Category: A complete automated solution for farm field and garden nurturing

Improvement in production of crops is a major challenge in a developing country like India. Novel smart technologies should be taken under agriculture stream to lead to green population country. So, we proposed a complete automated technology based on IoT for the farm fields and gardens. The plants and crops are monitored and controlled without… Continue reading Conclusion

The quality of the image captured using the camera is affected by the flash, frequency distortion, ambient light and intensity of the camera. The above factors are considered as noise. It can be removed from the image using a deep CNN (Andrey et al. 2017). An enhanced image for the brown spot disease of paddy leaves is… Continue reading Result and discussion

For experimental setup, we used the system of configuration: Intel Core i3, 2.40 GHz, 6 GB RAM with 70 GB hard disk in Ubuntu 16.04 environment. All the experiments were performed using Python 3.5 version, Tensorflow 1.3 version, Keras 2.0.8 version and cuDNN. Keras is a deep learning library which contains all deep learning models (Kumar… Continue reading Experimental setup and dataset

Figure 12.10shows the proposed architecture of the four-layer deep CNN. It is used to classify the entry of non-humans in the intrusion detection system (P Arokianathan et al. 2017) and the presence of the disease in the leaves of the paddy crops. The architecture includes four layers of CNN followed by batch normalization with a… Continue reading Proposed architecture

In this sub-system, the camera plays the main role. Pi activates the camera (Kaveeya et al. 2017a) at a specific time of the day. The camera captures the leaf images of the plant. It passes the captured image to the PC for classification. Whether the leaf is infected is checked in the PC using four-layer CNN.… Continue reading Infection detection sub-system

This sub-system consists of motor, pH sensor and rainfall sensor. The rainfall sensor is activated by the Pi to sense the fall of rain. If rain is falling, the motor (Saranu et al. 2018) is running or not is checked by the Pi. If the motor is running, Pi will turn off the motor and it… Continue reading Nutrition sub-system

In this sub-system, moisture sensor, temperature sensor, level sensor and motor play a major role. Soil moisture sensor (Sagar et al. 2017) checks the moisture content in the soil. If soil moisture is low, it continuously checks the moisture in the soil. Otherwise, the temperature sensor is activated. Pi checks if the temperature is less than… Continue reading Water supply sub-system

In this sub-system, IR sensor, camera and buzzer play a major role. IR sensor is activated to capture moving object in the fields. Once an object is detected, the camera will be activated by the Pi. The camera captures the moving object into the fields. The moving objects may be a human being, cow, goat,… Continue reading Intrusion detection sub-system

Figure 12.2 shows the sequence diagram of the proposed system. In the proposed system IoT plays a major role. The purpose of the IoT in this system is to share the data with the users. Raspberry Pi (Kaveeya et al. 2017b) is connected with a Wi-Fi module. The farmers or gardeners have to register their mobile with… Continue reading Work flow

Figure 12.1 shows the proposed architecture of the complete irrigation and garden nurturing system. Raspberry Pi is the main controller that collects data from all sensors to the cloud database. The soil moisture sensor is used to sense the water content in the soil, which is placed in the soil. The level sensor is placed nearer… Continue reading Proposed architecture