Machine learning (ML) refers to the processes that enable computers to think based on various learning methods. It can be also called domain which is a subset of Artificial Intelligence (AI). Deep learning (DL) has been a promising, new and modern technique for data analysis in recent years. It can be shown as the improved version of Artificial Neural Networks (ANN) which is one of the popular AI methods of today. The population of the world is increasing day by day and the importance of agriculture is also increasing in parallel. Because of this, many researchers have focused on this issue and have tried to apply machine learning and deep learning methods in agriculture under the name of smart farm technologies both to increase agricultural production and to solve some challenges of agriculture. In this study, it is aimed to give detailed information about these up-to-date studies. 77 articles based on machine learning and deep learning algorithms in the agriculture field and published in IEEE Xplore, ScienceDirect, Web of Science and Scopus publication databases between 2016 and 2020 years were reviewed. The articles were classified under five categories as plant recognition, disease detection, weed and pest detection, soil mapping-drought index, and yield forecast. They were examined in detail in terms of machine learning/deep learning architectures, data sets, performance metrics (Accuracy, Precision, Recall, F-Score, R2, MAPE, RMSE, MAE), and the obtained experimental results. Based on the examined articles, the most popular methods, used data sets/types, chosen performance criteria, and performance results among the existing studies are presented. It is seen that the number of AI-based applications related to agriculture is increasing compared to the past and the sustainability in productivity is so promising.

The production of agricultural products and the high yield in these products are of critical importance for the continuation of human life. In recent years, machine learning and deep learning technologies have been widely used in determining agricultural productivity. The purpose of this study was to estimate the yield of apple fruit by using a novel deep learning-based hybrid method. First, by using images belonging to the golden and royal gala apple varieties, a classification was made with the help of a convolutional neural network (CNN) that was designed for the study. Then, using classical machine learning algorithms and bagging and boosting algorithms, a hybrid application was performed by classifying the images whose feature extractions were done with the designed CNN. The results of the study, presented on 4 separate datasets (Datasets A, B, C, and D), were evaluated based on accuracy, precision, recall, F-measure, and Cohen kappa scores. Considering the accuracy results for Datasets B, C, and D, it was determined that the hybrid model that gave the best result was the CNN-SVM model. For Dataset A, the CNN-SVM and CNN-Gradient Boosting hybrid models gave the best and same accuracy. Dataset C was determined as the most appropriate dataset in terms of the more balanced distribution of train, test, and validation size in the datasets, the results of the proposed hybrid CNN model, and the evaluation of the results of the model. For Dataset C, it was found that the accuracy of the hybrid model was 99.70%. Precision, recall, f-measure, and Cohen kappa scores were 99%. The results of the study revealed that the hybrid models showed effective results in determining the productivity of apple fruit through images belonging to the golden and royal gala varieties.

Electrical load forecasting (ELF) is gaining importance especially due to the severe impact of climate change on electrical energy usage and dynamically evolving smart grid technologies in the last decades. In this regard, medium-term load forecasting, a crucial need for power system planning (generation optimization and outages plan) and operation control, has become prominent in particular. Machine learning and deep learning-based techniques are currently trending approaches in electrical load estimation due to their capability to model complex non-linearity, feature abstraction and high accuracy, especially in the smart power systems environment. In this study, several load forecasting models based on machine learning methods which comprise linear regression (LR), decision tree (DT), random forest (RF), gradient boosting, adaBoost, and deep learning techniques such as recurrent neural network (RNN) and long short-term memory (LSTM) are studied for medium-term electrical load demand forecasting at an aggregated level. Performance metric results of these analyzes are presented in detail. State-of-the-art feature selection models are examined on the dataset and their effects on these forecasting methods are evaluated. Numerical results show that forecasting performance can be significantly improved. These results are validated by the results of other studies on the subject and found to be superior.

Gouty arthritis (GA) and its association with kidney failure present significant challenges in healthcare, necessitating effective detection and management strategies. GA, characterized by the deposition of monosodium urate crystals in joints and other tissues, leads to inflammation and severe joint pain, often accompanied by metabolic comorbidities such as myocardial infarction and diabetes. Although GA has been widely studied in the medical field, limited research has explored the use of machine learning (ML) to identify key biomarkers affecting disease progression. This study aims to bridge this gap by leveraging ML models for predictive analysis. In this study, machine learning models such as decision trees, random forests, logistic regression, and artificial neural networks were used to classify GA using demographic, clinical, and laboratory data, and, most importantly, to identify the factors that affect GA. The analysis yielded promising results, with the decision tree model achieving the highest accuracy of 92.85%. Moreover, key factors such as urea, creatinine, and hemoglobin levels were identified during the initial attack, shedding light on the pathophysiology of GA. This study demonstrates how ML methods help identify key factors affecting GA and assist in disease management. By leveraging machine learning techniques, it is possible to refine the factors affecting GA and inform personalized interventions, ultimately improving patient care and outcomes.

Transportation refers to a process based on the movement of people or vehicles from one place to another. Sea routes and roads have existed for centuries. They generally play a very important role in people's daily life, trade and industrial activities. Highway, a mode of transportation, is the first preferred mode of transportation worldwide. However, various signs and rules have been set by the authorities to prevent chaos on the highways. Traffic signs are the most important of these rules. In this study, transfer learning models (VGG16, VGG19, Xception and EfficientNet) are used to classify traffic signs using a state-of-art traffic signs dataset (German Traffic Sign Detection Benchmark-GTSDB). Accuracy was used as the classification evaluation criterion. The CNN model designed for the study gave the best result with an accuracy rate of 98% and a model competing with the literature was proposed.

Creating balanced datasets is a significant challenge that substantially affects the performance of machine learning models in the classification of agricultural products. In this research, we tried to overcome this challenge by using an unbalanced dataset containing information on 7 date palm (Phoenix dactylifera L.) and 2 pistachio (Pistacia vera L.) cultivars. The aim of the study is to compare the classification performance of machine learning models on an unbalanced dataset and a balanced dataset using the SMOTE technique. Initially, classification was performed on the unbalanced dataset using machine learning approaches. Among the machine learning models applied on the unbalanced dataset, the Linear-SVM model showed the highest accuracy rate with an accuracy rate of 92,62%. In the data set extended by applying the SMOTE technique, the RBF-SVM model again showed the highest accuracy rate with 95,55% accuracy rate. In summary, our study highlights the difficulties in machine learning-based agricultural crop classification due to data unbalances. Utilizing the SMOTE technique for oversampling was effective in overcoming this obstacle and improving classification accuracy.