Soil Classification Using Machine Learning Methods and Crop Suggestion Based on Soil Series

Rahman, Sk Al Zaminur; Mitra, Kaushik; Islam, Sahidul

doi:10.1109/iccitechn.2018.8631943

Cited by 116 publications

(31 citation statements)

References 1 publication

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“…Understanding soil is an important factor in determining the quality of agricultural output. Machine learning techniques are widely used in crop suggestions based on soil [88], fertility grading of soil [89], soil moisture prediction [90], etc. Machine learning could also be employed in some of the connected areas related to smart indoor farms, such as the determination of production quality [91], designing of soil sensors [92], weed detection [93], etc.…”

Section: Core Data Handling Layermentioning

confidence: 99%

Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution

Hati

Singh

2021

AgriEngineering

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Conventional farming necessitates a large number of resources and infrastructure such as land, irrigation, manpower to manage farms, etc. Modern initiatives are required to automate conventional farms. Smart indoor farms offer the potential to remedy the shortfalls of conventional farms by providing a controlled, intelligent, and smart environment. This paper presents a three-dimensional perspective consisting of soilless farming, energy harvesting, and smart technologies, which could be considered as the three important characteristics of smart indoor farms. A six-layer smart indoor farms architecture has also been proposed, which explains how data are collected using various sensors and devices and then transmitted onto the cloud infrastructure for further analysis and control through various layers. Artificial lighting, smart nutrition management, and artificial climate control, to name a few, are some of the important requirements for smart indoor farms while considering control and service management factors. The major bottleneck in installing such systems is both the economical and the technical constraints. However, with the evolution of technology (and when they become widely available in the near future), a more favourable farming scenario may emerge. Furthermore, smart indoor farms could be viewed as a potential answer for meeting the demands of a sustainable agricultural revolution as we move closer to Agriculture 4.0. Finally, in order to adapt smart indoor farms and their study scope, our work has presented various research areas to potential researchers.

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Section: Core Data Handling Layermentioning

confidence: 99%

Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution

Hati

Singh

2021

AgriEngineering

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“…A study [20] used soil related data of upazila of Khulna district in Bangladesh for soil classification and crop suggestion, collected data from soil resources development institute, bangladesh. Another study [21] are collected from 3010 images of rice plants with diseases from the high-standard rice experimental field of the hunan rice research institute in China to detect the rice plant diseases.…”

Section: Standard Data Repositoriesmentioning

confidence: 99%

“…Using machine learning techniques, suggests associated procedures, moisture techniques concerning the temperature. A study [20] used Machine learning algorithms such as Gaussian kernel-based Support Vector Machines (SVM), k-Nearest Neighbour (k-NN), and Bagged Trees are used for soil classification, but proposed Gaussian kernel-based Support Vector Machines (SVM) based method performs better than the k-Nearest Neighbour (k-NN), and Bagged Trees. (CNN) for identification of different diseases in different crops.…”

Section: Soil Classificationmentioning

confidence: 99%

A Review on the Role of Machine Learning in Agriculture

Veeragandham

Santhi

2020

SCPE

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Machine learning is a promising domain which is widely used now a days in the field of agriculture. The availability of manpower for agriculture is not enough and skill full farmers are less. Understanding the situation of the crop is not that much easy to detect and prevent the diseases in the crop. It is also widely employed in various agricultural fields such as topsoil management, yield management, water management, disease management and climate conditions. The machine learning models facilitate very fast and optimal decisions. The model of machine learning involves with training and testing to predict the accuracy of the result. The use of machine learning in agriculture helps to increase the productivity and better management on soil classification, disease detection, species management, water management, yield prediction, crop quality and weed detection. This article aims at providing detailed information on various machine learning approaches proposed in the past five years by emphasizing the advantage and disadvantages. It also compares different machine learning algorithms used in the modern agricultural field.

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“…The application of powerful, intelligent ML methods has the potential to transform the current productive agriculture into sustainable agriculture. In precision agriculture, complex tasks such as land classification, soil management, crop selection, seasonal variations, fertilizers management, crop yield forecasting, and yield gap analysis are effectively evolving with ML methods [6][7][8][9]. Because of incredible learning and high computational power, ML approaches have also been adopted in the field of oil palm for the mechanization of several tasks such as tree counting and plant health assessment [10,11].…”

Section: Introductionmentioning

confidence: 99%

Oil Palm and Machine Learning: Reviewing One Decade of Ideas, Innovations, Applications, and Gaps

et al. 2021

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Machine learning (ML) offers new technologies in the precision agriculture domain with its intelligent algorithms and strong computation. Oil palm is one of the rich crops that is also emerging with modern technologies to meet global sustainability standards. This article presents a comprehensive review of research dedicated to the application of ML in the oil palm agricultural industry over the last decade (2011–2020). A systematic review was structured to answer seven predefined research questions by analysing 61 papers after applying exclusion criteria. The works analysed were categorized into two main groups: (1) regression analysis used to predict fruit yield, harvest time, oil yield, and seasonal impacts and (2) classification techniques to classify trees, fruit, disease levels, canopy, and land. Based on defined research questions, investigation of the reviewed literature included yearly distribution and geographical distribution of articles, highly adopted algorithms, input data, used features, and model performance evaluation criteria. Detailed quantitative–qualitative investigations have revealed that ML is still underutilised for predictive analysis of oil palm. However, smart systems integrated with machine vision and artificial intelligence are evolving to reform oil palm agri-business. This article offers an opportunity to understand the significance of ML in the oil palm agricultural industry and provides a roadmap for future research in this domain.

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Soil Classification Using Machine Learning Methods and Crop Suggestion Based on Soil Series

Cited by 116 publications

References 1 publication

Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution

Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution

A Review on the Role of Machine Learning in Agriculture

Oil Palm and Machine Learning: Reviewing One Decade of Ideas, Innovations, Applications, and Gaps

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