Potato leaf disease detection in an early stage is challenging because of variations in crop species, crop diseases symptoms and environmental factors. These factors make it difficult to detect potato leaf diseases in the early stage. Various machine learning techniques have been developed to detect potato leaf diseases. However, the existing methods cannot detect crop species and crop diseases in general because these models are trained and tested on images of plant leaves of a specific region. In this research, a multi-level deep learning model for potato leaf disease recognition has developed. At the first level, it extracts the potato leaves from the potato plant image using the YOLOv5 image segmentation technique. At the second level, a novel deep learning technique has been developed using a convolutional neural network to detect the early blight and late blight potato diseases from potato leaf images. The proposed potato leaf disease detection model was trained and tested on a potato leaf disease dataset. The potato leaf disease dataset contains 4062 images collected from the Central Punjab region of Pakistan. The proposed deep learning technique achieved 99.75% accuracy on the potato leaf disease dataset. The performance of the proposed techniques was also evaluated on the PlantVillage dataset. The proposed technique is also compared with the state-of-the-art models and achieved significantly concerning the accuracy and computational cost.

Due to exponential population growth, climate change, and an increasing demand for food, there is an unprecedented need for a timely, precise, and dependable assessment of crop yield on a large scale. Wheat, a staple crop worldwide, requires accurate and prompt prediction of its output for global food security. Traditionally, the development of empirical models for crop yield forecasting has relied on climate data, satellite data, or a combination of both. Despite the enhanced performance achieved by integrating satellite and climate data, the contributions from various sources (Climate, Soil, Socioeconomic, and Remote sensing) remain unclear. The lack of well-defined comparisons between the performance of regression-based approaches and different Machine Learning (ML) methods in yield prediction necessitates further investigation. This study addresses the gaps by combining data from multiple sources to forecast wheat yield in the Multan region in the Punjab province of Pakistan. The findings are compared to the benchmark provided by Crop Report Services (CRS) Punjab, with three widely used ML techniques (support vector machine (SVM), Random Forest (RF), and Least Absolute Shrinkage and Selection Operator (LASSO)) by integrating publicly available data within the GEE (Google Earth Engine) platform, including climate, satellite, soil properties, and spatial information data to develop alternative empirical models for yield prediction using data from 2017 to 2022, selecting the best attribute subset related to crop output. The data set of district-level simulated yields was analyzed with three Machin Learning models (SVM, RF, and LASSO) as a function of seasonal weather, satellite, and soil. The results indicate that combining all datasets using three ML algorithms achieves better yield prediction performance ( R ² : 0.74-0.88). Incorporating spatial information and other properties into benchmark models can improve the prediction from 0.08 to 0.12. Random forest outperformed the competitor models with a Root Mean Square Error (RMSE) of 0.05 q/ha and R ² of 0.88. Comparative analysis shows that random forest with 97% and SVM with 93% yielded better results in the study area.

In wireless sensor networks, the sensors transfer data through radio signals to a remote base station. Sensor nodes are used to sense environmental conditions such as temperature, strain, humidity, sound, vibration, and position. Data security is a major issue in wireless sensor networks since data travel over the naturally exposed wireless channel where malicious attackers may get access to critical information. The sensors in wireless sensor networks are resource-constrained devices whereas the existing data security approaches have complex security mechanisms with high computational and response times affecting the network lifetime. Furthermore, existing systems, such as secure efficient encryption algorithm, use the Diffie–Hellman approach for key generation and exchange; however, Diffie–Hellman is highly vulnerable to the man-in-the-middle attack. This article introduces a data security approach with less computational and response times based on a modified version of Diffie–Hellman. The Diffie–Hellman has been modified to secure it against attacks by generating a hash of each value that is transmitted over the network. The proposed approach has been analyzed for security against various attacks. Furthermore, it has also been analyzed in terms of encryption/decryption time, computation time, and key generation time for different sizes of data. The comparative analysis with the existing approaches shows that the proposed approach performs better in most of the cases.