Comparative Analysis of Various Models for Potato Leaf Disease Classification using Deep Learning

• Published in: 2023 Second International Conference on Electronics and Renewable Systems (ICEARS) pp. 1186 - 1193
• Main Authors: Krishnakumar, B., Kousalya, K., Indhu Prakash, K. V., Jhansi Ida, S., Ravichandra, B., G, Rajeshkumar
• Format: Conference Proceeding
• Language: English
• Published: IEEE 02-03-2023
• Subjects: Analytical models; Computational modeling; Crops; Deep Learning; Image color analysis; InceptionV3 Model; MobileNet V2 Model; Predictive models; Renewable energy sources; Resnet50 V2 Model; Tensor Flow; Training; VGG16 Model; VGG19 Model
• DOI: 10.1109/ICEARS56392.2023.10085425

Potato is one among the most extensively consumed staple foods, ranking fourth on the global food pyramid. Moreover, because of the global coronavirus outbreak, global potato consumption is expanding dramatically. Potato diseases, on the other hand, are the primary cause of crop quality and quantity decline. Plant conditions will be dramatically worsened by incorrect disease classification and late identification. Fortunately, leaf conditions can help identify various illnesses in potato plants. Potato (Solanum tuberosum L) is one of the majorly farmed vegetable food crops in worldwide. The output of potato crops in both quality and quantity is affected majorly due to fungal blight infections, which causes a severe impact on the global food yield. The most severe foliar diseases for potato crops are early blight and late blight. The causes of these diseases are Alternaria solani and Phytophthora infestants respectively. Farmers suspect such problems by focusing on the color change or transformation in potato leaves, which is effortless due to subjectivity and lengthy time commitment. In such circumstances, it is critical to develop computer models that can diagnose those diseases quickly and accurately, even in their early stages.