Application of Artificial Neural Networks and Image-Based Analysis for Black Eggplant (Solanum Melongena) Crop Monitoring: Case Report

Abstract

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.

Plant phenotyping relevance

画像解析とCNNを用いたナスの病害分類・作物分類が研究の中心であり、植物の病害状態を画像から推定するフェノタイピング手法の適用に該当する。

Code and data availability

This is a literature-review-style case report with no authors' own phenotype dataset, images, code, or models. The only datasets mentioned (e.g., the Mendeley 'Eggplant Leaf Disease Detection Database' and Rangarajan's dataset) belong to cited prior work, not this paper, and no availability statements or author URLs/de

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