Chemical and physicochemical properties of semi-dried organic strawberries. Tylewicz U., Mannozzi C., Romani S., Castagnini J.M., Samborska K., Rocculi P., Rosa M.D. Bioactive compounds and antioxidant activity in different types of berries.
Skrovankova S., Sumczynski D., Mlcek J., Jurikova T., Sochor J. Deep learning for tomato diseases: Classification and symptoms visualization. Elongated, sunken, purplish brown or reddish brown spots or streaks may be seen on stems. Edges of leaves eventually turn brown and dry out appearing burnt or 'scorched'. Leaf tissue around spots turn reddish or purple. Major tomato viruses in the Mediterranean basin. Small solid purple spots on leaves, No light colored center. Automatic plant pest detection and recognition using k-means clustering algorithm and correspondence filters. Experimental results stated that trained CNN models could be used in conjunction with variable rate agrochemical spraying systems, which will help farmers to reduce agrochemical use, crop input costs and environmental contamination.ĮfficientNet Leaf scorch disease convolutional neural network deep learning disease classification precision farming.įaithpraise F., Birch P., Young R., Obu J., Faithpraise B., Chatwin C. AlexNet achieved slightly lower validation accuracy (0.72, 0.79) in comparison with VGGNet and EfficientNet-B3. The field experiment results with controlled lightening arrangements, showed that the model EfficientNet-B3 achieved the highest classification accuracy, with 0.80 and 0.86 for initial and severe disease stages, respectively, in real-time. All the trained CNN models were integrated with a machine vision system for real-time image acquisition under two different lighting situations (natural and controlled) and identification of leaf scorch disease in strawberry plants. It was also observed that the severe disease (leaf scorch) stage was correctly classified more often than the initial stage of the disease. The performance accuracy of EfficientNet-B3 and VGG-16 was higher for the initial and severe stage of leaf scorch disease identification as compared to AlexNet and SqueezeNet. Four convolutional neural networks (SqueezeNet, EfficientNet-B3, VGG-16 and AlexNet) CNN models were trained and tested for the classification of healthy and leaf scorch disease infected plants.
Lesions begin as small, roundish, reddish-brown or purple spots on the upper leaf surface. In this research, deep learning models were used to identify the leaf scorch disease in strawberry plants. Leaf spot is one of the most common diseases of strawberry. Deep learning (DL) has recently gained popularity in image classification and identification due to its high accuracy and fast learning. For this reason, it is highly desirable to automatically identify the diseases in strawberry plants to prevent loss of crop quality.
Strawberry leaf scorch manual#
Plant diseases are difficult to diagnose correctly, and the manual disease diagnosis process is time consuming. Plant diseases are a major factor for crop losses in agriculture. Plant health is the basis of agricultural development.
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