Classification of soil aggregates: A novel approach based on deep learning

Azizi, Afshin; Abbaspour‐Gilandeh, Yousef; Mesri-Gundoshmian, Tarahom; Saleh-Bigdeli, Ali Akbar; Moghaddam, Hamid Abrishami

doi:10.1016/j.still.2020.104586

Cited by 61 publications

(17 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Indeed, skip connections allow gradient to flow information from earlier layers in the network to later layers. This is also in line with the similar study [43]. Thus, they pass information from the down sampling layers to the up-sampling layers.…”

Section: Discussionsupporting

confidence: 90%

A Novel Technique for Classifying Bird Damage to Rapeseed Plants Based on a Deep Learning Algorithm

et al. 2021

Self Cite

View full text Add to dashboard Cite

Estimation of crop damage plays a vital role in the management of fields in the agriculture sector. An accurate measure of it provides key guidance to support agricultural decision-making systems. The objective of the study was to propose a novel technique for classifying damaged crops based on a state-of-the-art deep learning algorithm. To this end, a dataset of rapeseed field images was gathered from the field after birds’ attacks. The dataset consisted of three classes including undamaged, partially damaged, and fully damaged crops. Vgg16 and Res-Net50 as pre-trained deep convolutional neural networks were used to classify these classes. The overall classification accuracy reached 93.7% and 98.2% for the Vgg16 and the ResNet50 algorithms, respectively. The results indicated that a deep neural network has a high ability in distinguishing and categorizing different image-based datasets of rapeseed. The findings also revealed a great potential of deep learning-based models to classify other damaged crops.

show abstract

Section: Discussionsupporting

confidence: 90%

A Novel Technique for Classifying Bird Damage to Rapeseed Plants Based on a Deep Learning Algorithm

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…An example of the available AI technique is shown in (Azizi and Gilandeh, 2020) where are used real world images as inputs of convolutional neural network (CNN) for classify the soil aggregates where is obtained good performance of the deep learning method as well as robustness of the presented deep model to noises and other unwanted signals. In (Achieng, 2019) is proposed machine learning algorithms (artificial neural networks (ANN) and deep neural networks (DNN)) for soil water retention curve (SWRC) for analyzing of flow and contaminant transport in the vadose zone.…”

Section: Ann (Artificial Neural Network)mentioning

confidence: 99%

Assessing of Soil Erosion Risk Through Geoinformation Sciences and Remote Sensing—A Review

Filchev

Kolev

2021

Earth and Environmental Sciences Library

View full text Add to dashboard Cite

During past decades a marked manifestation of widespread erosion phenomena was studied worldwide. Global conservation community has launched campaigns at local, regional and continental level in developing countries for preservation of soil resources in order not only to stop or mitigate human impact on nature but also to improve life in rural areas introducing new approaches for soil cultivation. After the adoption of Sustainable Development Goals of UNs and launching several world initiatives such as the Land Degradation Neutrality (LDN) the world came to realise the very importance of the soil resources on which the biosphere relies on for its existence. The main goal of the chapter is to review of different types and structures erosion models as well as its some applications. Several methods using spatial analysis capabilities of geographic information systems (GIS) are in operation for soil erosion risk assessment, such as: Universal Soil Loss Equation (USLE), Revised Universal Soil Loss Equation (RUSLE) in operation worldwide and in USA and Modèle d'Evaluation Spatiale de l'ALéa Erosion des Sols (MESALES) model. These and more models are being discussed in present work alongside more experimental models and methods for assessing soil erosion risk such as Artificial Intelligence (AI), Machine and Deep Learning etc. At the end of this work a prospectus for the future development of soil erosion risk assessment is drawn.

show abstract

“…To give an example of these studies, computer-aided diagnosis (CAD) systems, using AI (Artificial Intelligence), were used in order to accurately identify diseases and pests affecting small farmers' production and also to help understand the severity of symptoms, as well as allowing any farmer with access to a smartphone to benefit from expert knowledge in a practical and cost-effective manner (Esgario et al, 2020). Azizi et al (2020) used a convolutional neural network (CNN), a deep learning method, to classify soil clusters while they used VggNet16, ResNet50, and Inception-v4 trained models to train CNN. Esgario et al (2020) used deep learning to classify biotic stress in coffee and to estimate its severity.…”

Section: Introductionmentioning

confidence: 99%

Land Quality Index for Paddy (Oryza sativa L.) Cultivation Area Based on Deep Learning Approach using Geographical Information System and Geostatistical Techniques

ŞENYER>

Akay

Odabas

et al. 2023

Yüzüncü Yıl Üniversitesi Tarım Bilimleri Dergisi

View full text Add to dashboard Cite

Türkiye has ideal ecological conditions for growing rice, and its yield per hectare is often higher than the average worldwide. However, unbalanced fertilization, nutrient deficiency, and irrigation problems negatively affect paddy production when soil characteristics are not considered. The present study was conducted on a 1763-hectare field (652000-659000E-W and 4528000-4536000N-S) in 2019. This study's primary goal was to categorize land quality for rice production using 15 different physicochemical parameters and a GIS (Geographical Information Systems) and deep learning (DL) technique. Using these parameters soil types were classified and regression analysis was performed by DL. Different soil parameters as network outputs used in this study caused different performance levels in models. Therefore, different models were suggested for each network output. The R2 values indicated a respectable level for parameter prediction, and an accuracy of 88% was attained when classifying "class" data. The findings of the study demonstrated that deep learning may be used to forecast soil metrics and distinguish between different land quality classes. Additionally, a field investigation was used to validate the indicated land quality classifications. Using statistical techniques, a substantial positive link between rice yield and land quality classes was discovered.

show abstract

Classification of soil aggregates: A novel approach based on deep learning

Cited by 61 publications

References 19 publications

A Novel Technique for Classifying Bird Damage to Rapeseed Plants Based on a Deep Learning Algorithm

A Novel Technique for Classifying Bird Damage to Rapeseed Plants Based on a Deep Learning Algorithm

Assessing of Soil Erosion Risk Through Geoinformation Sciences and Remote Sensing—A Review

Land Quality Index for Paddy (Oryza sativa L.) Cultivation Area Based on Deep Learning Approach using Geographical Information System and Geostatistical Techniques

Contact Info

Product

Resources

About