each and every component and its design and selection requirements. In this chapter, information about several UAV systems and components and basic principles of design are presented.
Chapter 11 describes various security challenges in IoT-enabled agricultural system applications. The challenges facing these systems are software simplicity, secure data generation and transmission, and lack of supporting infrastructure. But at present the biggest obstacle is lack of smooth integration with the agricultural industry and lack of an optimally skilled human workforce. In addition to the need for sensors to work wirelessly and consume low power, they should have better connectivity and remote management, and the complexity and security of software should be rectified. There is also a high demand for fail-safe systems to mitigate the risk of data loss in any faults occurring during operation.
Chapter 12 looks at optimized crop treatment, such as watering, pesticide application, accurate planting, and harvesting, directly affects crop production rates. Using an organized fashion of field structure and proper irrigation scheduling and providing early prediction of weather conditions directly on the mobile devices of farmers may save thousands of lives, since this technique enables farmers to effectively produce crops in all growing seasons and losses will be minimized, ultimately decreasing the death toll due to lack of food in growing populations. For pest control, various ultrasound frequencies are used. When any pest interacts with any part of the plant, a capacitive touch shield can be used to detect its presence, generating an alert signal via an ultrasonic signal generator. For the supply of constant and continuous energy to the sensor, a solar cell powered battery needs to be set up in the field. To reduce power consumption of the battery, some logic needs to be set up to activate the sensor network at a particular time, with the sensor network otherwise remaining idle. A small exhaust fan is incorporated as a heat sink to protect the sensor devices or drive circuitry from the excess heat of the sun so that the longevity of the sensor devices and circuitry will increase. The IoT enables plane region step farming and animal and pest attack control, which truly enhances crop production rate.
In summary, at present there is a genuine need for agriculture upgradation and this book provides a technological overview that will open new dimensions which may be useful in discovering solutions to aid in the current growth in agricultural processes. The editors of this book are thankful to the all authors whose valuable contributions made this book as complete.
Editors Amitava Choudhury University of Petroleum and Energy Studies, Dehradun, India Arindam Biswas Kazi Nazrul University, Asansol, India Manish Prateek University of Petroleum and Energy Studies, Dehradun, India Amlan Chakrabarti University of Calcutta, Kolkata, India January 2021
1
A Study on Various Machine Learning Algorithms and Their Role in Agriculture
Kalpana Rangra and Amitava Choudhury*
School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India
Abstract
The term machine learning indicates empowering the machine to gain knowledge and process it for decision making. The domain of crop production is very important for organizations, firms, products related to agriculture. Data collection is done from different sources for crop forecasting. The collected data may vary in shape, size and type depending upon the source of collection. Agricultural data may be collected from metrological sources, agricultural and metrological, soil, sensors that are remotely installed, agricultural statistics, etc. Marketing, storage, transportation and decisions pertaining to crops have high requirement of accurate data that should be produced timely and can be used for predictions.
Keywords: Agriculture, machine learning, smart farming, decision tree, crop prediction, automated farming, ML models for agriculture
1.1 Introduction
Machine learning can be studied under two vast categories called supervised and unsupervised learning. Supervised learning pertains to fact that data and process is supervised by supervisor. The process of training data is controlled to find the conclusions for new data. Some of the most commonly used techniques for supervised learning are Artificial neural network, Bayesian network, decision tree, support vector machines, ID3, k-nearest neighbor, hidden Markov model, etc. For unsupervised learning enormous volume of data is given as input to program for which the program generates patterns and identify the relations among them. Unsupervised learning can be used to discover the hidden patterns. K-nearest neighbor algorithm, self-organizing map methods, and partial based clustering techniques, hierarchical clustering approaches, k-means clustering, etc., belong to class of unsupervised learning. Predictive power of computers can be increased by integrating machine with statistics. Data scientists and analysts use this integration to predict trends from raw data that is fed into the system. The amount of data obtained in agricultural field is increasing enormously so the machine learning techniques can be applied to agricultural production for predicting crop related queries. Decisions regarding crop production can be made by using several available machines learning techniques. All such techniques use mathematics and stats for algorithm generation.
1.1.1 Machine Learning Model
1.1.1.1 Artificial Neural Networks
The artificial neural network is a collaboration of artificial neurons based on human brains biological architecture. They replicate the behavior of the human brain for processing the data. Artificial neural network belongs to the category of supervised learning where a part of data is used for model training and the remaining is tested on the trained model. Once the neural net is trained, the similar patterns can be generated for obtaining efficient and solutions to problems and predictive analysis. The trained neural network can produce solutions even if the input data is incomplete or incorrect. Adding more layers and data increases the accuracy of the ANN. The ANN is capable of adopting their complexity without the need to know the underlying principles. The relationship among input and output for any process can be derived using ANN. Authors used [1] ANN to predict potato yield in Iran. Figure 1.1 shows the basic architecture of artificial neural network.
Input energy was taken as the input parameter. The work intended to design output energy and greenhouse gas emission for production forecast. The data collection was done from 260 farmers by taking inputs from them. Multiple ANNs were designed and utilized to forecast. The forecast efficiency was assessed from quality aspect. The prediction results. Electricity, chemical fertilizer and seed were identified as most important factors affecting production rate. Literature [3] quotes ANN systems are the results of inspiration of the human brain. Each node in neural net represents neurons and each link is the representation of interaction among two associated neurons. Execution of simple tasks is the responsibility of single neuron while the network performs more complex tasks that are aggregations of all the neuron groups in network. There exists an interconnected set of input and output that has weighted connections. The testing phase of network enables them to earn to predict the input sampled by performing weight tuning. Flood forecast uses neural networks to model rainfall and runoff relationships for predicting flood situations. Neural networks have better performance over conventional computing methods. ANN finds suitability for the time consuming problem solutions such as pest prediction. Research [4] found that validation of the symptoms
