Machine Learning in Agriculture: A Comprehensive Updated Review.
Lefteris Benos,Aristotelis C. Tagarakis,Georgios Dolias,Remigio Berruto,Dimitrios Kateris,Dionysis Bochtis +5 more
TLDR
In this paper, a review of the recent literature on machine learning in agriculture is presented, where a plethora of machine learning algorithms are used, with those belonging to Artificial Neural Networks being more efficient.Abstract:
The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” along with “crop management”, “water management”, “soil management”, and “livestock management”, and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018–2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.read more
Citations
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Challenges to Use Machine Learning in Agricultural Big Data: A Systematic Literature Review
TL;DR: A systematic literature review applying the PRISMA protocol and develops a framework that summarizes the main challenges encountered, machine learning techniques, and the leading technologies used in agricultural Big Data.
Journal ArticleDOI
Improving wheat yield prediction integrating proximal sensing and weather data with machine learning
Guojie Ruan,Xinyu Li,Fei Yuan,Davide Cammarano,Syed Tahir Ata-UI-Karim,Xiaojuan Liu,Yongchao Tian,Yan Zhu,Weixing Cao,Qiang Cao +9 more
TL;DR: In this article , the authors developed an in-season wheat yield prediction model at field-scale by integrating proximal sensing and weather data, and the results revealed that the ensemble learning models (Random Forest, eXtreme Gradient Boosting) achieved the best overall performance.
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Yield and Quality of Romaine Lettuce at Different Daily Light Integral in an Indoor Controlled Environment
TL;DR: In this paper , the effect of different photynthetic photon flux density (PPFD) provided by LEDs (Light Emitting Diodes) and photoperiod on biomass production, morphological traits, photosynthetic performance, sensory attributes, and image texture parameters of indoor cultivated romaine lettuce was evaluated.
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Bridging the Gaps in Traceability Systems for Fresh Produce Supply Chains: Overview and Development of an Integrated IoT-Based System
Aristotelis C. Tagarakis,Lefteris Benos,Dimitrios Kateris,Nikolaos Tsotsolas,Dionysis Bochtis +4 more
TL;DR: It was concluded that the commercially available traceability systems usually neither cover the entire length of the supply chain nor rely on open and transparent interoperability standards, so a user-friendly open access traceability system is proposed for an integrated solution for traceability and agro-logistics of fresh products, focusing on interoperability and data sharing.
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Behavioral Monitoring Tool for Pig Farmers: Ear Tag Sensors, Machine Intelligence, and Technology Adoption Roadmap
Santosh Pandey,Upender Kalwa,Taejoon Kong,Baoqing Guo,Phillip C. Gauger,David J. Peters,Kyoung-Jin Yoon +6 more
TL;DR: In this paper, the authors present a remote monitoring tool for the objective measurement of some behavioral indicators that may help in assessing the health and welfare status of pigs, such as posture, gait, vocalization, and external temperature.
References
More filters
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Journal ArticleDOI
A few useful things to know about machine learning
TL;DR: Tapping into the "folk knowledge" needed to advance machine learning applications is a natural next step in the development of artificial intelligence systems.
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
Ensemble learning: A survey
Omer Sagi,Lior Rokach +1 more
TL;DR: The concept of ensemble learning is introduced, traditional, novel and state‐of‐the‐art ensemble methods are reviewed and current challenges and trends in the field are discussed.
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.