Abstract
Agriculture is one of the sectors that data mining learned before becoming popular. Today, it helps in making smart decisions on a variety of agricultural challenges such as eliminating difficult manual tasks and predicting crop yields based on climate change data.
We present a new AGRI-PREDI solution that uses new intelligent agro-climatic functionalities. Data is collected from several national and international databases, and we apply smart new rules to create new trusted features. After that, we build a mathematical model that will be trained and adapted to different machine and deep learning models like CART (Decision Trees), SVM (Support Vector Machines), and KNN (K-Nearest Neighbors), as well as models of deep learning. Such as MLP (Multi-Layer Perceptron) and CNN (Convolutional Neural Networks).
The study of this article has been applied to Mediterranean olive growing. The results of our solution demonstrate that the proposed new rules are effective for crop yield prediction. Deep learning has the highest level of accuracy, with values of 97.945% for the CNN model and 93.216% for the MLP model, respectively. Due to its high efficiency and accuracy when the data increases. However, CART shows good efficiency due to its logical tree structure.
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Tsouli Fathi, M., Tsouli Fathi, R., Khrouch, S., Cherrat, L., Ezziyyani, M. (2023). AGRI-PREDI Prediction System of Climate Change Based on Machine Learning for Precision Agriculture in Mediterranean Region. In: Kacprzyk, J., Ezziyyani, M., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development. AI2SD 2022. Lecture Notes in Networks and Systems, vol 713. Springer, Cham. https://doi.org/10.1007/978-3-031-35248-5_11
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