![]() ![]() They are leaf spot diseases caused by Phytophthora infestans and the fungus Alternaria solani respectively that cause average yield losses of between 30% and 75%. Potato late blight and early blight influence the quality and quantity of the potatoes, hence causing direct crop loss. However, diseases such as early blight, late blight (LB), bacterial wilt (BW), and viruses reduce the production of smallholder potato farmers in sub-Saharan Africa. Potato has a short cropping cycle and produces a large amount per unit area in a short period (International Potato Centre, Sub-Saharan Africa 2020). The improvement of the potato production system in sub-Saharan Africa can be a pathway out of poverty. Potato is the third most important food crop in terms of global consumption. Low productivity, which characterizes agricultural production, remains a major concern in many African countries. About 80% of the agricultural output comes from smallholder farmers and employs nearly 65% of the population. The agricultural sector remains important to the socio-economic development of Africa, contributing 32% of the GDP. Vision systems that aid farmers in improving their disease management systemsĬontemporary society is concerned about food security issues due to the continual increase in population, rural to urban migration, climate change, and the reduction of cultivable land caused by the increase in industrialization and urbanization processes. The DenseNet121 model was shown to be useful in developing computer The DenseNet121 architecture with a batch ofģ2 and a Stochastic Gradient Descent (SGD) optimizer with a learning rate ofĠ.01 produced the best performance, with an accuracy of 98.34% and a 97.37%į1-score. An open-source datasetĬontaining 4082 images was used. The batch size, the optimizer, and the learning rate. ![]() Likewise, the hyperparameters analyzed were the number of epochs, ![]() The CNN architecturesĮvaluated were AlexNet, GoogleNet, SqueezeNet, DenseNet121, EfficientNet b7,Īnd VGG19. Transfer learning for training and four hyperparameters. To compare six cutting-edge CNN architectural models, taking into account Method hasn’t been widely used in the detection of potato late blight and earlyīlight diseases, which reduce yields significantly. Have been shown to be helpful in detecting disease in plants because of theirĬapacity to analyze vast volumes of data quickly and reliably. Have been shown to be effective in a variety of agricultural applications. Traditional leaf disease diagnosis procedures, which are generally As a result, machine-driven disease detection systems may be able to overcome the constraints of The potato blight disease are critical for promoting healthy potato plant growthĪnd ensuring adequate supply and food security for the fast - growing population. Production of potatoes, impacting many farmers around the world, particularly Potato late blight and early blight are common hazards to the long-term ![]()
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