Early Prediction of Tomato Diseases in Iraqi Agriculture using AI-Based Temperature and Humidity Data: A Random Forest Approach - Al-Ahbabi, Marwan Adnan; Al-Tuwaijari, Jamal; Al-Jbory, Awf A. (Basrah Journal for Engineering Sciences, 2025)
Plants and agriculture are important in Iraq and the world because they are among the essential basics of life; their importance lies in several fields, such as industry and food. Plant diseases are the first direct influence on plant production and the Iraqi economy. The primary contribution of this work is developing an efficient early warning system for tomato plant diseases based on readily available environmental data, demonstrating the usefulness of machine learning methods in real agricultural environments. This research investigates the use of artificial intelligence (AI) for the early prediction of tomato diseases in Iraqi agriculture, based on temperature and humidity data collected from Salah al-Din Governorate. Two major diseases were studied: Tomato Yellow Leaf Curl Virus (TYLCV) and late blight. The data were pre-processed and used to train predictive models, including linear regression and Random Forest Regressor (RFR). Results show that RFR outperformed linear regression, achieving a lower Root Mean Square Error (RMSE) of 0.053852 and a Mean Absolute Error (MAE) of 0.45000, indicating its superior accuracy in predicting disease occurrences.