Loading...
Dr. Manoj Kumar Sharma is the Principal of Shri Matsaya College of Education, Rajasthan, India. His research areas include Artificial Intelligence, Data Science, Educational Technology, and Smart Agricultural Applications.
Agriculture is crucial for global food security, and the prompt identification of plant diseases is imperative for enhancing crop productivity and quality. Conventional disease identification techniques frequently depend on manual examination by agricultural specialists, which can be labour-intensive, expensive, and susceptible to human mistake. Deep Learning (DL) has arisen as a potent method for the automated categorisation of fruit and leaf diseases through the application of sophisticated image processing and pattern recognition techniques. Deep learning methods, including Convolutional Neural Networks (CNNs), Transfer Learning architectures, Vision Transformers (ViTs), and hybrid models, may precisely detect diseases in plant photos, facilitating prompt intervention and precision agriculture methodologies. This chapter examines the ideas, methodology, datasets, architectures, applications, problems, and future trajectories of deep learning-based systems for classifying fruit and leaf diseases, emphasising their contribution to sustainable and intelligent agriculture.
Precise weather forecasting is essential for contemporary agriculture, as climatic conditions directly affect crop development, irrigation strategies, insect control, and overall farm efficiency. Conventional weather forecasting systems frequently encounter difficulties in delivering localised, real-time, and actionable information to farmers. Cloud-integrated weather prediction systems utilise cloud computing, artificial intelligence, machine learning, Internet of Things (IoT) devices, and big data analytics to gather, process, and analyse extensive meteorological and agricultural data. These advanced systems provide real-time meteorological observation, predictive analysis, and decision-making support for precision agriculture. This chapter examines the architecture, technology, methodology, applications, problems, and future trends of cloud-integrated weather prediction systems in agriculture. It emphasises the capacity of cloud-based forecasting tools to enhance crop management, mitigate climate-related risks, and promote sustainable agriculture practices.