Agricultural Informatics

Table 2.6 illustrates the technology for monitoring weed in smart agriculture.

Table 2.7 illustrates the technology for water management in smart agriculture.

Table 2.4Technology of using proper fertilizer.

Table 2.5Technology of monitoring intrusion.

Table 2.6Technology of monitoring weed.

Table 2.7Technology for water management.

As the researchers discussed in the previous section about variety of exciting methods developed which are very advantageous in modern agricultural field, still it lacks maximum level of efficiency. Different researchers have tried to capture one or two problems associated with agriculture and figured out the solution for the same. But the actual need is to have such a system which will combine solution of all the problems associated with agriculture in a single system. Thus the future system will be more acceptable to the farmers.

Researchers have built a prototype model for the above said problem by keeping the followings points in mind:

Increase in crop production value.

Minimizing activities of a farmer. He should have to do the basic field work only.

The cost of the integrated system to be nominal as much as possible.

Utilization of solar energy to get clean source of energy and emits lower amount of GFGs.

Cultivation process should be efficient with less human interfere.

Thus the work of the integrated Agro-IoT is as follows:

1 Humidity Monitoring.

2 Soil fertility Monitoring.

3 Low water level detection and automatic water level Management.

4 Climate Condition Monitoring.

5 Detection of Pest and control it by spraying pesticide automatically.

6 Detection of Weed and give a notice to farmer for cutting.

7 Fire Accident Detection and automatic control of fire.

8 Intrusion Detection (e.g. goat, cow, etc.) within a certain limit and automatic control.

9 Temperature Monitoring.

Figure 2.3illustrates the proposed prototype model of the field with Agro-IoT system.

A total of 6 types of sensors have been used to build the prototype model for the integrated system. The soil condition is getting measured by temperature, moisture, and ph sensor and sends the data to beaglebone black. Ultrasonic sensor senses the water level of the field and when it detects a lower voltage value than the predefined threshold value, then it send a signal to beaglebone black so that automatically the water pump will start to provide water in field. When the water level is enough in field the sensor again send the signal to beaglebone black to stop the water pump automatically.

Figure 2.3 Proposed prototype model for integrated Agro-IoT system.

The camera plays an important role in the prototype model. It captures image of the crop field in every milliseconds and sends the processed data to the image server which is connected to beaglebone black. Through the camera and with our proposed image processing algorithm three most important things i.e. pest, weed and fire can be detected easily. If the proposed image processing algorithm detects the amount of pest is severe, then through beaglebone black the sprinkler for spraying pesticide will get active and spray automatically. So pest can be controlled efficiently by the system without the farmer. When the proposed algorithm detect weed, it sends the signal to beaglebone black and farmer can get the alert via sms to control the weed by themselves only. In case of fire in the field, the proposed algorithm detect the region of fire and send the signal to beaglebone black so that the sprinkler for distributing water to the field gets active and take action to resolve it. Thus pest, weed and fire can be detected and controlled efficiently. Proximity sensor is also playing an important role to find any intruder. In the present of any animal in the field like cow or goat, the sensor sends the signal to beaglebone black and automatically it will send it to the corresponding device to uplift the iron railing from all the four sides of the field. Thus, no animal except bird can enter the field. All the device is getting active by utilizing the eco-friendly energy source of solar panel. Farmers get alert of every above mentioned condition via sms. For future records, the processed data from beaglebone black also get uploaded in cloud storage and an interface can be used by the farmer to get all the records of the field anytime from anywhere.

The continuous image of the field is captured by the camera in every millisecond and gets processed for any unwilling pattern from known pattern. HOG (Histogram Oriented Gradient) image processing strategy is applied here to distinguish between the known and unknown pattern. The known image pattern like crop leaf are treated as training image set which is input to the image processing algorithm. Then by image comparing technique, classifier classifies the image as known or unknown. If any unknown pattern is observed moveable then it will be treated as a weed and unknown pattern is observed not movable then it will be treated as pest.