Smart farming is becoming an essential approach to solve several issues in the agricultural area including how to create precise agriculture schemes, how to efficiently manage the resources such as workers, fertilizers, or the land itself, and possibly for monitoring the farm as the surveillance approach. Since in the recent years, the interest of younger generation for being farmers is getting declined. Therefore, the smart farming application can be a buzzword and possibly an approach to attract many generations to get involved in growing our food.
In this dissertation, the study on the construction of smart farming application is presented. As we know that some recent applications are constructed based on the Internet of Things (IoT) paradigm. Thus, the approach of the IoT is studied in this dissertation particularly for the construction of the smart farming framework using LoRa protocol. The study for the deployment of the scheme was conducted in the Indonesian rural area. The objective was to have an idea related to the factors affecting the performance of the scheme such as the data latency and the possible range of the proposed approach.
Secondly, we proposed a scheme using deep learning to classify the cocoa bean images retrieved from the Indonesian agriculture industry. The classification is based on the Indonesian export standard. To have an accurate classification we proposed structure called Progressive Contextual Excitation Network (PCENet) and Selective Context Adaptation Network (SCANet). From the study, we found that both proposed schemes can achieve accuracy of 86.8% and 88.7% which outperform the other well-known architectures.
Eventually, to deploy the proposed models, we conducted an experiment for compressing the proposed structure. The compression technique was done using pruning filters. The objective of the compression technique study is to give an insight how we can deploy the proposed models and accelerate the current models. Therefore, it can be possible for the deployment into a cheap and more affordable hardware such as the deployment into the Jetson Nano as an edge computing platform. From our study, we can possibly accelerate the PCENet model from 9.9 FPS to 16.7 FPS and 2.9 to 5.3 FPS for the SCANet model.

Smart farming is becoming an essential approach to solve several issues in the agricultural area including how to create precise agriculture schemes, how to efficiently manage the resources such as workers, fertilizers, or the land itself, and possibly for monitoring the farm as the surveillance approach. Since in the recent years, the interest of younger generation for being farmers is getting declined. Therefore, the smart farming application can be a buzzword and possibly an approach to attract many generations to get involved in growing our food.
In this dissertation, the study on the construction of smart farming application is presented. As we know that some recent applications are constructed based on the Internet of Things (IoT) paradigm. Thus, the approach of the IoT is studied in this dissertation particularly for the construction of the smart farming framework using LoRa protocol. The study for the deployment of the scheme was conducted in the Indonesian rural area. The objective was to have an idea related to the factors affecting the performance of the scheme such as the data latency and the possible range of the proposed approach.
Secondly, we proposed a scheme using deep learning to classify the cocoa bean images retrieved from the Indonesian agriculture industry. The classification is based on the Indonesian export standard. To have an accurate classification we proposed structure called Progressive Contextual Excitation Network (PCENet) and Selective Context Adaptation Network (SCANet). From the study, we found that both proposed schemes can achieve accuracy of 86.8% and 88.7% which outperform the other well-known architectures.
Eventually, to deploy the proposed models, we conducted an experiment for compressing the proposed structure. The compression technique was done using pruning filters. The objective of the compression technique study is to give an insight how we can deploy the proposed models and accelerate the current models. Therefore, it can be possible for the deployment into a cheap and more affordable hardware such as the deployment into the Jetson Nano as an edge computing platform. From our study, we can possibly accelerate the PCENet model from 9.9 FPS to 16.7 FPS and 2.9 to 5.3 FPS for the SCANet model.

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