Population explosion and the need to provide food for humankind and to increase the quality and quantity of food products in order to ensure food security with regard to its managerial, environmental, and developmental aspects has led to the use of new agriculture methods and technologies. Today, different approaches, such as plant breeding, genetically modified foods, in vitro planting, and the spread of closed ecological systems, are applied as a solution to increase food accessibility. In addition to these methods, using modern technologies under the title of precision agriculture have been proposed as ways to achieve food security. Early intervention to prevent the occurrence of unwanted events with the ability to monitor crops in all stages of production, from tillage to post harvest, are provided by using various methods, such as remote sensing and geographic information systems. In addition to the economic and environmental impacts of precision agriculture, expanding farmers' awareness through the use of modern technologies and the integration of scattered lands to achieve sustainability are known as social impacts of precision agriculture. In this manuscript we have tried to summarize the different methods of precision agriculture that could affect food security in its multiple branches.

In this study, blackberry fruits were dried in a pilot-scale hot-air dryer to identify the drying characteristics of the fruits. The air velocity was set as 1 m·s−1, and the temperature range was set as 54 to 75 °C. Fick’s law of diffusion was used to describe heat transfer during the decreasing rate period. Effective diffusivity values were calculated, and the Arrhenius constitutive model was used to describe the temperature dependence of these values. The Page, logarithmic, approximation of diffusion, two-term, and Midilli et al. models were used to fit the experimental data. A nonlinear regression analysis was used to calculate rate constants and model coefficients. The present findings revealed that among the tested models, the Midilli et al. model best described the experimental data; therefore, it was concluded that the model could be used to describe the drying characteristics of blackberry fruits.

Classification of hazelnuts is one of the values adding processes that increase the marketability and profitability of its production. While traditional classification methods are used commonly, machine learning and deep learning can be implemented to enhance the hazelnut classification processes. This paper presents the results of a comparative study of machine learning frameworks to classify hazelnut (Corylus avellana L.) cultivars (‘Sivri’, ‘Kara’, ‘Tombul’) using DL4J and ensemble learning algorithms. For each cultivar, 50 samples were used for evaluations. Maximum length, width, compression strength, and weight of hazelnuts were measured using a caliper and a force transducer. Gradient boosting machine (Boosting), random forest (Bagging), and DL4J feedforward (Deep Learning) algorithms were applied in traditional machine learning algorithms. The data set was partitioned into a 10-fold-cross validation method. The classifier performance criteria of accuracy (%), error percentage (%), F-Measure, Cohen’s Kappa, recall, precision, true positive (TP), false positive (FP), true negative (TN), false negative (FN) values are provided in the results section. The results showed classification accuracies of 94% for Gradient Boosting, 100% for Random Forest, and 94% for DL4J Feedforward algorithms.

One important goal of precision horticulture (PH), as well as precision agriculture (PA), is to measure and manage spatial and temporal variation in orchards. In this study, temporal and spatial analysis of yields were carried out over 2 years for a 0.5-ha apple orchard (at the Haymana Research Station of Ankara University, Turkey, from 2017 to 2018) to determine the variability of yields over time and included seven apple varieties: ‘Royal Gala’, ‘Red Chief’, ‘Braeburn’, ‘Mondial Gala’, ‘Jonagold’, ‘Fuji’, and ‘Mitch Gala’. To achieve this, yield data for two different years were analyzed for mean yield, temporal variance, and cv in terms of spatial and temporal stability, and their yield maps were produced. The results showed that ‘Jonagold’, ‘Braeburn’, and ‘Red Chief’ varieties yielded less than the average yield, whereas the other varieties produced average yields when the yield from 2 years was taken into account. Calculation of the values for determining temporal stability over time resulted in all existing varieties being identified as stable over time. For example, the ‘Jonagold’ and ‘Red Chief’ varieties showed 100% stability in terms of temporal variance. Results also showed that the ‘Gala’ varieties were stable for 2 years and produced high yields, whereas the other varieties were specified as stable and low yielding when spatial and temporal variability was considered in combination.