EXPLORING DEEP LEARNING APPROACHES FOR DETECTING NUTRITIONAL DEFICIENCIES IN CROP LEAVES: A COMPREHENSIVE OVERVIEW

Abstract

This research paper offers an extensive examination of diverse methodologies and computational approaches designed to identify deficiencies in critical plant nutrients, encompassing nitrogen, phosphorus, potassium, zinc, boron, sulfur, and iron. It systematically analyzes the diverse methodologies and strategies proposed by scholars, assessing their effectiveness, constraints, and precision. Plants demand 13 essential mineral nutrients for optimal growth, and any insufficiency or excess of these nutrients can critically disrupt growth or lead to plant mortality. Consequently, the establishment of a continuous monitoring system to oversee nutrient levels is imperative for the enhancement of crop yield and quality. Through diagnostic systems that utilize digital image processing, computer vision, machine learning, and deep learning frameworks (such as pre-trained Convolutional Neural Network models like InceptionV3, VGG16, VGG19, ResNet50, and ResNet152, along with Support Vector Machines), nutrient deficiencies can be detected significantly earlier than through manual methods, thereby allowing farmers to implement timely corrective actions. This article assesses the efficiency of these sophisticated methods in addressing the diagnosis of deficiencies in plant nutrients.

Plant phenotyping relevance

作物葉の栄養欠乏という植物状態を、画像処理・コンピュータビジョン・機械学習で検出する手法を体系的に評価するレビューであり、フェノタイピング手法が中心です。

Code and data availability

This is a review/survey article on deep learning for nutrient deficiency detection. It presents no original phenotyping measurements, datasets, images, code, or models of its own. All datasets and models mentioned (e.g., CoLeaf dataset, Plant Village) belong to cited prior studies, which are excluded as paper-specific.

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