Early Detection of Diseases in Precision Agriculture Processes Supported by Technology

Abstract

One of the biggest challenges for farmers is the prevention of disease appearance on crops. Farmers must deal with many different diseases, varying according to each crop produced. Governments around the world have specialized offices in charge of controlling border product entry to reduce the number of diseases af-fecting local producers. Even though governments and producers work together to fight against disease appearance and propagation, it is important to reduce the spread of diseases as quickly as possible in crop fields. For this reason, it is cru-cial to detect diseases in the early stages of propagation, to enable farmers to at-tack them on time, or remove the affected plants. In this research, we propose to use convolution neural networks to detect diseases in horticultural crops. We compare the results of disease classification in images of plant leaves, in terms of performance, time execution and classifier size. In the analysis we implement two distinct classifiers, a densenet-161 pre-trained model and a custom created model. We concluded that for disease detection in tomato crops, our custom model has better execution time and size, and the classification performance is acceptable. Therefore, the custom model could be useful to use to create a solution that helps small farmers in rural areas in resource-limited mobile devices.