Innovative Machine Vision for Detecting Plant Disease Via Leaf Morphology and Recommending Fertilizers

Abstract

Plant diseases are highly influential on agricultural productivity and sustainability, hence the importance of effective and reliable predictive models. The paper covers prediction models. By using Apple leaf photos, the model will assist in predicting plant illness. The research focuses mostly on the quality of output. In addition to the improvement in output quality, this study addresses some of the challenges faced in previous models by incorporating the latest input features. This research proposes a model that will predict the disease using the VGG16 algorithm For accuracy, the model uses VIT to predict the disease stage. Following that, Random Forest Algorithm was used to select the most appropriate fertilizer for the plant based on the plant's disease stage. The framework proposed here attempts to help the farmer and the agricultural expert by giving an overall solution for early detection of disease stages, promoting sustainable agriculture. The research achieved an accuracy of 79% while training the VIT model and an accuracy of 98% with the VGG16 model.

Plant phenotyping relevance

葉画像から植物病害と病期を推定する画像解析モデルが研究の中心であり、植物の病害状態を直接評価するフェノタイピング手法に該当する。肥料推薦は付随的要素。

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