Abstract
Research into agriculture has been gaining steam and displaying signals of significant expansion over the last several years. The most recent to emerge, using a variety of computer technologies in deep learning and remote sensing are simplifying agricultural tasks. The classification of agricultural land cover by humans necessitates a large team of experts and is time-consuming when dealing with vast areas. To implement this project, we have utilized the machine learning and deep learning algorithms to classify the land cover using the RGB version of the EuroSat dataset. This helps to differentiate the agricultural land from the other distinct landscapes aiding the farmers to determine the fine land for cultivation of crops. In this study, we examined the competence between four classifiers namely K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and the pretrained ResNet50 model, which is a convolutional neural network (CNN) technique, are all machine learning classifiers. However, few research have examined the performances of these classifiers with different training sample sizes for the same remote sensing photos, with a particular focus on Sentinel-2 Multispectral Imager images (MSI). Finally, the accuracy rates of the machine learning methods were only fair, with the ResNet50 model producing the best results with a 97.46% accuracy rate.
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Bharghavi, Y.M., Pavan Kumar, C.S., Lakshmi, Y.H., Sri Vyshnavi, K.P. (2023). Agriculture Land Image Classification Using Machine Learning Algorithms and Deep Learning Techniques. In: Bhateja, V., Yang, XS., Ferreira, M.C., Sengar, S.S., Travieso-Gonzalez, C.M. (eds) Evolution in Computational Intelligence. FICTA 2023. Smart Innovation, Systems and Technologies, vol 370. Springer, Singapore. https://doi.org/10.1007/978-981-99-6702-5_19
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