Medicinal plants are essential to healthcare since ancient times and are integral to developing drugs and other medical treatments. More than 25% of medicines in developed countries are produced from medicinal plants, while in developing countries, approximately 80% of individuals receive primary healthcare from these plants. Traditionally, these plants are identified manually by experts, which is tedious, time-consuming, subjective and dependent on the availability of experts. Furthermore, a wrong detection can result in serious health issues or death. This signifies the need for a more reliable approach to identifying medicinal plants, which is accurate and practical. Several automated methods were proposed previously, utilizing deep learning and traditional machine learning (TML) techniques, but they require singular leaf images and failed to achieve sufficient accuracy when demonstrated in a different setting. Capturing singular leaf images for each plant is also time-consuming and laborious. This paper presents a robust, accurate and practical system to identify medicinal plants from smartphone-captured plant images in the site of plants. The proposed system utilized a cascaded architecture to extract features using a pre-trained ResNet50 model, which were optimized using Particle Swarm Optimization (PSO) to classify the plants using a Support Vector Machine (SVM). The proposed ResNet50-PSO-SVM network classified seven medicinal plants with 99.60% accuracy, outperforming the state-of-the-art (99%). The system was demonstrated for three different smartphones, classifying an image in 0.15 seconds with 97.79% accuracy on average. The system’s high accuracy, rapid identification time and robustness ensured its practical use.

In recent years, Artificial Intelligence (AI) has emerged as a disruptive force, transforming industries worldwide through its ability to analyze vast amounts of data and generate informed decisions. The agricultural sector is undergoing a revolutionary transformation driven by the adoption of this technology to enhance productivity, efficiency, and sustainability. However, the lack of transparency in AI models has sparked doubts about their dependability, particularly when applied to critical industries like medicine and agriculture. To address these challenges, research into Explainable Artificial Intelligence (XAI) gained momentum in early 2017. Tools like Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive Explanations (SHAP), and Gradient-weighted Class Activation Mapping (Grad-CAM) helped make progress. These methodologies have proven instrumental in elucidating the internal workings of complex AI models, making their decision-making processes more transparent and comprehensible. This review surveyed current agricultural developments, including using AI to identify plant diseases. We explored articles from Scopus, Web of Science, IEEE Xplore, PubMed, Google Scholar, Agricola, and the Association for Computing Machinery, published between 2017 and 2024. After extracting and filtering, we included publications that satisfied the selection criteria in the review. It has provided an overview of the uses of XAI methods and the difficulties researchers have encountered. In addition, we reviewed recent advancements in machine learning (ML) and deep learning (DL) to find the limitations that agriculturists face in agriculture. We also provide a modern explainable model for identifying plant diseases and show how XAI can be used in agricultural applications. The conclusion of this paper indicates how XAI is necessary for ML, DL models as well as real time monitoring in the agriculture field.

Paddy cultivation is a significant global economic sector, with rice production playing a crucial role in influencing worldwide economies. However, insects in paddy farms predominantly impact the growth rate and ecological equilibrium of the agricultural field. Hence, the precise and timely identification of insects in agricultural settings presents a potential strategy for addressing this issue. This study aims to implement an automated system for paddy farming by employing a realtime framework that incorporates the Internet of Things (IoT), Blockchain technology, and Deep Learning (DL) algorithms. The primary emphasis of the DL-based system is on the timely identification of pests. In contrast, integrating the Internet of Things (IoT) and Blockchain technologies facilitates establishing a fully automated system with security within the agricultural domain. The DL-based system includes a secondary dataset of paddy insects, and then preprocessing, feature extraction, and identification have been performed. Besides, an IoT-based system is embodied with a camera module and microprocessor, accompanied by some apparatus required to automate the whole system. In addition, the research also includes the Blockchain to secure each individual data transmission among the several IoT components and the cloud server. While examining the proposed solution, various experimental data have been systematically documented and analyzed. The proposed framework attained a peak accuracy of 98.91% using the VGG19 model and ensemble classifiers to detect the pest with a specificity of 99.14% and a precision of 98.21%. The study additionally quantifies the mean duration of the cloud response when integrated with IoT, yielding an average time of 1.71 seconds after pest identification. Nevertheless, the system has exhibited a high level of efficacy in the context of real-time monitoring and automation of paddy farms.