this research investigates the use of artificial neural networks (ANNs) and image processing techniques for monitoring black eggplant crops, including for classification, disease detection, and potential yield estimation. A dataset of eggplant images was analysed, image pre-processing was performed, features were extracted via convolutional neural networks (CNNs), and classification/regression models were built. The results show that CNN-based methods achieve high accuracy in disease classification and crop classification tasks. The implications for precision agriculture and reduced environmental impact are discussed. The aim of this study is to use artificial intelligence, specifically networks, to examine diseases affecting eggplant, given its importance as a crop. Practitioners should begin with transfer learning using pre-trained CNNs for disease detection, progressively integrating multispectral sensors and recurrent networks for temporal modeling. The development of a dedicated black eggplant monitoring case report would significantly advance precision horticulture for this economically vital crop . The study could potentially be extended to other crops.
Mina Aljuboury (Thu,) studied this question.