The implementation of intelligent technology in agriculture is seriously investigated as a way to increase agriculture production while reducing the amount of human labor. In agriculture, recent technology has seen image annotation utilizing deep learning techniques. Due to the rapid development of image data, image annotation has gained a lot of attention. The use of deep learning in image annotation can extract features from images and has been shown to analyze enormous amounts of data successfully. Deep learning is a type of machine learning method inspired by the structure of the human brain and based on artificial neural network concepts. Through training phases that can label a massive amount of data and connect them up with their corresponding characteristics, deep learning can conclude unlabeled data in image processing. For complicated and ambiguous situations, deep learning technology provides accurate predictions. This technology strives to improve productivity, quality and economy and minimize deficiency rates in the agriculture industry. As a result, this article discusses the application of image annotation in the agriculture industry utilizing several deep learning approaches. Various types of annotations that were used to train the images are presented. Recent publications have been reviewed on the basis of their application of deep learning with current advancement technology. Plant recognition, disease detection, counting, classification and yield estimation are among the many advancements of deep learning architecture employed in many applications in agriculture that are thoroughly investigated. Furthermore, this review helps to assist researchers to gain a deeper understanding and future application of deep learning in agriculture. According to all of the articles, the deep learning technique has successfully created significant accuracy and prediction in the model utilized. Finally, the existing challenges and future promises of deep learning in agriculture are discussed.

Many different Artificial Neural Networks (ANN) models of flood have been developed for forecast updating. However, the model performance, and error prediction in which forecast outputs are adjusted directly based on models calibrated to the time series of differences between observed and forecast values, are very interesting and challenging task. This paper presents an improved lead time flood forecasting using Non-linear Auto Regressive Exogenous Neural Network (NARXNN), which shows better performance in term of forecast precision and produces minimum error compared to neural network method using Radial Basis Function (RBF) in examined 12-hour ahead of time. First, RBF forecasting model was employed to predict the flood water level of Kelantan River at Kuala Krai, Kelantan, Malaysia. The model is tested for 1-hour and 7-hour ahead of time water level at flood location. The same analysis has also been taken by NARXNN method. Then, a non-linear neural network model with exogenous input promoted with enhancing a forecast lead time to 12-hour. Both about the performance comparison has briefly been analyzed. The result verified the precision of error prediction of the presented flood forecasting model.

Gold is a precious metal once widely used as a standard for monetary exchange but was replaced by paper currency mostly used today. However interest in gold trading and investment has resurfaced recently in Malaysia probably due to its price stability. Samples of gold that are used as investment include the Kijang Emas, Public Dinar and the Public Gold which are currently available to the general public in Malaysia. This project will involve developing a system to aid a gold investor in deciding the best time in the future to buy or sell gold. The system developed is based on existing gold data time series and algorithms based on Artificial Neural Networks. The system should be able to give daily prediction to its users.

Intensively monitoring river water level and flows in both upstream and downstream catchments are essential for flood forecasting in disaster risk reduction. This paper presents a developed flood river water level forecasting utilizing a hybrid technique called adaptive neuro-fuzzy inference system (ANFIS) model, employed for Kelantan river basin, Kelantan state, Malaysia. The ANFIS model is designed to forecast river water levels at the downstream area in hourly lead times. River water level, rainfall, and river flows were considered as input variables located in upstream stations, and one river water level in the downstream station is chosen as flood forecasting point (FFP) target. Particularly, each of these input-output configurations consists of four stations located in different areas. About twenty-seven data with fifteen minutes basis recorded in January 2013 to March 2015 were used in training and testing the ANFIS network. Data preprocessing is done with feature reduction by principal component analysis and normalization as well. With more attributes in input configurations, the ANFIS model shows better result in term of coefficient correlation ( ) against artificial neural network (ANN)-based models and support vector machine (SVM) model. In general, it is proven that the presented ANFIS model is a capable machine learning approach for accurate forecasting of river water levels to predict floods for disaster risk reduction and early warning.

Modelling and simulation of the dynamic indoor relative humidity behaviour of a building is essential to test any proposed thermal comfort controller and strategy in the building. Like other plants, the dynamic indoor relative humidity behaviour of a building can be developed based on the white box model, black box model, and grey box model. This research focuses on the usage of autoregressive–moving-average (ARMA) model, a type of black box model to represent the dynamic indoor relative humidity behaviour of Industrial Instrumentation Laboratory at Malaysia-Japan International Institute of Technology (MJIIT), UniversitiTeknologi Malaysia (UTM) Kuala Lumpur and uses the real recorded data from the laboratory and minimal knowledge regarding the physical characteristics of the humidity behaviour in the laboratory. The performance of the ARMA model developed in this research is compared with the real recorded data from the laboratory. Result obtained shows that the ARMA model is enough for modelling and simulating the dynamic indoor relative humidity behaviour of the laboratory

Mathematical model representing the dynamic indoor air temperature of a building is important for reducing the time and cost required to test any proposed thermal comfort control algorithm and strategy for that building through computer simulation. There are many types of mathematical model, and each type has its strength(s) and weakness(es). An autoregressive–moving-average (ARMA) model, a type of black box model is used to represent the dynamic indoor air temperature behaviour of industrial instrumentation laboratory at malaysia-japan international institute of technology (MJIIT), universiti teknologi malaysia (UTM) Kuala Lumpur based on the recorded data from the laboratory and minimal physical characteristics knowledge of the laboratory. The ARMA model’s output developed in this research is compared with the actual data recorded from the laboratory for performance measurement. The obtained result shows that the ARMA model is sufficient for modelling and simulating the dynamic indoor air temperature behaviour of the laboratory.