Technological Progress Toward Peanut Disease Management: A Review.

Abstract

Peanut ( Arachis hypogea L.) crops in the southeastern U.S. suffer significant yield losses from diseases like leaf spot, southern blight, and stem rot. Traditionally, growers use conventional boom sprayers, which often leads to overuse and wastage of agrochemicals. However, advances in computer technologies have enabled the development of precision or variable-rate sprayers, both ground-based and drone-based, that apply agrochemicals more accurately. Historically, crop disease scouting has been labor-intensive and costly. Recent innovations in computer vision, artificial intelligence (AI), and remote sensing have transformed disease identification and scouting, making the process more efficient and economical. Over the past decade, numerous studies have focused on developing technologies for peanut disease scouting and sprayer technology. The current research trend shows significant advancements in precision spraying technologies, facilitating smart spraying capabilities. These advancements include the use of various platforms, such as ground-based and unmanned aerial vehicle (UAV)-based systems, equipped with sensors like RGB (red-blue-green), multispectral, thermal, hyperspectral, light detection and ranging (LiDAR), and other innovative detection technologies, as highlighted in this review. However, despite the availability of some commercial precision sprayers, their effectiveness is limited in managing certain peanut diseases, such as white mold, because the disease affects the roots, and the chemicals often remain in the canopy, failing to reach the soil where treatment is needed. The review concludes that further advances are necessary to develop more precise sprayers that can meet the needs of large-scale farmers and significantly enhance production outcomes. Overall, this review paper aims to provide a review of smart spraying techniques, estimating the required agrochemicals and applying them precisely in peanut fields.

Plant phenotyping relevance

ピーナッツ病害の識別・スカウティングに用いる画像、AI、リモートセンシング、各種センサー技術をレビューしており、植物の病害状態を推定する方法が主要な内容である。精密散布も扱うが、病害フェノタイピング手法のレビュー要素が明確である。

Code and data availability

This is a review article on peanut disease management technologies. It summarizes prior studies' sensors, algorithms, and accuracies but presents no original phenotype datasets, images, code, models, or supplements with author-deposited assets. All URLs in the text are commercial product pages or general references, no

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