Potato Blight Detection Using Fine-Tuned CNN Architecture

Potato is one of the major cultivated crops and provides occupations and livelihoods for numerous people across the globe. It also contributes to the economic growth of developing and underdeveloped countries. However, potato blight is one of the major destroyers of potato crops worldwide. With the introduction of neural networks to agriculture, many researchers have contributed to the early detection of potato blight using various machine and deep learning algorithms. However, accuracy and computation time remain serious issues. Therefore, considering these challenges, we customised a convolutional neural network (CNN) to improve accuracy with fewer trainable parameters, less computation time, and reduced information loss. We compared the performance of the proposed model with various machine and deep learning algorithms used for potato blight classification. The proposed model outperformed the others with an overall accuracy of 99% using 839,203 trainable parameters in 183 s of training time.

Automated summary

Potato is an important crop that supports numerous people and contributes to economic growth. However, potato blight is a major destroyer of potato crops worldwide. With the introduction of neural networks, researchers have made contributions to early detection of potato blight using different machine and deep learning algorithms. To address the challenges of accuracy and computation time, a customised convolutional neural network (CNN) was developed, which outperformed other algorithms with 99% accuracy using 839,203 trainable parameters in 183 s of training time.