Review of machine learning and deep learning models in agriculture

Bal, Fatih; Kayaalp, Fatih

doi:10.35860/iarej.848458

Cited by 18 publications

(4 citation statements)

References 83 publications

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“…With the advancement of technology, deep learning models have become a more and more popular tool for land mapping. Deep learning algorithms will be able to generate increasingly accurate and up-to-date land cover maps as more data becomes available and more efficient computational resources are produced [24].…”

Section: Deep Learning Models 311 Convolutional Neural Networkmentioning

confidence: 99%

Precision agriculture: exploration of deep learning models for farmland mapping

Tolentino,

Palaoag

2024

IJEECS

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Precision is required for agricultural advancements to be sustainable. Traditional farming lacks effective monitoring, resulting in resource waste and environmental problems. Farmland mapping is important for agricultural management and land-use planning. The use of deep learning techniques in farmland mapping is increasing rapidly. Excellent results have been generated from deep learning approaches in a number of applications, such as image processing and prediction. Agricultural agencies are now considering different applications of deep learning including land mapping, crop classification, and monitoring of paddy fields. This paper shall explore different deep learning models that are commonly used for image processing specifically in land mapping. The three deep learning models convolutional neural network (CNN), long short-term memory (LSTM), and recurrent neural network (RNN) were evaluated to find out which among the deep learning models is best for land mapping. It compares the classification accuracy of the models on image processing and it can be concluded that CNN algorithm normally makes better results when compared to other deep learning models. This study offers guideline and suggestions to researchers who are interested in contributing to the field of precision agriculture with the used of deep learning techniques.

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Section: Deep Learning Models 311 Convolutional Neural Networkmentioning

confidence: 99%

Precision agriculture: exploration of deep learning models for farmland mapping

Tolentino,

Palaoag

2024

IJEECS

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“…ML can establish efficient relationships between data inputs based on the past experiences and can effectively analyze large volume of data [13]. It involves the process of creating mathematical models on sample data sets called "training datasets" to make decisions and predictions [14]. The algorithm or model first trained and then classified/predicted with a ML technique.…”

Section: Machine Learningmentioning

confidence: 99%

Machine Learning-Driven Remote Sensing Applications for Agriculture in India—A Systematic Review

Pokhariyal,

Patel,

Govind

2023

Agronomy

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In India, agriculture serves as the backbone of the economy, and is a primary source of employment. Despite the setbacks caused by the COVID-19 pandemic, the agriculture and allied sectors in India exhibited resilience, registered a growth of 3.4% during 2020–2121, even as the overall economic growth declined by 7.2% during the same period. The improvement of the agriculture sector holds paramount importance in sustaining the increasing population and safeguarding food security. Consequently, researchers worldwide have been concentrating on digitally transforming agriculture by leveraging advanced technologies to establish smart, sustainable, and lucrative farming systems. The advancement in remote sensing (RS) and machine learning (ML) has proven beneficial for farmers and policymakers in minimizing crop losses and optimizing resource utilization through valuable crop insights. In this paper, we present a comprehensive review of studies dedicated to the application of RS and ML in addressing agriculture-related challenges in India. We conducted a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and evaluated research articles published from 2015 to 2022. The objective of this study is to shed light on the application of both RS and ML technique across key agricultural domains, encompassing “crop management”, “soil management”, and “water management, ultimately leading to their improvement. This study primarily focuses on assessing the current status of using intelligent geospatial data analytics in Indian agriculture. Majority of the studies were carried out in the crop management category, where the deployment of various RS sensors led yielded substantial improvements in agricultural monitoring. The integration of remote sensing technology and machine learning techniques can enable an intelligent approach to agricultural monitoring, thereby providing valuable recommendations and insights for effective agricultural management.

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“…In recent years, deep learning (DL) has emerged as a promising, novel, and cutting-edge technology for data processing. It's a better form of artificial neural networks (ANN), which is one of today's most prominent AI technologies [22], [23].…”

Section: Machine Learning (Ml)mentioning

confidence: 99%

Olive trees cases classification based on deep convolutional neural network from unmanned aerial vehicle imagery

Sehree

Khidhir²

2022

IJEECS

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Unmanned aerial vehicles (UAVs) are one of the various aerial remote sensing platforms with ease of use and cost-effectiveness it can deliver high-resolution imaging, obtained using a variety of sensors. Photogrammetric data is derived by the use of unmanned aerial systems (UAS, which consists of a UAV, sensor(s), and base station). As a result of these types, vegetation monitoring is conceivable. Deep neural networks have had a lot of success with image classification tasks, especially in the remote sensing field. In this paper, we demonstrate how deep neural networks can be used to classify olive trees status from aerial images. We have addressed a multi-class classification problem. In this work five different neural network architectures: VGG16, ResNet50, MobileNet, Xception, and VGG19 had been compared. Transfer learning had been accomplished using training of the fully connected layer(s) at the end of the deep learning layers. We used metrics such as accuracy, precision, recall, and confusion metric to evaluate the results. With accuracy, our model achieves the best results using ResNet50 with an accuracy is (97.2%).

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Review of machine learning and deep learning models in agriculture

Cited by 18 publications

References 83 publications

Precision agriculture: exploration of deep learning models for farmland mapping

Precision agriculture: exploration of deep learning models for farmland mapping

Machine Learning-Driven Remote Sensing Applications for Agriculture in India—A Systematic Review

Olive trees cases classification based on deep convolutional neural network from unmanned aerial vehicle imagery

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