The recent advancements in agricultural and medical research have demonstrated significant progress in leveraging deep learning and multimodal approaches to address critical challenges. In agriculture, the focus has shifted towards developing efficient and accurate disease detection systems for crops, with notable improvements in both detection and classification tasks. These systems often integrate lightweight models suitable for mobile deployment, enhancing accessibility for farmers in remote areas. Additionally, explainability in deep learning models has become a focal point, ensuring that these tools are not only accurate but also transparent and interpretable. In the medical field, multimodal ensemble models have shown remarkable potential in predicting treatment outcomes and diagnosing diseases, particularly in kidney-related conditions. These models combine various data types, including clinical, omics, and histopathology data, to provide more accurate prognostic and diagnostic insights. The integration of explainable AI in medical diagnostics further ensures that these models are reliable and actionable. Overall, the research trends indicate a move towards more integrated, efficient, and interpretable AI solutions in both agriculture and medicine.
Integrated AI Solutions in Agriculture and Medicine
Sources
Paddy Disease Detection and Classification Using Computer Vision Techniques: A Mobile Application to Detect Paddy Disease
A multimodal ensemble approach for clear cell renal cell carcinoma treatment outcome prediction
An Enhancement of CNN Algorithm for Rice Leaf Disease Image Classification in Mobile Applications
Explainability of Deep Learning-Based Plant Disease Classifiers Through Automated Concept Identification
Accurate Water Level Monitoring in AWD Rice Cultivation Using Convolutional Neural Networks
A Novel Ensemble-Based Deep Learning Model with Explainable AI for Accurate Kidney Disease Diagnosis