In the past decade, there has been a tactical shift in focus on applying advanced technology in agriculture. It includes data acquisition through various historical, geographical, and instrumental sources. However, it is a myth that anything with technology in agriculture is Smart Agriculture.
Technologically advanced doesn’t necessarily mean that it is an intelligent system. Intelligent systems differentiate themselves through their ability to record the data and make sense out of it. Subsequently, advanced technology in agriculture includes hardware in the Internet of Things (IoT) and Software as a Service (SaaS).
Therefore, both must capture the data and give actionable insights to manage all farm operations pre and post-harvest. The organized data covers every aspect of finance and field operations that users can monitor from anywhere in the world.
Differences Between Traditional And Intelligent Farming
- The same set of practices for crop cultivation throughout the region.
- Traditional farming requires manual maintenance of all the field and finance data separately, leading to errors.
- Application of fertilizers and pesticides throughout the field.
- Geo-tagging and zone detection are not possible.
- There is no way to predict the weather.
- The analysis of each farm to see the suitable crops and water requirements for optimization
- Early detection and application at the affected region only, saving costs
- Field and finance data are available in the same place showing the profits, yields, and patterns with simple reports.
- Satellite imagery detects the different zones in farms
- Weather analysis and prediction.
Advanced Technology in Agriculture – IoT and Robots
IoT (Internet of Things) in agriculture involves sensors, drones, and robots connected through the internet. These functions automatically and semi-automatically perform operations and gather data to increase efficiency and predictability.
With growing demands and labor shortages across the globe, agriculture automation and robots (Agribots) are starting to gain attention among farmers. Crop production decreased by an estimated rate of $3.1 billion) a year due to labor shortages in the USA alone.
Recent advancements in sensors and Artificial Intelligence (AI) technology has let machines train on their surroundings have made Agribots or agricultural robots more notable. We are now in the early stages of the Agribotics revolution, with most products still in trial phases and research and development mode.
IoT-based remote sensing utilizes sensors placed along the farms like weather stations to gather data transmitted to analytical tools for analysis. Subsequently, they monitor the crops for changes in light, humidity, temperature, shape, and size.
The data collected by sensors in terms of moisture, temperature, precipitation, and dew detection helps determine the weather pattern in farms. This helps in the cultivation of suitable crops. Additionally, the soil quality analysis helps determine the nutrient value and drier areas of farms, soil drainage capacity, or acidity, which allows adjusting the amount of water needed for irrigation and the opt most beneficial type of cultivation.
Computer imaging involves using sensor cameras installed at different corners of the farm or drones equipped with cameras to produce images that undergo digital image processing. Later, these images form the basis for quality control, disease detection, sorting and grading yield, and irrigation monitoring. When combined with machine learning, image processing uses images from the database to compare with images of crops to determine the size, shape, color, and growth quality.
Advanced Technology in Agriculture – Drones and Robots
Semi-automatic robots with arms can detect weeds and spray pesticides in the affected plants, saving up the plants and overall pesticide costs. Farmers can also use these robots in lifting and harvesting. Additionally, the farmers can navigate the heavy farming vehicles from the comfort of homes through their smartphones to perform tasks. Global Positioning Systems (GPS) can track their positions at every time.
Imaging, mapping, and surveying the farms is done by Drones equipped with cameras and sensors. They can be remotely controlled or fly automatically through software-controlled flight plans in their embedded systems, coordinating with sensors and GPS. The drone data can draw insights regarding crop health, irrigation, spraying, and planting. Additionally, it gives information about soil and field, plant counting and yield prediction, and much more.
Technology Adoption in Agriculture
One can use technology in different aspects of agriculture, such as herbicide, improved seeds, pesticides, and fertilizer. Over the years, advancements in technology have proved to be extremely useful in the agricultural sector. Presently, farmers can grow crops in areas where earlier it was not possible.
Thanks to biotechnology, it is currently possible to grow crops in such areas. Now it has been made possible by genetic engineering to introduce certain strains into the genes of crops or animals. Such efforts boost the resistance of the produce to pests and droughts.
There is a limitation on how to speed up the process of modern technological adoption in agriculture. Particularly, it requires a lot of understanding of some of the elements that influence the decision of farmers to adopt modern technology. Institutional, social, and economic factors affect how fast or slow agricultural technologies are adopted.
The land size, cost, and benefits of technology are some financial factors that determine the rate of agricultural technology adoption. Farmers’ education level, age, social groupings, and gender are some social factors that influence the probability of adoption of advanced technology in agriculture.
Role Of SaaS-Based Cloud Computing In Agriculture
Advanced technology such as Cloud-based software is used for the management of financial and field activities of farms. The farmers maintained data manually by storing lengthy records on papers before the advent of computers. This method worked fine, but it was prone to human calculation errors.
After the computer boom in the 1980s, it was not long before financial software. They used spreadsheets to maintain the economic data. Therefore, the biggest challenge that farmers faced was the inability to manage field data.
Farmers used this software to keep finance data only. Around the mid-2000s, satellite image use with tools like Raven Receiver for field zone tracking became widely used. Farmers had to implement and coordinate different tools to manage complete farm operations. With constant improvements, Agritech SaaS has become an all-in-one tool for managing all these activities and more through a single device.
Agriculture is one of the most significant applications of cloud software for data collection and retrieval. Cloud software stores a huge volume of data relating to weather cycles, crop patterns, soil quality, harvesting, and satellite imagery to provide insights with sharp accuracy and speed.
All the data associated with the farm is stored in the cloud and hence readily accessible. Thus, in the future, if the crops are infected with the same symptoms as ten years ago, users can use the data to find the remedy used at that time.
Data Processing and Analysis
Database management in cloud software ties up all the loose ends of every type of data available concerning farms to enable a higher decision-making level. All the meteorological data, market data, farm data, GIS, and water availability from past and present are analyzed thoroughly.
Post analysis, the optimum value of seeding, water, and pesticide requirements for a farm are attained. The systems also have an alert system whenever discrepancies in crop growth are detected. Hence these systems work efficiently in case of pest attacks, informing farmers with actionable data.
Data Storage and Dissemination
Data storage is the backbone of predictive analysis. Earlier, we had hardware-based data storage. Hence, it required a careful maintenance and storage. Consequently, the loss of hardware meant the permanent loss of data. However, today’s Agritech systems are mostly cloud-based.
This effectively means that one need not invest in purchasing and maintaining hardware. All the relevant information is accessible through phones, PCs, and tablets. The more data is available relating to farms, the more will be accurate detection of weather phenomena, pests, crop yield, and profits.
Benefits of Using SaaS solutions in Agriculture.
Some of the benefits of application of SaaS solutions in Agriculture
- Readily available and accessible management through smartphones, tablets and PCs
- Alert Log & Management (pest infestation, diseases etc.)
- Incorporates end-to-end solutions from farm to fork traceability
- Robust & flexible system for Farm Management
- Satellite and weather input based advisory
- Higher yields due to constantly monitored and optimized inputs.
- Traceability & Output Predictability
- Crop reports & insights – easy reporting on-the-go
- Better quality due to compliance of food standards and nutrition tracking
- Accountable & Efficient Operations
- Geo tagging for accountability & accurate predictability
- There is less waste due to customized practices accounting for precise application of resources and thus reduction in production costs
- Standard package of practices
- Adherence to Compliance & Certification
Advanced technology in agriculture focuses on the application of the acquired data and combining it from various data sources. Particularly, the analysis of data shows the bigger picture to manage all the farm’s activities.
Consequently, smart farming is a giant leap from traditional farming as it brings certainty and predictability to the table. Robotics, automation, and cloud software systems are tools for intelligent farming. Robotics, drones, and sensor equipment placed throughout the farms can collect data.
The processing of the data produces farm insights. Cloud-based software can collect the data on farms and process the data relative to weather patterns, yields, irrigation and satellite imagery, and off-farm – such as markets and dealer availability to perform predictive analysis. Cloud-based software finds applications for farmers, banks, food processing companies, insurance providers, seed production, and government.