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Water, nutrients and pesticide will be supplied according to the exact requirement of plants.

This would facilitate the understanding of plant growth and any development of pests or weeds. Automatic detection of these type of issues in plants is supposed to achieve by a software solution for automatic detection and classification of plant leaf diseases. Initially image processing is supposed to do the object detection and to perform the leaf counting task it is intended to consider the capability of deep convolution neural network. An array of sensors and cameras connected through Narrowband Internet of Things (NB-IoT) will collect data that will be processed using machine learning techniques. Initially the system decides the age of the plant and learn about the required conditions. When it comes to this project, almost the total management of the plant is done by the system from the beginning to end. In response, the proposed method is capable of identifying the exact status and health of plants to customize the supplies accordingly and ensure the healthy growth of plants. This may result in a shortage or excess amount of supplies that will adversely affect the health of plants. Existing solutions to achieve this objective are only capable of providing supplies from time-to-time without knowing the exact requirement of the plant. Providing water, nutrients, pesticides and other supplies is one of the most resource consuming tasks in a large-scale plantation.

Therefore, we need to embrace cost efficient and effective technologies that would facilitate the automation of large-scale plantations to obtain an increased yield per given area. On the other hand, the demand for high quality agricultural products is on the rise. However, it is observed that land and human resources available in Sri Lanka for agriculture becomes scarce. Agriculture sector is a major economic force in Sri Lanka, making a significant contribution to the national economy.

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