Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations

Abstract

Accurate and rapid plant disease detection is critical for enhancing long-term agricultural yield. Disease infection poses the most significant challenge in crop production, potentially leading to economic losses. Viruses, fungi, bacteria, and other infectious organisms can affect numerous plant parts, including roots, stems, and leaves. Traditional techniques for plant disease detection are time-consuming, require expertise, and are resource-intensive. Therefore, automated leaf disease diagnosis using artificial intelligence (AI) with Internet of Things (IoT) sensors methodologies are considered for the analysis and detection. This research examines four crop diseases: tomato, chilli, potato, and cucumber. It also highlights the most prevalent diseases and infections in these four types of vegetables, along with their symptoms. This review provides detailed predetermined steps to predict plant diseases using AI. Predetermined steps include image acquisition, preprocessing, segmentation, feature selection, and classification. Machine learning (ML) and deep understanding (DL) detection models are discussed. A comprehensive examination of various existing ML and DL-based studies to detect the disease of the following four crops is discussed, including the datasets used to evaluate these studies. We also provided the list of plant disease detection datasets. Finally, different ML and DL application problems are identified and discussed, along with future research prospects, by combining AI with IoT platforms like smart drones for field-based disease detection and monitoring. This work will help other practitioners in surveying different plant disease detection strategies and the limits of present systems.

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“…Jafar A. et al conducted a systematic review of the application of AI and modern technologies in detecting plant diseases, highlighting limitations and suggesting future directions involving IoT drones. However, no specific key solution was proposed to address the challenges identified [29]. Barman U. et al developed a smartphone-based application for detecting tomato leaf diseases using Vision Transformer (ViT) and Inception V3-based deep learning models.…”

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“…Moreover, Dhaka et al [18], in their review, provided valuable insights into the integration of Internet of things (IoT) and deep learning models as powerful tools for addressing the automatic detection, visualization, and classification of plant diseases. In their review, Jafar et al [19] delved into the critical role of accurate and rapid plant disease detection, such as the combination of artificial intelligence (AI) with IoT platforms such as smart drones for field-based disease detection and monitoring, enhancing long-term agricultural yields. Furthermore, Devi et al [20] reviewed the common detection techniques of plant viruses (i.e., ELISA, Western blot, dot blot, immuno-fluorescent assay) and stressed the considerable progress made in microarray and next-generation sequencing detection of plant diseases, like LAMP (loop mediated isothermal amplification), RPA (recombinase polymerase amplification) and HAD (helicase-dependent amplification), con-tributing to enhance productivity, improving crop quality, reducing production costs, and mitigating the environmental impact of chemicals in agriculture.…”

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“…This has caused serious food safety issues and significantly reduced the economic benefits of tomato cultivation. Consequently, rapid and accurate disease detection plays a crucial role in the prevention and control of tomato diseases [ 8 , 9 ]. Currently, the identification and control of tomato diseases primarily rely on empirical methods (Fig.…”