Smart Farming: A Futuristic Methodology for Water Distribution & Soil Monitoring

We live in a tropical country where cultivating plants is a main part of Agriculture. The quality and quantity of the harvest depend on many external constraints. By applying modern Information and Communication Technologies into Agriculture, an effective outcome can be expected in the cultivation.

Smart Farming is a managing concept of farming by using modern technology to increase the quality and quantity of agriculture products with the use of a limited number of human resources. This is a revolution in the agriculture industry as it helps to guide actions required to maintain an agricultural system effectively and efficiently.

Uncertainty of global climate, rising concerns in the population and rapid escalation of food are boosting the demand for smart farming. In order to improve the cultivation, watering the crops and keeping the soil moist to the proper level is really important. Due to environmental conditions, pH level and moisture of the soil can be changed.

The soils have such a large impact on agricultural productivity, water quality and climate. Soils make it possible for crops to grow by mediating the chemical, physical and biological processes and supply the plants with enough water, nutrients and other elements.

The problem is identifying when and how much watering the crops need in order to maintain the proper moisture level of the soil in the field. For that, the following methodology can be introduced.

Methodology

Following hardware devices and software should be used to control the operation of keeping the moisture level of the soil.

Temperature Sensors

Soil Moisture Sensors

Humidity Sensors

Airflow Sensors

Thermal Sensors attached Drones

The proposed methodology is as follows.

First, an IoT based platform should be built. A database should be implemented to store the data captured from sensors for later analysis. Here, the sensors should be implemented in the following manner.

The temperature sensors and humidity sensors should be connected around the field properly to capture the temperate of the environment. The temperature sensors are capable of capturing the hotness or coolness of objects. Humidity sensors are capable of detecting and reporting moisture and temperature in the air.

There should be soil moisture sensors in three different levels under the soil in order to measure the volumetric water content in the soil. The soil moisture sensors are consist of two probes and these two probes allow the current to pass through the soil and by getting the resistance value, it can measure the moisture value of the soil. These three sensor coupling should be done in the front, back, sides and middle of the field in order to get more accurate data of the soil. By capturing the soil air permeability by airflow sensors from singular locations as well as dynamically while in motion, the data such as soil properties, structure, compaction of the soil, moisture level and soil types can be collected for later analysis.

In order to get more accurate temperature data of the field, drones attached thermal sensing cameras should be used. By analyzing the thermal map of the field, the farmers can identify the temperature high areas in red, average areas in yellow and low areas in green.

Thermal map of a field

The main advantages of identifying the soil moisture level of the fields are as follows.

Data gathering and processing help to take better decisions to improve productivity and efficiency.

The farmers should analyze and extract the gathered data to take necessary smart decisions. By analyzing the temperature, humidity, soil air permeability, soil moisture level and thermal map data gathered for a certain amount of time, the farmers can identify the patterns displayed for each season throughout the year, how to predict the upcoming temperature levels, and how the soil moisture level changes in day and night and etc.

Economic Benefit of less cost for water.

By processing the gathered qualitative and quantitative data, the farmers are capable of identifying the exact amount of water the field area should consume. By providing the accurate water distribution to the soil will reduce the overwatering the crops. Thus, the farmers will be able to reduce the water cost.

Increase the quality and quantity of the harvest.

With the necessary decisions made to improve the productivity and the efficiency as well as by identifying the water amount needed for each area, the probability of rotten harvest due to less water or over watering, temperature and humidity changes is decreased. Thus, the quality and quantity of the harvest are increased.

Disadvantages

The main disadvantages related to the proposed methodology are as follows.

High cost for IoT based devices and technologies.

Initial costs to purchase, assemble and implement IoT based devices and technologies are high which the average farmers cannot afford. The maintaining cost is remarkably high which must be done regularly.

Human Resources should be well sound in technical and technological aspects.

The average farmers do not possess the technical and technological intelligence related to smart farming. In order to cover the gap, operators with such intelligence should be hired and the cost to manage is high.

Faults in IOT based devices can cause faulty decisions.

If the devices and technologies are not properly assembled or maintained, the data gathered might lead to overwatering, less watering and wastage of resources.

Nature is unpredictable.

Agriculture is a natural phenomenon which relies on nature. The farmers cannot control or predict natural disasters such as heavy rains, droughts, storms, pests and sunlight availability. Even though the farmers can predict the seasonal changes in nature, sudden environmental changes will not be captured in the data analyzing process.

References

[1]. Achim Waltera, Robert Fingerb, Robert Huberb and Nina Buchmanna, “Smart farming is key to developing sustainable agriculture”. 2017.

[2]. Sjaak Wolfert, Lan Ge, Cor Verdouw and Marc-Jeroen Bogaardt, “Big Data in Smart Farming — A review”. 2017.

[3]. Vaibhavraj S. Roham, Abhijeet S. Patil, Ganesh A. Pawar and Prasad R. Rupnar, “Smart Farm using Wireless Sensor Network”. 2015.

[4]. Sandra L. Wolters, Thanos Balafoutis, Spyros Fountas and Frits K. van Evert, “Research project results on Smart Farming Technology”. 2016.

[5]. Nikesh Gondchawar and R. S. Kawitkar, “IoT based Smart Agriculture”. 2016.

[6]. Prem Prakash Jayaraman, Ali Yavari, Dimitrios Georgakopoulos, Ahsan Morshed and Arkady Zaslavsky, “Internet of Things Platform for Smart Farming: Experiences and Lessons Learnt”. 2016.

Senior IT Business Analyst