Exploration of machine learning methods for prediction and assessment of soil properties for agricultural soil management: a quantitative evaluation

Motia, Sanjay; Reddy, S. R. N.

doi:10.1088/1742-6596/1950/1/012037

Cited by 16 publications

(7 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine learning was defined in the 2018 book “Foundations of Machine Learning” as computational processes that utilize historic data and past experiences to modify, improve, repair and predict future performance accurately [ 34 ]. Machine learning in sustainable agriculture latest utilization is in optimizing supply chains [ 35 ], in-field monitoring [ 36 ], soil temperature prediction [ 37 ] and sustainable soil management [ 38 ]. The different types of machine learning technologies that can be implemented to foster sustainable production are decision trees, neural networks, polynomial predictive methods and K-nearest neighbors [ 39 ].…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis

2022

View full text Add to dashboard Cite

There is a dearth of literature that provides a bibliometric analysis concerning the role of Artificial Intelligence (AI) in sustainable agriculture therefore this study attempts to fill this research gap and provides evidence from the studies conducted between 2000–2021 in this field of research. The study is a systematic bibliographic analysis of the 465 previous articles and reviews done between 2000–2021 in relation to the utilization of AI in sustainable methods of agriculture. The results of the study have been visualized and presented using the VOSviewer and Biblioshiny visualizer software. The results obtained post analysis indicate that, the amount of academic works published in the field of AI’s role in enabling sustainable agriculture increased significantly from 2018. Therefore, there is conclusive evidence that the growth trajectory shows a significant climb upwards. Geographically analysed, the country collaboration network highlights that most number of studies in the realm of this study originate from China, USA, India, Iran, France. The co-author network analysis results represent that there are multi-disciplinary collaborations and interactions between prominent authors from United States of America, China, United Kingdom and Germany. The final framework provided from this bibliometric study will help future researchers identify the key areas of interest in research of AI and sustainable agriculture and narrow down on the countries where prominent academic work is published to explore co-authorship opportunities.

show abstract

Section: Discussionmentioning

confidence: 99%

Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis

2022

View full text Add to dashboard Cite

show abstract

“…Considering that, India's economy depends heavily on agriculture. A significant portion of the world's population depended on agriculture for food and a livelihood of indispensable [6]. It is essential to the production of food as well as employment opportunities for a significant community.…”

Section: Introductionmentioning

confidence: 99%

Soil Health Analysis and Classification of Micronutrients for Crop Suggestion Using Machine Learning

N.,

Prasad K

2024

IJMCR

View full text Add to dashboard Cite

An essential component of agriculture is soil. Many types of soil exists. Different parameters can be observed in each type of soil, and various crops can be grown on different types of soils. To understand which crops develop in certain types of soil, they need to be informed on the characteristics and features of various soil types. The capability to identify the inputs needed for productive and cost-effective farming was provided by soil analysis. A proper soil test will ensure that the proper amount of fertilizers are utilized, providing the crop to meet its needs while also profiting from the nutrients already available in the soil. The phrase "soil health" refers to the biological, chemical, and physical elements of the soil that are significant for plant nutrients. Soil analysis is a collection of different chemical processes that determines the amounts of available plant nutrients in the soil. The most important factors for producing a healthy crop are micronutrient analysis. The classification of soil's micronutrients is the primary objective of this analysis. In this case, machine learning methods may be helpful. It has made significant progress in recent years. Hence in this analysis, soil health analysis and classification of Micronutrients for crop suggestion using Machine Learning is presented. The coloured images of the soil samples are obtained and processed using a number of algorithms and filters. Color, texture, and other features can be extracted using these developed algorithms. Logistic Regression algorithm is used to classify micronutrients.

show abstract

“…To make precise decisions depends on the crop type which has to be planted and gains a good yield, data like usage of fertilizers, pesticides, meteorological, and soil information has to be made available to the agriculturalists promptly and accurately. Better crop production is reached by farmers via study of the appropriate circumstances, thus, minimizing the damages and crop loss that arises because of the unfavorable situations ( Motia & Reddy, 2020 ). Numerous hybrids ( Słowik & Cpałka, 2021 ) and high yielding varieties of plants were produced daily.…”

Section: Introductionmentioning

confidence: 99%

Enhanced reptile search optimization with convolutional autoencoder for soil nutrient classification model

Raman¹,

Chelliah²

2023

PeerJ

View full text Add to dashboard Cite

Background Soil nutrients play an important role in soil fertility and other environmental factors. Soil testing is an effective tool for evaluating soil nutrient levels and calculating the appropriate quantitative of soil nutrients based on fertility and crop requirements. Because traditional soil nutrient testing models are impractical for real-time applications, efficient soil nutrient and potential hydrogen (pH) prediction models are required to improve overall crop productivity. Soil testing is an effective method to evaluate the presence of nutrient status of soil and assists in determining appropriate nutrient quantity. Methods Various machine learning (ML) models proposed, predict the soil nutrients, soil type, and soil moisture. To assess the significant soil nutrient content, this study develops an enhanced reptile search optimization with convolutional autoencoder (ERSOCAE-SNC) model for classifying and predicting the fertility indices. The model majorly focuses on the soil test reports. For classification, CAE model is applied which accurately determines the nutrient levels such as phosphorus (P), available potassium (K), organic carbon (OC), boron (B) and soil pH level. Since the trial-and-error method for hyperparameter tuning of CAE model is a tedious and erroneous process, the ERSO algorithm has been utilized which in turn enhances the classification performance. Besides, the ERSO algorithm is derived by incorporating the chaotic concepts into the RSO algorithm. Results Finally, the influence of the ERSOCAE-SNC model is examined using a series of simulations. The ERSOCAE-SNC model reported best results over other approaches and produces an accuracy of 98.99% for soil nutrients and 99.12% for soil pH. The model developed for the ML decision systems will help the Tamil Nadu government to manage the problems in soil nutrient deficiency and improve the soil health and environmental quality. Also reduces the input expenditures of fertilizers and saves time of soil experts.

show abstract

Exploration of machine learning methods for prediction and assessment of soil properties for agricultural soil management: a quantitative evaluation

Cited by 16 publications

References 60 publications

Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis

Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis

Soil Health Analysis and Classification of Micronutrients for Crop Suggestion Using Machine Learning

Enhanced reptile search optimization with convolutional autoencoder for soil nutrient classification model

Contact Info

Product

Resources

About