Enhanced Disease Detection for Potato Crop using CNN with Transfer Learning

Abstract

Abstract Potatoes are ubiquitous and the fourth most consumed staple food in the world. Moreover, the demand is growing daily as a result of the global market. Diseases such as late blight and early blight greatly affect the quality and quantity of potatoes. Interpreting these diseases manually is cumbersome, making it more difficult to identify which potato leaves are infected with one of the diseases. Fortunately, the diseases of the potato leaves can be determined based on the leaf conditions. Using heavy architectures for convolutional neural networks, like GoogleNet, Resnet15, VGG16, and Xception, this suggested study introduces a method that uses deep learning to categorize the two varieties of illnesses and produces a precise classifier. In the first 40 CNN epochs, we were able to attain 97% accuracy, proving the viability of the deep neural network strategy.Also, a deeper analysis of various model building techniques like transfer learning, ensemble, and non-transfer techniques are done and by comparing their accuracy scores the better technique suited for this problem statement is justified.

Plant phenotyping relevance

ジャガイモ葉の画像状態から病害を分類するCNN手法が研究の中心であり、植物の病害状態を直接推定する画像ベース表現型解析に該当する。

Code and data availability

The paper's plant-phenotyping input is the public PlantVillage potato leaf image dataset from Kaggle, explicitly stated as the source of all training/validation images and confirmed in the data availability statement. No author code or models are deposited.