Elevate your farm’s efficiency with precision! Explore advanced drones for agricultural crop surveillance, optimizing yield and resource management seamlessly.
An Overview of Drone Applications in Agriculture
Unmanned aerial vehicles (UAVs), commonly known as drones, have made significant advancements over the past two decades for various applications, including surveillance, geographic studies, fire control, security, defense, search and emergency response, agriculture, and more. Using drones for agricultural crop surveillance have been altered to provide farmers with significant cost efficiencies, increased operational performance and improved returns. One of the main agricultural tasks that by using drones may improve significantly is crop monitoring or surveillance.
Drone monitoring systems assist farmers in observing aerial views of their crops. This provides information regarding water systems, soil composition, pests and disease infestations. Crop images captured by the drones contain data within the infrared and visual spectral range. These images yield various features that allow extracting information about the plants’ health in a way that is not visible to the naked eye. Another crucial aspect of this technology is its ability to monitor harvest on a regular basis, i.e. every week or even every hour. The frequent access to crop information helps farmers to take the necessary preventive measures for better crop protection.
In this article, after a brief review on crop monitoring challenges and smart agricultural applications, i.e. using artificial intelligence based technologies, we discussed artificial intelligence powered drones for agricultural crop surveillance and their benefit over human-based systems. Finally we conclude the article by emphasizing the important challenges of using done technologies for crop monitoring.
Crop Surveillance Challenges
Agriculture is the main source of food, income and employment and is a major contributor to the global economy. Food security is a challenge because the population is growing rapidly. According to official reports, the world must boost food productivity by about 50% by 2050 to feed a growing population of nine billion people. At the same time, the basic resources needed to grow crops, such as land and water, are becoming limited.
Due to low-level agriculture technology, lesser power availability, and unskilled farmers, crop monitoring involves manual human inspection using traditional ways However, this is a labor-intensive, costly, and potentially error-prone process with the following challenges:
It limits the inspector to the examination of a smaller sample of crops from a limited area of agricultural land within a specified period of time. Furthermore, the inspectors are experts on a specific agricultural process. To cover a larger area or to investigate different practices, multiple inspectors can be hired. But the cost increases.
The knowledge and skill of the inspector determines the quality of the inspection. Thus, it is possible to get less effective results that may be seriously flawed.
AI Applications in Agriculture
By enabling more efficient and sustainable farming methods, AI technology has revolutionized traditional farming practices. Here’s a look at the practical applications of AI to agriculture and how it’s changing the way crops are grown.
Crop and soil health monitoring
Machine learning and computer vision may also be used by AI to identify weeds, pests, illnesses, nutritional deficits, shortages of water, and other concerns that influence crop development. Using these modern technology, farmers obtain high-quality advice for irrigation, fertilization, and insect management. By improving soil conditions and eliminating resource waste, AI boosts yields while minimizing environmental consequences.
Detection of insect and plant diseases
AI-based systems use image recognition and machine learning algorithms to identify symptoms of illness or pests in plants, using a database of known pests and diseases. AI helps producers to recognize and respond to possible problems promptly by evaluating massive quantities of data and historical records. This method prevents disease propagation as well as yield loss.
Other applications include: monitoring the maturity of the crop, autonomous tractors, agricultural robotics, ensuring livestock health, intelligent spraying, cultivation of seeds and crop protection.
AI-Powered Drones for Agricultural Crop Surveillance
As we explained, the integration of AI in agriculture is transforming the way we manage and monitor crops. AI-enabled drones for agricultural crop surveillance are at the forefront of this revolution, overcoming human expertise limitations and providing farmers with unprecedented insights into crop health and productivity. Color and infrared photography shot by various platforms has been used to monitor crop progress for more than 50 years. Using modern image data analysis technologies, a camera-mounted drone diagnoses crops with illnesses or deficits.
Images acquired by the drone-mounted camera may be used to produce a map of vegetation indices. These indicators can be used to estimate agricultural information such as crop disease, fertilizer needs, and water stress. Vegetation indices aid in distinguishing between healthy, sick, and weedy plants. These indices are based on crop image spectrums, and crop image spectrums are connected to crop health. Harvest yield and vegetation indices assessed at various harvest phases have strong connections. These interactions play a significant role in yield monitoring.
The selection of sensors to be utilized in conjunction with drones for agricultural crop surveillance is critical for effective crop monitoring. The selection of sensors is mostly determined by their uses, such as illness diagnosis, nutrition detection, and water status identification, among others.
For example, in 2020, a UAV-based automated yellow rust disease monitoring system was suggested. The data was collected using a multispectral camera. It caught five distinct spectrum bands: RGB, additional RedEdge, and NIR. For semantic segmentation, the suggested system used U-Net. Extra band use increased image segmentation performance. The picture data was then classified using the Random Forest algorithm-based deep learning approach. In this work, the raw data is acquired using appropriate sensors, and the obtained data is processed in the data acquisition and processing blocks. Finally, cleaning and grouping transform the obtained data into useable information.
How AI-Enabled Drones Overcome Human Expertise Limitations ?
AI-enabled drones for agricultural crop surveillance overcome human expertise limitations in several ways:
Scalability and Efficiency: AI-enabled drones can rapidly cover vast expanses of land, significantly reducing the time and effort required to monitor crop health, making them a practical solution for large-scale agricultural operations.
Real-time Data Processing and Insights: By harnessing the power of AI, drones for agricultural crop surveillance can collect, process, and analyze data in real-time, providing farmers with immediate insights into crop health. These insights enable farmers to make informed decisions promptly, optimizing crop management practices for improved productivity. AI algorithms can analyze vast amounts of data from multiple sources, including drone imagery, historical data, and weather information, to provide comprehensive and accurate insights into crop health.
Precision agriculture: This is an approach to agricultural management based on observing and responding to within-crop variability. AI and machine learning in agriculture can improve crop yields through real-time sensor data and visual analytics data from drones.
Disease detection: Drones have been used for various precision agriculture applications such as pest detection, crop yield prediction, crop spraying, yield estimation, water stress detection, land mapping, crop nutrient deficiency detection, weed detection, livestock control, agricultural product protection, and soil analysis.
Predictive analytics and risk assessment: AI drones can successfully monitor crops to report the ideal time to harvest. They can image and analyze young plants and accurately predict their expected growth characteristics.
Cost-effectiveness: The cost of human labor and time often prohibits the manual cataloging of individual plants in a field. AI-enabled drones for agricultural crop surveillance, coupled with deep learning algorithms, can automatically generate detailed catalogs of crops, eliminating the need for labor-intensive manual efforts, reducing costs, and improving efficiency.
In this article we at saiwa discussed about applications of drone and artificial intelligence in one of the most agricultural tasks, i.e. crop surveillance. Although, currently the use of drones for agricultural crop surveillance is the most promising method for product monitoring, but at the same time, there are always challenges:
Data processing and analysis: There are many ways to collect data, but processing them and using appropriate and efficient algorithms is one of the constant challenges.
Cost: Crop Surveillance costs can vary depending on the monitoring method and how much a product needs monitoring, and it is mainly a major challenge for greenhouses and farmers who grow crops that require a lot of monitoring.
Infrastructure: Choosing each type and method of product maintenance requires its own infrastructure, for example, drones need infrastructure such as charging stations and data transmission networks.
In situ data collection: A major challenge for farmers is the time gap between receiving information and processing it, and accordingly, choosing a method that processes data close to real-time is very important.
Integration with existing systems: Crop Surveillance smart methods must be able to communicate and integrate with different platforms in the agricultural and greenhouse fields.
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