AI-Driven Precision Farming Tools in Tomato Crop Evaluation within Jordanic Context

Precision farming has become increasingly important in modern agriculture to optimise resource use and improve crop yields efficiently. A comprehensive search strategy was employed across multiple databases including Agris, Web of Science, and Scopus. Studies were screened based on predefined inclusion criteria related to AI applications in agriculture and specific focus on tomatoes in Jordan. AI tools showed a significant improvement (p < 0.05) in detecting crop diseases with an average detection rate of 85% compared to traditional methods, highlighting their potential for enhancing tomato yield management. The review underscores the promising role of AI in precision farming for tomatoes but also identifies gaps in data standardization and tool adoption rates across different regions. Further research should explore long-term impacts and scalability issues while encouraging policy makers to support technological integration in agricultural policies. Precision Farming, Artificial Intelligence, Tomato Crops, Jordan, Nigeria Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.