Numerous machine learning methods like SVM (support vector machine), random forest, KNN (k-nearest neighbor), Nave Bayes, decision tree, etc., have been exploited for recognition, discovery, and categorization of plant diseases; however, the advancement of machine learning by DL (deep learning) ...
EfficientNets was implemented for the classification of plant diseases. Specifically, the EfficientNets models B0 to B5 were implemented on the PlantVillage dataset. Furthermore, Transfer learning techniques were included in the proposed model to minimize the training time. The two phases of Transfer ...
factors responsible for plant diseases in Sect. "Factors responsible for plant diseases", detection and classification of plant diseases in Sect. "Detection and classification
Through the years, plant diseases have been a consistent risk to food security. Hence, their rapid identification could significantly mitigate the economic losses around the world, also reducing the harmful effect of manures and pesticides on the climate. When the disease is recognized, matching the...
Plant diseases have a significant impact on global food security and the world's agricultural economy. Their early detection and classification increase the chances of setting up effective control measures, which is why the search for automatic systems that allow this is of major interest to our so...
A Matlab code is written to classify the type of disease affected leaf. Here I have considered two different types of diseases, i.e 'Anthranose' & 'Blackspot'. Segmentation of the disease affected area was performed by K means clustering. Over 13 different statistical and texture based ...
Plant disease classification using deep learning Agriculture plays a crucial role in the Indian economy. Early detection of plant diseases is very much essential to prevent crop loss and further spread of... KP Akshai,J Anitha - International Conference on Signal Processing & Communication 被引量:...
Rice plant diseasesIn traditional practices, detection of rice plant diseases by experts is a subjective matter whereas by testing in the laboratory is time-consuming. As a consequence, it causes reduction on agricultural production and economic loss to farmers. To overcome this, there is a demand...
The approach is more reliable as the detection and classification of plant diseases are more precise. 展开 关键词: K-Means Clustering Gray Level Co-Occurrence Matrix (GLCM) Region of Interest (ROI) Bacteria Foraging Optimization Algorithm (BFOA) Convolutional Neural Network (CNN) Deep Learning ...
Agriculture stands as India’s most crucial industry, despite grappling with a 35% annual loss in crop yield attributed to plant diseases. Traditionally, the detection of plant diseases has been a laborious process, hampered by insufficient laboratory infrastructure and expert knowledge. Plant...