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A comprehensive review on automation in agriculture using artificial intelligence

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TLDR
In this article, a survey of the work of many researchers to get a brief overview about the current implementation of automation in agriculture is presented and a proposed system which can be implemented in botanical farm for flower and leaf identification and watering using IOT.
Abstract
Agriculture automation is the main concern and emerging subject for every country. The world population is increasing at a very fast rate and with increase in population the need for food increases briskly. Traditional methods used by farmers aren't sufficient enough to serve the increasing demand and so they have to hamper the soil by using harmful pesticides in an intensified manner. This affects the agricultural practice a lot and in the end the land remains barren with no fertility. This paper talks about different automation practices like IOT, Wireless Communications, Machine learning and Artificial Intelligence, Deep learning. There are some areas which are causing the problems to agriculture field like crop diseases, lack of storage management, pesticide control, weed management, lack of irrigation and water management and all this problems can be solved by above mentioned different techniques. Today, there is an urgent need to decipher the issues like use of harmful pesticides, controlled irrigation, control on pollution and effects of environment in agricultural practice. Automation of farming practices has proved to increase the gain from the soil and also has strengthened the soil fertility. This paper surveys the work of many researchers to get a brief overview about the current implementation of automation in agriculture. The paper also discusses a proposed system which can be implemented in botanical farm for flower and leaf identification and watering using IOT.

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Citations
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References
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Journal ArticleDOI

Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications

TL;DR: The steps that should be followed in the development of artificial neural network models are outlined, including the choice of performance criteria, the division and pre-processing of the available data, the determination of appropriate model inputs and network architecture, optimisation of the connection weights (training) and model validation.
Journal ArticleDOI

Deep learning in agriculture: A survey

TL;DR: A survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges indicates that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.
Journal ArticleDOI

Deep learning models for plant disease detection and diagnosis

TL;DR: In this article, convolutional neural network models were developed to perform plant disease detection and diagnosis using simple leaves images of healthy and diseased plants, through deep learning methodologies.
Journal ArticleDOI

Machine Learning in Agriculture: A Review.

TL;DR: A comprehensive review of research dedicated to applications of machine learning in agricultural production systems is presented, demonstrating how agriculture will benefit from machine learning technologies.
Journal ArticleDOI

Remote Sensing and Control of an Irrigation System Using a Distributed Wireless Sensor Network

TL;DR: Details of the design and instrumentation of variable rate irrigation, a wireless sensor network, and software for real-time in-field sensing and control of a site-specific precision linear-move irrigation system are described.
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