Rice is a highly important crop worldwide, but it is plagued by various diseases that pose significant challenges for farmers and the industry. Timely and accurate detection of these diseases is critical for preventing and controlling their spread. To address this issue, this study presents a deep learning approach that uses convolutional neural networks (CNNs) to classify images of rice plants affected by different diseases. The model is trained on a large dataset of rice images with varying diseases and is then tested on separate sets of images. Results show that the proposed approach can effectively identify various rice diseases with high accuracy, making it an excellent option for automated disease detection in rice fields.
Moreover, the Internet of Things (IoT) has gained significant traction in the agricultural sector for rice disease detection. It is imperative to detect diseases in rice crops and provide farmers with actionable advice to enhance the yield of their crops. This article aims to answer several research questions based on a survey of 20 IoT-based rice disease detection papers from 2015 to date. Additionally, we have compiled a list of rice diseases, the sensors utilized for disease detection, the algorithms and models used, and the wireless communication protocols. Farmers, IoT service providers, researchers, and policymakers can benefit from the insights presented in this post .
Introduction:
Rice is a staple food for more than half of the world's population, and it plays a significant role in food security. However, the yield and quality of rice crops are threatened by various diseases. Rice diseases can reduce the yield and quality of the crops, resulting in significant economic losses for farmers. To overcome this issue, the use of the Internet of Things (IoT) has been introduced to detect rice diseases. In this blog post, we will explore the concept of using IoT for rice disease detection and its benefits.
What is IoT and How it Works:
The Internet of Things (IoT) is a network of devices, sensors, and other technologies that communicate with each other and exchange data over the internet. IoT works by connecting these devices to the internet, which allows them to send and receive data in real-time.
In truth, our primary goal is to undertake a thorough examination of the literature that is already accessible on IoT-based rice. illness detection systems to summarize and form a broad conclusion about the present trend while also laying the groundwork for additional research.
01. What kinds of rice leaf diseases can be detected with IoT?
There are several ubiquitous rice diseases that can infect rice tissues including leaves. These diseases are discussed below:
Rice Blast: Rice This is a fungal disease caused by Pyricularia oryzae and can be detected by sensors that measure changes in leaf temperature and humidity.
Sheath Blight: This fungal disease is caused by Rhizoctonia solani and can be detected by sensors that measure changes in leaf temperature and humidity. A leaf spot is a limited, Discolored, diseased area of a leaf that is caused by fungal, Bacterial or viral rice diseases. Bacterial Leaf Blight:This disease is caused by the bacteria Xanthomonas oryzae and can be detected by sensors that measure changes in environmental conditions such as temperature, humidity, and leaf wetness.
Rice Stripe Virus: This virus can be detected by sensors that measure changes in leaf color, chlorophyll fluorescence, and leaf temperature. [web Searching]
Blossom End Rot: A water-soaked spot on the blossom end of tomato fruits is the defining symptom of blossom-end rot. This is a common problem in gardens .
Bud Blast: Bud blast occurs when an orchid bud in development begins to seem shriveled, wilted, or dry. A flower that has already flowered naturally falling off is NOT a bud burst. [web Searching] Botrytis blight: Botrytis cinerea is a fungus that causes the disease otrytis blight, Also referred to as ”gray . mold.”Particularly in cold, moist conditions,It can infect some crops, Fragile Fruits, rice, Leaf and bushes.
Brown spot: This fungus affects the coleoptile, Leaves, Leaf sheath, Panicle branches, Glumes and spikelet's. The most obvious damage is a lot of big blotches on the leaves that can kill the whole leaf. Empty grains, speckled or discolored seeds, and empty grains are all signs of an infected seed.
Peach Scab: Just peel the peach before eating if it has these kinds of black stains. The tiny black spots on this peach are the hallmark of a specific type of fungal illness and are often referred to as peach freckles, peach scab, or, in more formal circles, Cladosporium carpophilum.[web Searching]
Rust disease The fungus parasite that causes rust disease can only exist in the presence of living rices. In moderately wet conditions, Rust infections are more prevalent. Rust spreads to healthy rices by way of the spores that are carried by sick rices .
02. State-of-the-art IoT-based approaches to identify rice leaf diseases?
State-of-the-art IoT-based approaches to identify rice leaf diseases are :
• Image processing-based approach: This approach involves using image analysis algorithms to detect and classify rice leaf diseases. It requires creating a dataset of images of healthy and diseased rice leaves and then using machine learning algorithms such as convolutional neural networks (CNNs) to detect and classify the leaf diseases.
• Sensor-based approach: This approach involves collecting data from a variety of environmental sensors such as soil moisture, temperature, humidity and leaf chlorophyll content to detect and classify rice leaf diseases. This approach uses drones to capture images of rice fields, which can then be analyzed using computer vision algorithms to detect and classify diseases. This approach provides a cost-effective and efficient way of surveying large areas and identifying diseases in a short amount of time.
• Remote sensing-based approach: This approach uses satellite imagery and aerial images to detect and classify rice leaf diseases. It requires using image processing algorithms to extract features from the images and then using machine learning models to detect and classify the diseases
03. What types of sensors are utilized to gather information on rice leaf diseases?
The evolution of sensor networks and IoT has gained immense attention and developed an approach for detecting rice leaf diseases. They are:
Color Sensor: TableIIIThere are many kinds of sensors use of IoT in rice disease detection. A programmable light-torecurrence converter/sensor for shade lights is the TCS3200. In a single solid CMOS integrated circuit, A current-torecurrence converter and a programmable silicon photo diode are combined .
Temperature sensor: A temperature sensor is an electronic device that measures the temperature of its surroundings and converts the input data into electronic data, Allowing it to record, Monitor or communicate temperature changes. Examples include the DHT11, LM35, and DHT22 .
Humidity sensor: A humidity sensor is a piece of technology that measures the humidity in its immediate environment and produces an electrical signal. These two instances are DHT11 and DHT22 . Dust Sensor: A Sharp GP2Y1010AU0F is built into the basic air rice disease device known as Dust Sensor. The analog voltage output of the sensor correlates with dust density.
04. What are the wireless communication technologies/protocols are used?
Wireless communication technologies have become an increasingly popular alternative in networks. Its usage is discussed below: Wireless Communication Technology use to agriculture must employee several protocols to recognize leaf disease and acquire evidence of rice protection.
Wireless Communication protocols are GSM, GPRS, IEEE 802.11 (WiFi), IEEE 802.15.4 (ZigBee), Bluetooth.Global System for Mobile Communication GSM was once regarded as the standard for communication on a global scale. The user is informed by sending an ongoing message to the farmer’s mobile phone utilizing GPRS packet switching technology. Wireless networking technology known as Wi-Fi is utilized in rice diagnostics via computers (laptops and desktops), Mobile devices (certain phones and wearable) and other networks and video cameras. Zigbee is a wireless technology that is based on standards and intended to support low-cost, lowlatency machine-to-machine (M2M) and Internet of Things (IoT) networks. Low-data-rate, low-power applications utilize ZigBee.
05. What type of Security challenges in IoT-based rice disease detection?
A short description of challenges, limitations, future developments and security issues of IoT-enabled agricultural system are presented below: Challenges IoT Security :
• Data Security: IoT-based disease detection systems must ensure that data is securely stored and transmitted. This includes data encryption, authentication, and authorization to protect data from unauthorized access or manipulation.
• Network Security: Security measures must be implemented to protect the integrity of the network, such as firewalls, authentication, and access control.
• Device Security: Securing the devices used in the system, such as sensors and computers, is also important. This includes implementing secure boot, software updates, and secure device management.
• Data Privacy: The disease detection system must also protect the privacy of the data collected. This includes ensuring that the collected data is not used for any other purposes than intended and not shared with third parties without permission.
• Physical Security: Physical security measures must be taken to protect the system from malicious attacks, such as installing CCTV cameras and access control systems.
• Cyber Security: Cyber security measures must also be implemented to protect the system from malicious attacks, such as intrusion detection systems, malware detection systems, and patching.
06. How might IoT technology be utilized to reduce the price of crop production?
IoT technology can be used to reduce the price of crop production in a variety of ways. For example, sensors can be used to monitor soil moisture, temperature, and other environmental conditions, allowing farmers to optimize their crop management. Additionally, IoT-enabled devices such as drones can be used to monitor crop health, detect diseases, and apply fertilizers and herbicides precisely and efficiently. Automated irrigation systems, powered by IoT technology, can also be used to reduce water wastage and improve crop yields. Finally, IoT technology can be used to track and monitor crop production in real-time, allowing farmers to take proactive measures to reduce wastage and increase efficiency.
07. How many kinds of Pros and Cons of IoT in Agriculture?
Internet of things (IoT) facilitates the several advantages in day-to-day life in the agricultural sector. Some of its benefits are given below: Pros of IoT in Agriculture:
• Improved Efficiency: IoT enables farmers to monitor and control crop irrigation, soil temperature, and other environmental factors in real-time, helping them to make more informed decisions.
• Reduce Costs: By automating tasks and processes, IoT helps farmers save time and money. It also reduces the need for manual labor and expensive equipment.
• Accurate Data: IoT devices can collect data from a variety of sources, including soil moisture, weather conditions, and crop health. This data helps farmers make better decisions about what to plant and how to manage their crops.
• Water Increased Production: IoT can help maximize crop yields by providing real-time information about soil conditions, crop health, and irrigation levels. This helps farmers to optimize their production and ensure they are getting the most out of their land.
• Reduce the danger of groundwater contamination.
• This field will provide skilled career prospects.
• Create work opportunities .
As the Internet of things facilitates a set of benefits, it also creates a significant set of challenges. Some of the IoT challenges are given below: Cons of IoT in Agriculture:
• Security Risks: IoT devices are vulnerable to cyberattacks, which can lead to the loss of sensitive data or even a complete shutdown of the agricultural system.
• Privacy Issues: IoT devices collect a lot of data about the environment and the people who live and work in it. This data could be used to track and monitor people, which raises privacy concerns.
• Cost: Implementing IoT in agriculture requires significant investment in terms of hardware, software, and personnel. This can be cost-prohibitive for some farmers .
08. What factors should be taken into consideration for preventing rice diseases?
• Crop rotation: Growing different crops in the same area in different seasons can reduce the buildup of plant diseases in the soil.
• Use of resistant varieties: Planting varieties that are resistant to certain diseases can help prevent them from occurring.
• Proper soil management: Applying organic matter, such as compost, and avoiding over-tilling can help reduce the amount of disease-causing organisms in the soil.
• Sanitation: Cleaning farm equipment and tools can help prevent the spread of diseases. • Proper irrigation: Keeping the soil moist but not waterlogged can help reduce the prevalence of certain diseases.
• Timely planting: Planting rice at the right time of the year can help reduce the chances of disease outbreaks.
• Control of weeds: Weeds can harbor diseases and reduce the effectiveness of fungicides, so controlling them is important.
• Use of fungicides: Applying fungicides to the crop can help prevent certain diseases. Changing the environment, removing sick rices, or using protective insecticides are all ways to protect healthy rices. Because a single contaminated seed can cause an entire tray or even an entire greenhouse’s worth of unhealthy rices, good sanitation techniques are extremely crucial to avoid issues [Web Searching]. Leaf wetness sensor: Sharp GP2Y1010AU0F-equipped forward air rice disease device with a leaf wetness Sensor . The analog voltage output of the sensor correlates with dust density. Acoustic sensor:Noise detector An acoustic sensor is used to identify sounds. In the field, These sensors are routinely employed to detect pests. Pests are an annoyance because they harm crops and spread diseases among rices .
Using IoT for Rice Disease Detection:
IoT can be used to detect rice diseases in various ways. One of the most common ways is to use sensors that can detect the changes in the environment and the plants. These sensors can detect changes in temperature, humidity, and other environmental factors that can indicate the presence of a disease. The data collected by these sensors is sent to the cloud, where it is analyzed using machine learning algorithms to detect the disease.
Another way to use IoT for rice disease detection is to use drones equipped with cameras and sensors. These drones can fly over the rice fields and capture images of the plants. The images are then analyzed using machine learning algorithms to detect any signs of disease.
Benefits of Using IoT for Rice Disease Detection:
There are several benefits of using IoT for rice disease detection. First, it allows for early detection of diseases, which can help farmers take timely action to prevent the spread of the disease. Early detection can also reduce the use of pesticides, which can be harmful to the environment and human health.
Second, IoT can help farmers optimize their use of resources such as water, fertilizers, and pesticides. By detecting diseases early, farmers can apply pesticides and fertilizers more efficiently, reducing the overall amount used. This can save farmers money and reduce the environmental impact of farming.
Implementation Challenges:
While the use of IoT for rice disease detection has several benefits, there are also some implementation challenges that need to be addressed. One of the primary challenges is the cost of implementing IoT solutions. The sensors and other technologies required for IoT can be expensive, making it difficult for small-scale farmers to adopt these solutions.
Another challenge is the need for specialized skills to implement and maintain IoT solutions. Farmers may need to learn new skills or hire specialized personnel to set up and maintain IoT systems.
Privacy and data security is also a concern when using IoT. The data collected by IoT devices can be sensitive and needs to be protected from unauthorized access. It is crucial to ensure that the data collected is stored securely and only accessible to authorized personnel.
Future Scope:
The use of IoT for rice disease detection is still in its early stages, and there is a lot of potential for further research and development. One area that requires more attention is the development of more accurate and reliable sensors for disease detection. Additionally, more research is needed to develop machine learning algorithms that can accurately identify different types of rice diseases.
The integration of IoT with other technologies such as blockchain and artificial intelligence (AI) can further enhance the benefits of IoT for rice disease detection. For example, blockchain can be used to ensure the authenticity and security of the data collected, while AI can be used to provide more accurate disease detection and diagnosis.
Conclusion:
In conclusion, the use of IoT for rice disease detection has the potential to revolutionize the way we detect and prevent diseases in rice crops. Despite some implementation challenges, the benefits of using IoT for rice disease detection are significant and can contribute to the sustainability of rice farming and food security. With further research and development, IoT can be a valuable tool for farmers in detecting diseases early, optimizing resource use, and reducing the environmental impact of farming.
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