Traditional farming is labour-intensive, and the need to constantly check crops may be a strain on farmers. On another side, agricultural yield space is shrinking by the day. So, without a question, managing a huge amount of food that is legally right for us is a very difficult problem for any human being. There may always be a footprint available to meet the high demand. By achieving the idea of smart farming based on new technology by using the Internet of Things (IoT), the authors have presented a strategy in this study work by which a farmer may manage water irrigation, detect the total amount of brightness, and monitor the moisture level of soil and current status of crops using IoT. By utilizing such a technology, the farmer would obtain an auto lighting system, an auto water irrigation system, prohibit external vehicles, and conserve electricity by utilizing real-time data obtained from various types of sensors and utilizing a Wi-Fi system. The suggested system's hardware is all directly linked to the NodeMCU ESP8266. An algorithm has been created to manage the entire project. The solar panel will supply the entire system's necessary electric power, allowing us to save money, conserve electricity, and make the total system more environmentally friendly. This work’s suggested system can identify meteorological conditions that are beneficial to agriculture. This proposed concept has exceptional performance potential as an interface between sensors as input and the IoT as an output medium. The suggested system is compared to other existing systems in a variety of ways. 

Application of IoT for Developing Sustainable and Smart Farming

The population of the world in 2021 was approximate 7.9 billion which will be increased about 10 billion by 2050. In this symphony, the necessity of food and pure water will await about double. On the other hand, the space of free land for agriculture is decreasing day by day. So, it is a very hard challenge for everyone to manage a huge amount of food which is a courtly right for us. Always this challenge is might be a footprint for fulfilling the great demand. For solving such types of problems, we have to connect with the modern technological systems. Nowadays, the Internet of Things (IoT) is an optimal way to prevent such types of challenges. In this paper, we proposed a model by which a farmer can control lighting system, water pump, soil condition, and crops condition with the help of IoT. By implementing such type of model, the farmer will be able to monitor an auto lighting system, an auto water irrigation system, prevent external objects, save the electric power and analyze real-time data which are collected from different types of sensors by using a Wi-Fi system. All the hardware of the proposed model is directly connected with NodeMCU ESP8266. The essential energy of the whole system depends on the solar panel which reduces the cost, saves electricity and makes the total system eco-friendly and cost-effective. By using our proposed model, the farmers can detect the condition of the weather which makes a good impact on agriculture. For the current demand, the proposed model will make a good platform to complete our civil rights in upcoming future. 

GreenFarm: An IoT-Based Sustainable Agriculture with Automated Lighting System

The advancement of research in the field of natural language processing has made peoples’ daily lives much easier, with numerous applications at the disposal. The traditional methods like the Hidden Markov Model, the CRF classifier, the Naive Bayes classifier, and others are being replaced by neural networks in recent times. However, most of these methods work considerably well only with huge amounts of training data and, hence are not suitable for languages that are poor in terms of trainable resources. The challenge is to make the system work considerably well with minimal training. This paper presents research work to understand the effect of training size for part of speech tagging, which is one of the preliminary tasks for any NLP application. Experiments are conducted to understand the training size required for standard techniques to perform with high accuracy. The results of the experiments conducted for English and Assamese are presented in this paper.

Towards Minimal Training for Low Resource Languages

The advancement of research in the field of natural language processing has made peoples’ daily lives much easier, with numerous applications at the disposal. The traditional methods like the Hidden Markov Model, the CRF classifier, the Naive Bayes classifier, and others are being replaced by neural networks in recent times. However, most of these methods work considerably well only with huge amounts of training data and, hence are not suitable for languages that are poor in terms of trainable resources. The challenge is to make the system work considerably well with minimal training. This paper presents research work to understand the effect of training size for part of speech tagging, which is one of the preliminary tasks for any NLP application. Experiments are conducted to understand the training size required for standard techniques to perform with high accuracy. The results of the experiments conducted for English and Assamese are presented in this paper. 

Natural language processing has been a challenging area for researchers in recent times. A lot of research works are conducted to improve the performance of natural language processing that have helped many applications to be made available for our day to day life. Most of the developments have happened only for a few western languages. Researchers have also published some quality works related to Indian languages in recent times. However, the research works conducted for most of the other languages spoken in India, the country with the second largest population in the world, are at a very primitive stage. This paper aims to review the advances in this area for one of the Low Resource Indian languages –“Assamese”, the official language of Assam, the gateway to North East India. It is also spoken in many other states of North Eastern part of India. In this work, we present a highlight of research works related to various methods that are applied to Assamese Text processing. It is observed that certain language characteristics of Assamese are best applicable to certain popular methods used for Natural Language Processing. The reason behind this relook is to overview and report the present state and future possibilities of research in text processing in Assamese. This paper should serve as a good beginning for anyone interested to carry forward the computational works for Assamese. We conclude the paper with a brief discussion on the scope of Assamese Text processing in the future.

Present state and future scope of Assamese text processing

The system proposed in this paper is a solution for monitoring the various atmospheric and environmental factors responsible for better yield of Rice Cultivation. The technology used behind this system is the Internet of Things (IoT), IoT is a platform that connects various devices over the internet. The proposed system is divided into two working parts: a Hardware part and a Software part. The Hardware part consists of various sensors to read the various atmospheric conditions and a Software part that displays the necessary information on a mobile application. Apart from these. there is also a Chemical Test involved to measure the fertility of the soil. The system measures the various factors needed for Rice Cultivation and finally displays the output on a mobile application.

Internet of Things based Smart Farming

Over the years, many different algorithms are proposed to improve the accuracy of the automatic parts of speech tagging. High accuracy of parts of speech tagging is very important for any NLP application. Powerful models like The Hidden Markov Model (HMM), used for this purpose require a huge amount of training data and are also less accurate to detect unknown (untrained) words. Most of the languages in this world lack enough resources in the computable form to be used during training such models. NLP applications for such languages also encounter many unknown words during execution. This results in a low accuracy rate. Improving accuracy for such low-resource languages is an open problem. In this paper, one stochastic method and a deep learning model are proposed to improve accuracy for such languages. The proposed language-independent methods improve unknown word accuracy and overall accuracy with a low amount of training data. At first, bigrams and trigrams of characters that are already part of training samples are used to calculate the maximum likelihood for tagging unknown words using the Viterbi algorithm and HMM. With training datasets below the size of 10K, an improvement of 12% to 14% accuracy has been achieved. Next, a deep neural network model is also proposed to work with a very low amount of training data. It is based on word level, character level, character bigram level, and character trigram level representations to perform parts of speech tagging with less amount of available training data. The model improves the overall accuracy of the tagger along with improving accuracy for unknown words. Results for “English” and a low resource Indian Language “Assamese” are discussed in detail. Performance is better than many state-of-the-art techniques for low resource language. The method is generic and can be used with any language with very less amount of training data.

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Rupam Baruah

Papers

Present state and future scope of Assamese text processing

Internet of Things based Smart Farming

Highly Efficient Parts of Speech Tagging in Low Resource Languages with Improved Hidden Markov Model and Deep Learning