Timely diagnosis and accurate identification of grape leaf diseases are decisive for controlling the spread disease ensuring healthy development industry. The objective this research was to propose a simple efficient approach improve accuracy with limited computing resources scale training image dataset based on deep transfer learning an improved MobileNetV3 model (GLD-DTL). A pre-training obta...

The neural networks have significance on recognition of crops disease diagnosis, but it has disadvantage of slow convergent speed and shortcoming of local optimum. In order to identify the maize leaf diseases by using machine vision more accurately, we propose an improved particle swarm optimization algorithm for neural networks. With the algorithm, the neural network property is improved. It r...

In Agriculture Sciences, detection of diseases is one the most challenging tasks. The mis-interpretations plant often lead to wrong pesticide selection, resulting in damage crops. Hence, automatic recognition at earlier stages important as well economical for better quality and quantity fruits. Computer aided (CAD) has proven a supportive tool disease classification, thus allowing identificatio...

Indonesia is the fourth biggest rice producer in Asia with its production accounting for 35.4 million metric tons yearly. This figure can increase unless crop failure resolved. Identifying diseases, however, may serve as an approach to minimizing risk of failure. The classification detect diseases was previously researched using ResNet101 method 100% accuracy. Despite this perfect accuracy, doe...

Over the last few years, impact of climate change has increased rapidly. It is influencing all steps plant production and forcing farmers to adapt their crop management practices using new technologies based on data analytics. This study aims classify diseases images collected directly in field deep learning. To this end, an ensemble learning paradigm investigated build a robust network order p...