This research focused on testing with maize, economical crop grown in Phetchabun province, Thailand, by installing a total of 20 sets of internet of things (IoT) devices which consist of soil moisture sensors and temperature and humidity sensors (DHT11). Data science tools such as rapidminer studio was used for data cleansing, data imputation, clustering, and prediction. Next, these data would undergo data cleansing in order to group them to obtain optimization clustering to identify the optimum condition and amount of water required to grow the maize through k-mean technique. From the analysis, the optimization result showed 3 classes and these data were further analyzed through prediction to identify precision. By comparing several algorithms including artificial neural network (ANN), decision tree, naïve bayes, and deep learning, it was found that deep learning algorithm can provide the most accurate result at 99.6% with root mean square error (RMSE)=0.0039. The algorithm obtained was used to write function to control the automated watering system to make sure that the temperature and humidity for growing maize is at appropriate condition. By using the improved watering system, it improved the efficacy of watering system which saves more water by 13.89%

<p>The internet of things (IoT) is a network of physical devices and is becoming a major area of innovation for computer-based systems. Agriculture is one of the areas which could be improved by utilizing this technology ranging from farming techniques to production efficiency. The objective of this research is to design an IoT to monitor local vegetable (Coriander; <em>Coriandrum sativum</em> L.) growth via sensors (light, humidity, temperature, water level) and combine with an automated watering system. This would provide planters with the ability to monitor field conditions from anywhere at any time. In this research, a group of local vegetables including coriander, cilantro, and dill weed were experimented. The prototype system consists of several smart sensors to accurately monitor the mentioned vegetable growth from seedling stage to a fully grown plant which will ensure the highest production levels from any field environment. Three different types coriander were measured under these parameters: height, trunk width, and leaf width. The result showed that IoT ecosystem for planting different types of coriander could produce effective and efficient plant growth and ready for harvest with a shorter time than conventional method.</p>

<p>In this modern age, several new methods have been developed, especially in image processing for agriculture business, which consists of technologies derived from artificial intelligence (AI) capabilities called machine learning. Classify is a widely used method to analyze patterns, trends, as well as the body of knowledge from the data visualization. Image classification application improves discrimination and prediction efficiency. The objective of this research was to feature extraction of sweet tamarind and compare the algorithm for classification. This research used images from golden sweet tamarind species with the use of MATLAB and Python language. The steps of this research consisted of 1) preprocessing step for finding the distance to appropriate of the image quality, 2) feature extracting for finding the number of black pixels and the number of white pixels, perimeter, diameter, and centroid, and 3) classifying for algorithms' comparison. The results showed that the camera's distance to the image was 60 cm. The coefficient of determination was at 0.9956, and the Standard Error of Estimate was 7,424.736 pixels. The conclusion of classification found that the random forest had the highest accuracy at 92.00%, SD. = 8.06, precision = 90.12, recall = 92.86, and F1-score = 91.36.</p>