Deep Learning Techniques Used in Agriculture: A Review (original) (raw)

Deep learning in agriculture: A survey

Computers and Electronics in Agriculture, 2018

Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.

Review of machine learning and deep learning models in agriculture

International Advanced Researches and Engineering Journal, 2021

Machine learning (ML) refers to the processes that enable computers to think based on various learning methods. It can be also called domain which is a subset of Artificial Intelligence (AI). Deep learning (DL) has been a promising, new and modern technique for data analysis in recent years. It can be shown as the improved version of Artificial Neural Networks (ANN) which is one of the popular AI methods of today. The population of the world is increasing day by day and the importance of agriculture is also increasing in parallel. Because of this, many researchers have focused on this issue and have tried to apply machine learning and deep learning methods in agriculture under the name of smart farm technologies both to increase agricultural production and to solve some challenges of agriculture. In this study, it is aimed to give detailed information about these up-to-date studies. 77 articles based on machine learning and deep learning algorithms in the agriculture field and published in IEEE Xplore, ScienceDirect, Web of Science and Scopus publication databases between 2016 and 2020 years were reviewed. The articles were classified under five categories as plant recognition, disease detection, weed and pest detection, soil mapping-drought index, and yield forecast. They were examined in detail in terms of machine learning/deep learning architectures, data sets, performance metrics (Accuracy, Precision, Recall, F-Score, R 2 , MAPE, RMSE, MAE), and the obtained experimental results. Based on the examined articles, the most popular methods, used data sets/types, chosen performance criteria, and performance results among the existing studies are presented. It is seen that the number of AIbased applications related to agriculture is increasing compared to the past and the sustainability in productivity is so promising.

Design of deep learning system for agricultural purpose

International Journal of Communication and Information Technology

Agriculture and its requirements are, at the time, quite challenging to handle. The bulk of the country's residents are dependent on agriculture for their income. Food production should also be increased to keep up with the World's population growth. Agriculture has benefited significantly from recent technological advancements. Agricultural experts are becoming excited by current technology advances such as the Internet of Things (IoT), Machine Learning (ML), and Deep Learning (DL). IoT agriculture and farming are a whole new area of IoT application. We all know how to use IoT-based analytics like sensing soil temperature, nutrients, and humidity and regulating and monitoring water consumption for plant growth. The Internet of Things collects and produces vast volumes of data across several sectors and applications. Many challenges facing the agriculture business may be dealt with using deep learning and IoT technologies.

From machine learning to deep learning in agriculture -the quantitative review of trends

In the last two decades, we have witnessed the intensive development of artificial intelligence in the field of agriculture. In this period, the transition from the application of simpler machine learning algorithms to the application of deep learning algorithms can be observed. This paper provides a quantitative overview of papers published in the past two decades, thematically related to machine learning, neural networks, and deep learning. Also, a review of the contribution of individual countries was given. The second part of the paper analyses trends in the first half of the current year, with an emphasis on areas of application, selected deep learning methods, input data, crop mentioned in the paper and applied frameworks. Scopus and Web of Science citation databases were used.