Soil Classification and Crop Prediction Using Machine

Soil classification is a major problem and a heated topic in many countries. The world's population is drastically increasing at an alarming rate which in turn makes the demand for food crops. Farmers are forced to block soil cultivation since their conventional methods are insufficient to fulfil escalating needs. To optimize agricultural output, farmers must understand the best soil type for a certain crop, which has an impact on growing food demand. There areseveral methods for categorizing soil in a scientific way, but each has its own set of disadvantages, such as time and effort. Computer-based soil classification approaches are essential since they will aid farmers in the field and will be quick. Advanced Machine Learning technique-based soil classification methodologies can be used to classify soil and extract various featuresfrom it

The effects of multiple layers feed-forward neural network transfer function in digital based Ethiopian soil classification and moisture prediction

In the area of machine learning performance analysis is the major task in order to get a better performance both in training and testing model. In addition, performance analysis of machine learning techniques helps to identify how the machine is performing on the given input and also to find any improvements needed to make on the learning model. Feed-forward neural network (FFNN) has different area of applications, but the epoch convergences of the network differs from the usage of transfer function. In this study, to build the model for classification and moisture prediction of soil, rectified linear units (ReLU), Sigmoid, hyperbolic tangent (Tanh) and Gaussian transfer function of feed-forward neural network had been analyzed to identify an appropriate transfer function. Color, texture, shape and brisk local feature descriptor are used as a feature vector of FFNN in the input layer and 4 hidden layers were considered in this study. In each hidden layer 26 neurons are used. From the experiment, Gaussian transfer function outperforms than ReLU, sigmoid and tanh transfer function. But the convergence rate of Gaussian transfer function took more epoch than ReLU, Sigmoid and tanh

Application of an Artificial Neural Network for the CPT-based Soil Stratigraphy Classification

Subsurface soil profiling is an essential step in a site investigation. The traditional methods for in situ investigations, such as SPT borings and sampling, have been progressively replaced by CPT soundings since they are fast, repeatable, economical and provide continuous parameters of the mechanical behaviour of the soils. However, the derived CPT-based stratigraphy profiles might present noisy thin layers, and its soil type description might not reflect a textural-based classification (i.e. Universal Soil Classification System, USCS). Thus, this paper presents a straightforward artificial neural network (ANN) algorithm, to classify CPT soundings according to the USCS. Data for training the model have been retrieved from SPT-CPT pairs collected after the 2011 Christchurch earthquake in New Zealand. The application of the ANN to case studies show how the method is a cost-effective and time-efficient approach, but more input parameters and data are needed for increasing its performance

Smartphone assisting convolutional neural networks for soil texture classification in dry and wet humid conditions in West Guwahati, Assam

Soil texture using a hydrometer or pipette method requires expertise, although these are accurate. A soil expert may help the farmer to detect the soil texture by analyzing the visual texture of the soil, which is not always accurate. This paper presents the smartphone image-based sand and clay soil classification in wet and dry humid conditions using Self Convolution Neural Network (SCNN) and finetuned MobileNet.A soil dataset of 576 soil images was prepared using a low-cost smartphone under natural light conditions. Different augmentation techniques such as shift, range, rotation, and zoom were applied to the soil dataset to increase the number of images in the soil dataset. The best performance of the MobileNet was reported at epoch 15 with a testing and training loss of 0.0091 and 0.0194, respectively. Though the SCNN model performed best at epoch 10 with a testing accuracy of 99.85%, the MobileNet reported less computation time (167.8s) than the SCNN (273.2s). The precision and recall of the models were 99.62 (MobileNet) and 99.84 (SCNN). The accuracy of the SCNN reported itself as the best model, whereas the computing time of the MobileNet reported itself as the best model in different humid conditions. The model can be used to replicate the traditional soil texture analysis method and the farmers can use it for better productivity

Métodos de aprendizado de máquina para classificação de solos utilizando parâmetros in situ

TCC (graduação) - Universidade Federal de Santa Catarina, Campus Joinville, Ciência e Tecnologia.Analisar e subsequentemente classificar o solo é parte importante de muitos processos de engenharia. Porém os testes de laboratório são custosos e demorados e seus resultados se referem apenas à localização da qual a amostra foi obtida, precisando extrapolar os resultados para a aplicação em grandes áreas. Ensaios de campo, como o CPTu, têm sido usados devido a velocidade de testagem e consistência dos resultados. Esse estudo busca classificar os solos usando algoritmos de aprendizado de máquina como: árvore de decisão, floresta aleatória, KNN, regressão logística e naive Bayes Gaussiano. Para o treinamento e teste é usado um dataset desbalanceado de 1.862 amostras, obtidas combinando diversos datasets que contém testes feitos em vários países com características de solo diferentes. Os testes feitos em cada solo é o CPTu e ensaio de laboratório para mensurar a densidade real dos grãos e o peso específico do solo. Obtiveram-se resultados promissores, com acurácia superior a 93%.Analyzing the soil and classifying it is an important part of many engineering processes. However laboratory tests are expensive and time-consuming and its results apply only to the sampling location and it needs to be extrapolated before being applied to bigger areas. Field tests, such as CPTu, have been used due to its testing speed, and result consistency. This study aims to classify soils using machine learning algorithms, using decision tree, random forest, KNN, logistic regression and Gaussian Naive Bayes. To test and train the model, an unbalanced dataset of 1862 samples concatenates multiple datasets that are composed from tests made in multiple countries with different soil characteristics. The tests made on each soil sample are CPTu and laboratory tests to measure the soil specific gravity and soil specific weight. We succeeded at obtaining promising results, with accuracy superior to 93%

Estimation of the rock deformation modulus and RMR based on data mining techniques

In this work Data Mining tools are used to develop new and innovative models for the estimation of the rock deformation modulus and the Rock Mass Rating (RMR). A database published by Chun et al. (2008) was used to develop these models. The parameters of the database were the depth, the weightings of the RMR system related to the uniaxial compressive strength (UCS), the rock quality designation (RQD), the joint spacing (JS), the joint condition (JC), the groundwater condition (GWC) and the discontinuity orientation adjustment (DOA), the RMR and the deformation modulus. As a modelling tool the R program environment was used to apply these advanced techniques. Several algorithms were tested and analysed using different sets of input parameters. It was possible to develop new models to predict the rock deformation modulus and the RMR with improved accuracy and, additionally, allowed to have an insight of the importance of the different input parameters.Fundação para a Ciência e a Tecnologia (FCT

Predicting the suitability of lateritic soil type for low cost sustainable housing with image recognition and machine learning techniques

From a sustainability point of view, laterites-compressed earth bricks (LCEB) are a promising substitute for building structures in place of the conventional concrete masonry units. On the other hand, techniques for identifying and classifying laterites soil for compressed earth bricks (CEB) production are still relying on direct human expertise or ‘experts’. Human experts exploit direct visual inspection and other basic senses such as smelling, touching or nibbling to generate a form of binomial classification, i.e. suitable or unsuitable. The source of predictive power is otherwise supposed to be found in color, scent, texture or combinations of these. Lack of clarity regarding the actual method and the possible explanatory mechanisms lead to 1) difficulties to train other people into the skills and 2) might also add to apathy to using CEB masonry units for housing. Here we systematize the selection method of experts. We chose imaging analysis techniques based on 1) easiness in image acquisition (Digital Camera) and 2) availability of machine learning and statistical techniques. We find that most of the predictive power of the ‘expert’ can be packed into visual inspection by demonstrating that with image analysis alone we get a 98% match. This makes it practically unnecessary the study of any other ‘expert’ skills and provides a method to alleviate the housing problems dealing with material construction in the developing world

Soil Classification Resorting to Machine Learning Techniques

Soil classification is the act of resuming the most relevant information about a soil profile into a single class, from which we can infer a large amount of properties without extensive knowledge of the subject. These classes then make the communication of soils, and how they can best be used in areas such as agriculture and forestry, simpler and easier to understand. Unfortunately soil classification is expensive and requires that specialists perform varied experiments, to be able to precisely attribute a class to a soil profile. This master’s thesis focuses on machine learning algorithms for soil classification mainly based on its intrinsic attributes, in the Mexico region. The data set used contains 6 760 soil profiles, the 19 464 horizons that constitute them, as well as physical and chemical properties, such as pH or organic content, belonging to those horizons. Four data modelling methods were tested (i.e., standard depths, n first layers, thickness, and area weighted thickness), as well as different values for a k-Nearest Neighbours imputation. A comparison between state of the art machine learning algorithms was also made, namely Random Forests, Gradient Tree Boosting, Deep Neural Networks and Recurrent Neural Networks. All of our modelling methods provided very similar results, when properly parametrised, reaching Kappa values of 0.504 and an accuracy of 0.554, with the standard depths method providing the most consistent results. The k parameter for the imputation showed very little impact on the variation on the results. Gradient Tree Boosting was the algorithm with the best overall results, closely followed by the Random Forests model. The neuron based methods never achieved a Kappa score over 0.4, therefore providing substantially worse results.A classificação de solos é o ato de resumir a informação sobre um perfil do solo em uma única classe, da qual é possivel inferir várias propriedades, mesmo com a ausência de conhecimento sobre a área de estudo. Estas classes fazem a comunicação dos solos e de como estes podem ser usados, em áreas como a agricultura e silvicultura, mais simples de perceber. Infelizmente a classificação de solos é dispendiosa, demorada, e requer especialistas para realizar as experiências necessárias para classificar corretamente o solo em causa. A presente tese de mestrado focou-se na avaliação de algoritmos de aprendizagem automática para o problema de classificação de solos, baseada maioritariamente nos atributos intrínsecos destes, na região do México. Foi utilizada uma base de dados contendo 6 760 perfis de solos, os 19 464 horizontes que os constituem, e as propriedades químicas e físicas, como o pH e a percentagem de barro, pertencentes a esses horizontes. Quatro métodos de modelação de dados foram testados (standard depths, n first layers, thickness, e area weighted thickness), tal como diferentes valores para uma imputação baseada em k-Nearest Neighbours. Também foi realizada uma comparação entre algoritmos de aprendizagem automática, nomeadamente Random Forests, Gradient Tree Boosting, Deep Neural Networks e Recurrent Neural Networks. Todas as modelações de dados providenciaram resultados similares, quando propriamente parametrisados, atingindo valores de Kappa de 0.504 e accuracy de 0.554, sendo que o métdodo standard depths obteve uma performance mais consistente. O parâmetro k, referente ao método de imputação, revelou ter pouco impacto na variação dos resultados. O algoritmo Gradient Tree Boosting foi o que obteve melhores resultados, seguido de perto pelo modelo de Random Forests. Os métodos baseados em neurónios tiveram resultados substancialmente piores, nunca superando um valor de Kappa de 0.4

Recent advances and future challenges for artificial neural systems in geotechnical engineering applications

Published as Open Access article.Artificial neural networks (ANNs) are a form of artificial intelligence that has proved to provide a high level of competency in solving many complex engineering problems that are beyond the computational capability of classical mathematics and traditional procedures. In particular, ANNs have been applied successfully to almost all aspects of geotechnical engineering problems. Despite the increasing number and diversity of ANN applications in geotechnical engineering, the contents of reported applications indicate that the progress in ANN development and procedures is marginal and not moving forward since the mid-1990s. This paper presents a brief overview of ANN applications in geotechnical engineering, briefly provides an overview of the operation of ANN modeling, investigates the current research directions of ANNs in geotechnical engineering, and discusses some ANN modeling issues that need further attention in the future, including model robustness; transparency and knowledge extraction; extrapolation; uncertainty.Mohamed A. Shahin, Mark B. Jaksa and Holger R. Maie