Soil Nutrients and pH Level Testing Using Multivariate Statistical Techniques for Crop Selection
01 Jan 2021-pp 485-498
TL;DR: In this paper, a multivariate data analysis technique is used to determine the highly impacted data in soil and crop growth, the importance and relationship between soil variables were factored by using the regression analysis technique.
Abstract: The multivariate data analysis technique is used to determine the highly impacted data in soil and crop growth. The importance and relationship between soil variables were factored by using the regression analysis technique. The correlation matrix technique was used for comparing several variables to correlate positive and negative signs. From the soil testing procedure and understanding of results, it shows that soil nutrients and pH level have a powerful effect on variation in the usage of fertilizers, crop selection, and high crop yield. pH determination can be used to indicate whether the soil is suitable for the plant's growth or in need of adjustment to produce optimum plant growth. Based upon the predictive analysis results, nitrogen and potassium content are naturally high compared to other soil nutrients of this region and suggested fertilizers required for crop growth. To produce healthy crop yield, farmers should select the crops as per soil types, nutrients level, and pH level.
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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.
Abstract: Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.
1,262 citations
TL;DR: In this paper, the ability of mid-infrared diffuse reflectance spectroscopy (4000-602 cm(-1)) to predict chemical and textural properties for a globally distributed soil spectral library was evaluated.
Abstract: Globally applicable calibrations to predict standard soil properties based on infrared spectra may increase the use of this reliable technique. The objective of this study was to evaluate the ability of mid-infrared diffuse reflectance spectroscopy (4000-602 cm(-1)) to predict chemical and textural properties for a globally distributed soil spectral library. We scanned 971 soil samples selected from the International Soil Reference and Information Centre database. A high-throughput diffuse reflectance accessory was used with optics that exclude specular reflectance as a potential source of error. Archived data on soil chemical and physical properties were calibrated to first derivative spectra using partial least-squares regression. Good predictions for the spatially independent validation set were achieved for pH value, organic C content, and cation exchange capacity (CEC) (n = 291, r(2) of linear regression of predicted against measured values >= 0.75 and ratio of standard deviation of measured values to root mean square error of prediction (RPD) >= 2.0). The root mean square errors of prediction (RMSEP) were 0.75 pH units, 9.1 g organic C kg(-1) and 5.5 cmol(c) CEC kg(-1). Satisfactory predictions (r(2) = 0.65-0.75, RPD = 1.4-2.0) were obtained for exchangeable Mg concentration and clay content. The respective RMSEPs were 4.3 cmol(c) kg(-1) and 126 g kg(-1). Poorer predictions (r2 = 0.61 and 0.64) were achieved for sand and exchangeable Ca contents. Although RMSEP values are large relative to laboratory analytical errors, our results suggest a marked potential for the global spectral library as a tool for advice on land management, such as the classification of new samples into basic soil fertility classes based on organic C and clay contents, CEC, and pH. Further research is needed to test the stability of this global calibration on new data sets.
167 citations
TL;DR: This survey incorporates an overview of some of the existing supervised and unsupervised machine learning models associated with the crop yield in literature and compares one approach with other using various error measures like Root Mean Square Error (RMSE) and Coefficient of Determination (R2).
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TL;DR: This survey presents some of the most used data mining techniques in the field of agriculture, such as the k-means, the k nearest neighbor, artificial neural networks and support vector machines, and an application in agriculture for each of these techniques.
Abstract: In this survey we present some of the most used data mining techniques in the field of agriculture. Some of these techniques, such as the k-means, the k nearest neighbor, artificial neural networks and support vector machines, are discussed and an application in agriculture for each of these techniques is presented. Data mining in agriculture is a relatively novel research field. It is our opinion that efficient techniques can be developed and tailored for solving complex agricultural problems using data mining. At the end of this survey we provide recommendations for future research directions in agriculture-related fields.
122 citations
TL;DR: The association of VIS-NIR spectral data to landforms, vegetation classes, and soil types demonstrate potential for soil characterization and are evaluated for soil properties in the Central Amazon, Brazil.
Abstract: Visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR) has shown levels of accuracy comparable to conventional laboratory methods for estimating soil properties. Soil chemical and physical properties have been predicted by reflectance spectroscopy successfully on subtropical and temperate soils, whereas soils from tropical agro-forest regions have received less attention, especially those from tropical rainforests. A spectral characterization provides a proficient pathway for soil characterization. The first step in this process is to develop a comprehensive VIS-NIR soil library of multiple key soil properties to be used in future soil surveys. This paper presents the first VIS-NIR soil library for a remote region in the Central Amazon. We evaluated the performance of VIS-NIR for the prediction of soil properties in the Central Amazon, Brazil. Soil properties measured and predicted were: pH, Ca, Mg, Al, H, H+Al, P, organic C (SOC), sum of bases, cation exchange capacity (CEC), percentage of base saturation (V), Al saturation (m), clay, sand, silt, silt/clay (S/C), and degree of flocculation. Soil samples were scanned in the laboratory in the VIS-NIR range (350–2500 nm), and forty-one pre-processing methods were tested to improve predictions. Clay content was predicted with the highest accuracy, followed by SOC. Sand, S/C, H, Al, H+Al, CEC, m and V predictions were reasonably good. The other soil properties were poorly predicted. Among the soil properties predicted well, SOC is one of the critical soil indicators in the global carbon cycle. Besides the soil property of interest, the landscape position, soil order and depth influenced in the model performance. For silt content, pH and S/C, the model performed better in well-drained soils, whereas for SOC best predictions were obtained in poorly drained soils. The association of VIS-NIR spectral data to landforms, vegetation classes, and soil types demonstrate potential for soil characterization.
105 citations