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.

Thanks to social media, people are now able to leave guiding comments quickly about their favorite restaurants, movies, etc. This has paved the way for the field of sentiment analysis, which brings together various disciplines. In this study, Yelp restaurant reviews and IMDB movie reviews dataset were used together with the data collected from Twitter. Word2Vec (W2V), Global Vector (GloVe) and Bidirectional Encoder Representation (BERT) word embedding methods, Term Frequency-Reverse Document Frequency (TF-IDF), and the Bag-of-Words (BOW) were used on these datasets. Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Support Vector Machine (SVM), and Naive Bayes (NB) were used in the sentiment analysis models. Accuracy, F-measure (F), Sensitivity (Sens), Precision (Pre), and Receiver Operating Characteristics (ROC) were used in the evaluation of the model performance. The Accuracy rates of the models created by the Machine Learning (ML) and Deep Learning (DL) methods using the IMDB dataset were in the range of 81%-90% and 84%-94%, respectively. These rates were in the range of 80%-86% and 81%-89% for the Yelp dataset, and in the range of 75%-79% and 85%-98% for the Twitter dataset. The models that incorporated the BERT word embedding method have the best performance, compared to the other models with ML and DL. Therefore, BERT method is recommended for this type of analysis in future studies.

The first place to get ideas on all the activities considered to occur in everyday life was the comments on the websites. This is an area that deals with these interpretations in the natural language processing, which is a sub-branch of artificial intelligence. Sentiment analysis studies, which is a task of natural language processing are carried out to give people an idea and even guide them with such comments. In this study, sentiment analysis was implemented on public user feedback on websites in two different areas. TripAdvisor dataset includes positive or negative user comments about hotels. And Rotten Tomatoes dataset includes positive (fresh) or negative (rotten) user comments about films. Sentiments analysis on datasets have been carried out by using Word2Vec word embedding model, which learns the vector representations of each word containing the positive or negative meaning of the sentences, and the Term Frequency Inverse Document Frequency text representation model with four machine learning methods (Naïve Bayes-NB, Support Vector Machines-SVM, Logistic Regression-LR, K-Nearest Neighbour-kNN) and two ensemble learning methods (Stacking, Majority Voting-MV). Accuracy and F-measure is used as a performance metric experiments. According to the results, Ensemble learning methods have shown better results than single machine learning algorithms. Among the overall approaches, MV outperformed Stacking.

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.