Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

Abstract

Smart farming is a new concept that makes agriculture more efficient and effective by using advanced information technologies. The latest advancements in connectivity, automation, and artificial intelligence enable farmers better to monitor all procedures and apply precise treatments determined by machines with superhuman accuracy. Farmers, data scientists and, engineers continue to work on techniques that allow optimizing the human labor required in farming. With valuable information resources improving day by day, smart farming turns into a learning system and becomes even smarter. Deep learning is a type of machine learning method, using artificial neural network principles. The main feature by which deep learning networks are distinguished from neural networks is their depth and that feature makes them capable of discovering latent structures within unlabeled, unstructured data. Deep learning networks that do not need human intervention while performing automatic feature extraction have a significant advantage over previous algorithms. The focus of this study is to explore the advantages of using deep learning in agricultural applications. This bibliography reviews the potential of using deep learning techniques in agricultural industries. The bibliography contains 120 papers from the database of the Science Citation Index on the subject that were published between 2016 and 2019. These studies have been retrieved from 39 scientific journals. The papers are classified into the following categories as disease detection, plant classification, land cover identification, precision livestock farming, pest recognition, object recognition, smart irrigation, phenotyping, and weed detection.

IOTA Based Anomaly Detection Machine learning in Mobile Sensing

“…The IoTA Framework is on the caller's side. [14] It includes the sensor platform, the sensor data interface and the SIP client, as shown in Fig. 1.…”

MNet: A Framework to Reduce Fruit Image Misclassification

“…The literature survey shows that systems based on internal classification are mostly sensor-based, complex, and costly because of extra hardware like NIR sensors. Most recently, Ünal [3] and Meshram et al [4] show that the emphasis has been on the development of computer vision systems based on machine learning and deep learning algorithms to solve problems in the agriculture field. Mostly these systems consider the external quality attributes like size, shape, gloss, and color for fruit classification.…”

Deep Feature Extraction for Detection of Tomato Plant Diseases and Pests based on Leaf Images

“…Thanks to deep learning, unlike traditional machine learning methods, learning from raw data can be performed without the need for feature extraction. In the literature, many studies have been conducted on the detection of plant diseases and pests based on deep learning [7,8]. Mohanty et al [1] retrained AlexNet and GoogLeNet pre-trained convolutional neural network (CNN) models, both from scratch and by fine-tuning.…”