An accurate image retrieval technique is required due to the rapidly increasing number of images. It is important to implement image annotation techniques that are fast, simple, and, most importantly, automatically annotate. Image annotation has recently received much attention due to the massive rise in image data volume. Focusing on the agriculture field, this study implements automatic image annotation, namely, a repetitive annotation task technique, to classify the ripeness of oil palm fruit and recognize a variety of fruits. This approach assists farmers to enhance the classification of fruit methods and increase their production. This study proposes simple and effective models using a deep learning approach with You Only Look Once (YOLO) versions. The models were developed through transfer learning where the dataset was trained with 100 images of oil fruit palm and 400 images of a variety of fruit in RGB images. Model performance and accuracy of automatically annotating the images with 3500 fruits were examined. The results show that the annotation technique successfully annotated a large number of images accurately. The mAP result achieved for oil palm fruit was 98.7% and the variety of fruit was 99.5%.

The partially shaded photovoltaic (PSPV) condition reduces the generated power and contributes to hot spot problems that may lead to breakdown of shaded modules. PSPV generates multiple peak, one global one and many other local peaks. Many efficient, accurate and reliable maximum power point tracker (MPPT) techniques are used to track the global peak instead of local peaks. The proposed technique is not limited to global peak tracking, but rather it is capable of tracking the sum of all peaks of the PV arrays using an interleaved boost converter (IBC). The proposed converter has been compared with the state of the art conventional control method that uses a conventional boost converter (CBC). The converters used in the two PSPV systems are interfaced with electric utility using a three-phase inverter. The simulation findings prove superiority of the PSPV with IBC compared to the one using CBC in terms of power quality, reliability, mismatch power loss, DC-link voltage stability, efficiency and flexibility. Also, IBC alleviates partial shading effects and extracts higher power compared to the one using CBC. The results have shown a remarkable increase in output generated power of a PSPV system for the three presented scenarios of partial shading by 61.6%, 30.3% and 13%, respectively, when CBC is replaced by IBC.

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.