Machine Learning Approach for Early Detection of Plant and Fish Diseases Security

Abstract

The information technologies currently used in plant and fish farming are largely based on equipment and mechanism, image processing, and pattern acknowledgement, computerized modelling, geographical information systems, expert systems (Pakar), data supervision, artificial intelligence (AI), decision maker devices, and care centres or links. The use of advanced technologies eases the prediction and prevention of parasite infestation and other disease outbreaks. The food productivity of the food sources, including plants and fish, is limited by diseases. The early detection of the disease’s infection by naked eyes is somehow difficult. Therefore, early detection through different image processing tools has been introduced widely. Due to the increasing number of reported paper on the potential use of data quarrying and types of machine learning (ML) for plant and fish disease prediction, this chapter consolidates and presents scientific information on the application of data mining and ML in both types of diseases and discussed how imaging technology can be applied to study the diseases and the method in the detection, with comprehensions on the different encounters and prospects. In addition, the potential application of ML in terms of plant and fish disease discoveries in Indonesia are put forward.