Invading pests and diseases always degrade the quality and quantity of plants. Early and accurate identification of plant diseases is critical for plant health and growth. This work proposes a smartphone-based solution using a Vision Transformer (ViT) model for identifying healthy plants and unhealthy plants with diseases. The collected dataset of tomato leaves was used to collectively train Vision Transformer and Inception V3-based deep learning (DL) models to differentiate healthy and diseased plants. These models detected 10 different tomato disease classes from the dataset containing 10,010 images. The performance of the two DL models was compared. This work also presents a smartphone-based application (Android App) using a ViT-based model, which works on the basis of the self-attention mechanism and yielded a better performance (90.99% testing) than Inception V3 in our experimentation. The proposed ViT-SmartAgri is promising and can be implemented on a colossal scale for smart agriculture, thus inspiring future work in this area.

Alzheimer’s disease is a weakening neurodegenerative condition with profound cognitive implications, making early and accurate detection crucial for effective treatment. In recent years, machine learning, particularly deep learning, has shown significant promise in detecting mild cognitive impairment to Alzheimer’s disease conversion. This review synthesizes research on machine learning approaches for predicting conversion from mild cognitive impairment to Alzheimer’s disease dementia using magnetic resonance imaging, positron emission tomography, and other biomarkers. Various techniques used in literature such as machine learning, deep learning, and transfer learning were examined in this study. Additionally, data modalities and feature extraction methods analyzed by different researchers are discussed. This review provides a comprehensive overview of the current state of research in Alzheimer’s disease detection and highlights future research directions.

Soil is known to be the most valuable natural source for all agriculture fields. Soil has two properties, namely- physical and chemical. These properties include soil moisture, texture, etc. and the latter include pH value. Soil texture plays an important role in crop cultivation. The physical properties of soil such as texture and granular size determine the water and nutrient holding capacity. Also the chemical property like pH value is very important for plant growth and development. Soil having pH value between 5.5 and 7 is optimal for agricultural purpose. Hence, a detailed study of soil pH property is necessary for cultivation. But laboratory method of soil pH calculation is a very costly and tedious process. Therefore, it is essential to develop an expert-based system that will overcome this issue. However, the system must be able to give correct result and should match with those conducted in laboratory. Farmers analyze pH either in lab or by soil pH card based on soil image color. But this is not an effective method since it relies heavily on human perception. Hence, we have developed an expert based system which can determine the pH of the soil without any human error. For this, we have conducted our experiments with the help of MatLab tool and smart phone as we have concerned about the rural farmers. We have analyzed and compared the proposed system results with the traditional laboratory methods with regression and have found 86 % accuracy in our model.