Smart Farming An Overview

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Smart farming is an emerging concept that differs drastically from traditional farming techniques by appropriately using technology. It includes using robotics, IoT, Artificial Intelligence, and drones merged with Geo Spatial Artificial Intelligence techniques for improving the quality and quantity and of the harvest. At the same time, smart farming focuses on the optimization of human labor. The concept redefines the agricultural industry completely. 

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 Let us explore the varied aspects of smart farming and how one can benefit from it. 

What is Smart Farming? 

Smart farming is a concept which aims to make the activities associated with farming sustainable, reliable, and predictable. Although it remains one of the main objectives for the industry, achieving it is often not possible with the methods of traditional farming. However, that has become a thing of the past with technologies like sensors and actuators, robotics, IoT, and drones. 

Smart agriculture or precision farming is one of the extreme use cases of spatial data science. It includes analyzing the field’s LandSat data and satellite imagery data to predict weather forecasting, crop health, yield performance.

Smart Farming vs. Traditional Farming Techniques

Smart farming is very much different from traditional farming methods. Let us explore conventional farming vs. smart farming. 

Based on Farming Practices 

Traditional farming methods involve uniform practices for crop production across the region. Smart farming methods take a step ahead and analyze suitable crops and their water requirements, consequently ensuring resource optimization. 

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Based on Maintenance 

In traditional farming, the field data is maintained manually, which is prone to errors. There is no scope for detecting soil problems in advance. Smart farming help overcome this problem and prevent financial losses. 

Based on Use of Fertilizers and Pesticides 

Smart farming enables farmers to use pesticides and fertilizers wherever necessary. There is no such scope in traditional agriculture. Fertilizer distribution systems give recommendations based on NPK (nitrogen, phosphorus, potassium) values and yield prediction based on soil samples and farm area. 

Based on Satellite imagery 

Smart farming helps in detecting affected areas for taking corrective steps. Geo-tagging, zone detection, and related methods are not there in traditional farming. Satellites imagery data can monitor soil conditions like moisture level and ground heat for identifying ideal scenarios to grow crops. Furthermore, satellite imagery can also help monitor the natural environment for better targeting pesticides and fertilizers. 

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Based on Weather Prediction 

Traditional methods are lacking technology. Consequently farmers cannot predict the weather in scientific manner. Technology in smart farming methods helps analyze and predict temperature. Consequently, crop damage is prevented from drought or unseasonal rains. Additionally, pest attack prediction models also enable planning for the anticipated pest attacks. 

Challenges in Smart Farming

Landsat and satellite imagery data are redefining the landscape of farming and food industry. They look promisingly well for sustainable means of agriculture. Consequently, it can ensure food security. Food production will be possible without affecting environment. However, it is laden with a fair set of challenges mentioned below. 

IoT Device Enablement 

Actual benefits of IoT can be realized only when there is an increase computational power and decrease in sensor energy consumption. Additionally, more devices need to have intelligent functions related to self-management and self-configuration. Additionally, these sensors and devices needs to be cost-effective. The focus should be on device characteristics with lower profit margins. Consequently, sensors with NFC and RFID tags may not be much useful, especially if the cost of the food product is very low. 

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System Operations 

Devices having fixed standards and legacy architectures pose a challenge for widespread adoption due to compatibility factors with other systems. There is a challenge to find strategies with measures that do not require any modifications. 

Network Availability 

IoT systems requires the best connectivity options for them to perform optimally. As IoT systems work in farms located in rural and remote areas, there are challenges regarding connectivity. Consequently, there is a need for devices that can function even with less power and communication.  

Advanced-Data Capabilities 

IoT devices generate a lot of data. Consequently, the extraction of meaningful insights from them is critical. However, the level of data currently being generated is still at very early stages in the agriculture industry. The present data essentially caters to decision-support systems. However, advanced information like predictive modeling and production planning will take some time. 

Data Security 

Wherever there is data, there is always a concern about its security. Critical information related to predicting soil fertility and crop yield needs protection. Cloud computing based services are ideal for storing and processing data. Particularly, when there is data aggregation from various farms, it becomes all the more important to ensure that individual farm data remains secure. 

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What Devices are Used for Smart Farming? 

Smart farming uses a large number of devices for gathering and analyzing data. Some of them are as follows:

  • Soil testing sensors for testing the moisture content of the soil and analyzing its suitability for crop production.
  • Humidity sensors help farmers monitor crop health by checking the moisture content of the earth.
  • Permafrost testers to check and confirm the presence of permafrost surfaces in soil
  • Nutrient sensors for identifying the nutrient level of the soil.
  • Precision harvesters make harvesting activity more economical and efficient.
  • Water sprinklers for irrigating crops most optimally.
  • Drones for monitoring crop growth and optimizing the last area of agriculture operations.  

Smart Farming Techniques 

The following techniques are some of the best practices involved in smart farming with Artificial Intelligence. 

Monitoring of Remote Equipment 

A communications controller or computer is linked with the agricultural machine. The connectivity is provided to data buses and other controllers of the device.  

 The connectivity between data bus and controllers ensures that alerts generated from sensors get transmitted to the controller. It further creates summary reports and snapshots which go to central information servers. The information, after processing, finally reaches the user on the application. 

Field Mapping and Resource

Remote sensing with Sensor-based mapping is an ideal data source for applications and their study. The sensors providing information to the smartphone app can give much more benefits for the farmers. Previously, it was not possible to get advanced data or assistance from experts. 

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Predictive Analytics 

It is possible to analyze current farming as well as historical data to prepare for future yields. Satellite images assist in understanding and managing the farm’s natural environment, which gives them cues for sustainable agricultural practices. Furthermore, Yield maps help in better targeting fertilizers for improved crop production standards. Consequently, the data-driven decision-making is critical to improving the bottom line. Hence, predictive analytics assists farmers in making informed and intelligent decisions for maximum profits. 

Monitoring of Crops

It is now possible to monitor crops remotely through field data as well as remote sensor data. It helps check vital parameters like cropping intensity, crop condition, planting status, Yield, productivity, and drought prediction. 

Weather Prediction 

Due to the uneven weather patterns, crop production gets affected adversely. Statistical weather forecasts utilize historical climatic data to explain the relationship between different weather periods. Therefore, it becomes possible to predict winter temperatures based on the data of summer temperatures. 

Using Predictive Analytics in Smart Farming 

Smart farming utilizes data analytics to collect information about various farming activities. It assists in creating algorithms for sustainable and better agriculture. 

Here are the various components of predictive analytics in innovative methods of farming. 

Data Collection 

Cloud computing in agriculture supports large-scale gathering and retrieving of data from multiple sources. Data can be related to crop environment monitoring, soil conditions, crop mapping, yield information management, and satellite images. They offer insights with accuracy and excellent speed. Subsequently, the data is accessible at any time, as the data remains stored in the cloud. Consequently, farmers can use historical data to overcome problems related to crop production. 

Data Analysis 

The analysis of data helps in gathering insights that assist in better decision-making. Data related to soil moisture content, water availability, and GIS are a few examples. This information can help the Agtech industry understand the soil moisture levels, optimal water requirements, and much more. Subsequently, the system can alert the concerned person. Consequently, corrective actions can be taken when any discrepancies are detected. Additionally, the system can also warn about the possibility of a pest attack. 

Data Storage 

It is one of the vital aspects of predictive analytics. Earlier, when storage required physical infrastructure, its maintenance was challenging. Consequently, the data gets compromised easily with the occurrence of any hardware fault. The problem of data compromise can be eliminated with cloud computing systems. 

Now you do not have to invest in expensive hardware or its maintenance. Additionally, the data remains accessible to anyone all the time on their smartphones. Due to this availability of quality data in large amounts, you can have better insights for improved decision-making. 

Smart Farming Benefits Using SaaS Solutions? 

Here are a few of the advantages of using SaaS solutions in innovative methods of agriculture. 

  • It is a comprehensive and flexible system for the management of farms. 
  • Anyone can access the data anytime.
  • Generation of alerts to overcome issues like pest attacks. 
  • Guidance related to the weather.
  • Constant monitoring of the activities of farms leads to better yield possibilities.
  • Results Predictability.
  • Comprehensive insights and reports. 
  • Improved efficiency in operations and accountability.
  • Tracking made easy tracking by Geo-tagging.
  • Reduced production costs due to better resource utilization. 
  • Adherence to compliances and norms 

Takeaway

The future of farming is about making the maximum use of technology for improved and higher yields. The farming practices will continue to redefine technological innovations. IoT-based solutions are ideal for improving the quality and quantity of crop production. 

 

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