Application of Convolutional Neural Network in Plant Research
Abstract
Convolutional neural networks (CNNs) are a category of deep learning models engineered to acquire spatial and spectral hierarchies from image input autonomously, rendering them highly effective for visual analysis. They have transformed plant and forest sciences by facilitating automated, accurate, and scalable picture analysis. Through hierarchical feature extraction, CNNs exceed conventional image-processing techniques, facilitating precise plant disease diagnosis, phenotyping, crop categorization, and stress detection. Integrated with unmanned aerial vehicles (UAVs), drones, and Internet of Things (IoT)-enabled sensors, CNNs boost precision agriculture via real-time monitoring, yield prediction, and weed identification. At the same time, their combination with hyperspectral and thermal imaging improves the detection of drought, salinity, and nutrient deficiencies. In forestry, CNNs provide precise estimation of chlorophyll content, evaluation of forest health, mapping of deforestation, and detection of forest fires, achieving accuracies beyond 95%. Emerging versions, such as attention-based, ensemble, and explainable AI (XAI) models, enhance interpretability, flexibility, and computing efficiency, tackling issues of data diversity and transparency. This chapter presents a comprehensive examination of CNN architectures, mechanisms, and various applications in agriculture and forestry. It includes disease identification, phenotyping, stress diagnosis, postharvest storage, and environmental monitoring, while delineating existing limitations and prospective advancements for sustainable, AI-integrated management of plants and forests.
