On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey

Abstract

The agricultural sector remains a key contributor to the Moroccan economy, representing about 15% of gross domestic product (GDP). Disease attacks are constant threats to agriculture and cause heavy losses in the country’s economy. Therefore, early detection can mitigate the severity of diseases and protect crops. However, manual disease identification is both time-consuming and error prone, and requires a thorough knowledge of plant pathogens. Instead, automated methods save both time and effort. This paper presents a contemporary overview of research undertaken over the past decade in the field of disease identification of different crops using machine learning, deep learning, image processing techniques, the Internet of Things, and hyperspectral image analysis. Additionally, a comparative study of several techniques applied to crop disease detection was carried out. Furthermore, this paper discusses the different challenges to be overcome and possible solutions. Then, several suggestions to address these challenges are provided. Finally, this research provides a future perspective that promises to be a highly useful and valuable resource for researchers working in the field of crop disease detection.

Plant phenotyping relevance

植物病害の画像・ハイパースペクトル・機械学習による識別手法を中心に扱うレビューであり、感染植物の病徴・病害状態を観測するフェノタイピング手法レビューに該当する。

Code and data availability

This is a survey paper with no original phenotyping measurements or analysis code. The Data Availability Statement reads 'Not applicable', and the supplementary materials contain only explanatory figures (a ML vs. DL comparison figure and a PRISMA flow diagram), not datasets, images, code, or models. All datasets and图像

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