Deep Learning Approaches For Object Recognition In Plant Diseases A Review

Deep learning approaches for object recognition in plant diseases: a review

Plant diseases pose a significant threat to the economic viability of agriculture and the normal functioning of trees in forests. Accurate detection and identification of plant diseases are crucial for smart agricultural and forestry management. Artificial intelligence has been successfully applied to agriculture in recent years. Many intelligent object recognition algorithms, specifically deep learning approaches, have been proposed to identify diseases in plant images. The goal is to reduce labor and improve detection efficiency. This article reviews the application of object detection methods for detecting common plant diseases, such as tomato, citrus, maize, and pine trees. It introduces various object detection models, ranging from basic to modern and sophisticated networks, and compares the innovative aspects and drawbacks of commonly used neural network models. Furthermore, the article discusses current challenges in plant disease detection and object detection methods and suggests promising directions for future work in learning-based plant disease detection systems.

Innovative Approaches to Agricultural Plant Disease Identification: Integrating Deep Learning into Traditional Methods

Identifying and managing plant diseases is a critical aspect of agriculture, significantly impacting the economy and food security. The early detection and management of plant diseases are essential to maintain healthy crops, reduce plant damage, and ensure high yields and quality of agricultural products. The traditional approach involves visually observing symptoms and abnormal growth patterns, which is often limited by the subjectivity of human observation and labor costs. Nowadays, traditional manual inspection of plant diseases is being replaced by advanced techniques, such as sensing networks, machine vision, remote sensing, and robotics. Researchers and engineers have developed numerous plant disease identification techniques and automatic disease inspection systems over the last few decades.

Recent Advances in Big Data, Machine, and Deep Learning for Precision Agriculture