In modern days, artificial intelligence plays an important role in every scenario of life. Our economy mainly relies on agriculture, so this backwardness of technology affects the economy. When we are concerned about agriculture, the main issue that the agriculture sector facing now is, disease identification. Identification of diseases in the correct time can avoid loss of crops and finance of cultivator. Most farmers depend on a traditional method of detection, this method requires enormous amounts of work and time, but correctness of prediction is low. This Paper mainly focuses on disease identification in bell peppers in large farms based on deep learning networks such as Vgg 16, Vgg 19, and AlexNet. Generally, farmers won’t able to find out whether their plant is affected by diseases or not. The spread of diseases affects crop production. Only method to avoid the loss of crop production is by identifying the diseases at its early stage. We do testing based on the image from the different parts of the farm. We also intend to study pre-trained CNN architecture of VGG and AlexNet known as transfer learning, to detect disease detection in bell pepper. Based on our study we found out that Vgg 19 has better performance for disease detection in bell pepper.
₹10000 (INR)
IEEE-2023