Guava Trees Disease Monitoring Using the Integration of Machine Learning and Predictive Analytics
The increase in population, food demand, and the pollution levels of the environment are considered major problems of this era. For these reasons, the traditional ways of farming are no longer suitable for early and accurate detection of biotic stress. Recently, precision agriculture has been extensively used as a potential solution for the aforementioned problems using high resolution optical sensors and data analysis methods that are able to cope with the resolution, size and complexity of the signals from these sensors. In this paper, several methods of machine learning have been utilized in order to study pests, their types, population, and agricultural conditions in terms of soil and climate for some crops such as potatoes, guava, and cotton, which are among the main Egyptian crops. In the process of obtaining a suitable estimate of insects population affecting each of the aforementioned crops, a hardware model control, based on the results provided by the predictive analysis, an estimate of the electromagnetic force is applied to the cultivated areas to get rid of the pests as well as giving a background to farmers about the possibility of infecting a crop such as Potato with Late Blight, according to climatic conditions. © 2021 IEEE.