultivate more crops with less resources. This method employs advanced sensors and analytical tools to gather data on soil conditions, weather, and crop growth. Armed with this data, farmers can make strategic decisions about planting, harvesting, and fertilizing. This results in better crop yields and less waste.
[0015]
In some embodiments, AI-driven systems offer real-time insights into crop 10 conditions, optimizing farm operations. For instance, these technologies can pinpoint areas needing herbicides, reducing usage and environmental impact. Automated systems also cut down on labor costs while enhancing productivity. Agricultural automation is revolutionizing farming methods. With the global population at 7.7 billion and rising, the need for smart solutions is paramount. Labor shortages in agriculture can escalate costs by up to 50%, 15 highlighting the importance of AI-driven farm machinery.
[0016]
AI is transforming crop monitoring. Machine learning algorithms analyze satellite images and drone footage to detect crop stress, nutrient deficiencies, and pests. Continuous monitoring allows farmers to address issues promptly, enhancing crop health and yields. AI also monitors soil conditions, offering insights on pH levels, nutrient content, and 20 moisture levels to optimize fertilization.
[0017]
FIG. 2 illustrates a method proposed for the improvement of agriculture productivity by using artificial intelligence and blockchain technology according to an
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embodiment herein
. In some embodiments, One of the essential stages in the production of agricultural products is irrigation. Agricultural uses account for around 80% of the entire water supply, although the amount of water available varies considerably from region to region. The adoption of microirrigation technology takes an hour. But farmers still adhere to an old irrigation pattern, which causes a huge loss of water. Existing sprinkler systems are not suitable 5 for all crops. The height of the sprinkler irrigation is 4 feet, so it cannot irrigate crops greater than 4 feet. In drip and sprinkler irrigation systems, the pipe structure of the system spreads throughout the field, making intercultural operations difficult. Binding up the system during harvesting and sawing new crops leads to damage to pipes as well as to crops and a highly labor-consuming process. Damage due to rodents is more common in fixed-set irrigation 10 systems. The center pivot irrigation system and the linear move irrigation system are constructed of heavy pipes and are complexly structured. They are not suitable for fields of unusual shapes because of their high capital and maintenance requirements. To overcome these problems, we proposed a hybrid irrigation system that would be able to automatically irrigate different crops from a remote location. The objective of the proposed hybrid irrigation system 15 is to overcome the problems of existing systems.
[0018]
In some embodiments, This research has developed a smart irrigation system that automatically waters crops without human involvement. As a center pivot and linear move irrigation system, it also irrigates crops with vertical sprinkles, but in a center pivot and linear move irrigation system, farmers need heavy machinery, power supply, or human resources to 20 move the system in the field to meet the irrigation requirements. The proposed irrigation system is a stable and user-friendly model. It is designed in a T shape, which is fixed in the field with some distance, and irrigates the field with sprinklers. The system uses moisture
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sensors to measure the moisture content of the soil in fields. When the moisture levels drop
below the minimum level, the NodeMCU board activates the water pump, providing water to the crop. The water sensor also monitors the conditions of the water reservoir, sending signals to the NodeMCU when the reservoir is empty.
[0019]
In some embodiments, Wind speed sensors measure the wind speed on the farm, 5 while rain sensors detect the rain status in the field. The data from soil moisture sensors are displayed on an LCD screen. Solenoid valves are embedded to control water flow in different farms. If soil moisture drops below the threshold value for farm field A, the solenoid valves are activated, while the remaining valves remain off. This irrigation system helps farmers irrigate their fields with less labor and time. The hybrid irrigation system is built with the ability 10 to automatically irrigate crops, considering factors such as weather conditions, temperature, humidity, and soil moisture. Taxonomy is used to consider the choice of climatic and soil conditions, while our sensor network keeps track of factors, such as temperature, humidity, and soil moisture. In contrast to the three levels of conventional IoT design (application layer, network layer, and perception layer), our proposed IoT architecture comprises four layers: 15 application layer, processing layer, transport layer, and perception layer.
[0020]
Sensors are included in the physical layer, often referred to as the perception layer, to collect data such as soil moisture level, air humidity and temperature, rainfall level, and wind speed. Using networks like Wi-Fi, 2G, 3G, and LAN, sensing data that has already been acquired is sent from the transport layer to the processing layer. The transport layer sends 20 massive amounts of data to the processing layer, which stores, analyzes, and processes it. It uses modern tools, such as cloud servers and the IoT. The main goal of the application layer is to provide user-specific application services. Our system manages the sensors, GSM module,
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ThingSpeak cloud server, IoT server, Android application (Blynk application), and other
components. We have been able to prepare our system for complete autonomy thanks to these technologies. , Claims:I/We Claim:
1.
A method for improving crop yield through automated irrigation, the method 1 comprises: 2
providing precise, real-time data on field conditions, allowing irrigation systems 3 to respond accurately to crop needs; 4
predicting optimal watering schedules based on environmental and historical data, 5 which optimizes resource use and enhances plant health; 6
reducing water waste, prevent over- or under-irrigation, and support sustainable 7 farming, ultimately resulting in higher, more reliable crop yields; and 8
directly increasing yield potential. Crops remain hydrated during critical growth 9 stages, which prevents stress and leads to higher productivity.

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