Plant disease detection and classification techniques: a comparative study of the performances

Abstract

Abstract One of the essential components of human civilization is agriculture. It helps the economy in addition to supplying food. Plant leaves or crops are vulnerable to different diseases during agricultural cultivation. The diseases halt the growth of their respective species. Early and precise detection and classification of the diseases may reduce the chance of additional damage to the plants. The detection and classification of these diseases have become serious problems. Farmers’ typical way of predicting and classifying plant leaf diseases can be boring and erroneous. Problems may arise when attempting to predict the types of diseases manually. The inability to detect and classify plant diseases quickly may result in the destruction of crop plants, resulting in a significant decrease in products. Farmers that use computerized image processing methods in their fields can reduce losses and increase productivity. Numerous techniques have been adopted and applied in the detection and classification of plant diseases based on images of infected leaves or crops. Researchers have made significant progress in the detection and classification of diseases in the past by exploring various techniques. However, improvements are required as a result of reviews, new advancements, and discussions. The use of technology can significantly increase crop production all around the world. Previous research has determined the robustness of deep learning (DL) and machine learning (ML) techniques such as k-means clustering (KMC), naive Bayes (NB), feed-forward neural network (FFNN), support vector machine (SVM), k-nearest neighbor (KNN) classifier, fuzzy logic (FL), genetic algorithm (GA), artificial neural network (ANN), convolutional neural network (CNN), and so on. Here, from the DL and ML techniques that have been included in this particular study, CNNs are often the favored choice for image detection and classification due to their inherent capacity to autonomously acquire pertinent image features and grasp spatial hierarchies. Nevertheless, the selection between conventional ML and DL hinges upon the particular problem, the accessibility of data, and the computational capabilities accessible. Accordingly, in numerous advanced image detection and classification tasks, DL, mainly through CNNs, is preferred when ample data and computational resources are available and show good detection and classification effects on their datasets, but not on other datasets. Finally, in this paper, the author aims to keep future researchers up-to-date with the performances, evaluation metrics, and results of previously used techniques to detect and classify different forms of plant leaf or crop diseases using various image-processing techniques in the artificial intelligence (AI) field.

Plant phenotyping relevance

植物病害の画像による検出・分類手法を比較するレビューであり、感染植物の症状・病害状態を画像から推定する方法が中心です。

Code and data availability

This is a survey paper comparing published plant disease detection techniques; it reports no original phenotyping measurements, datasets, images, models, or analysis code of its own. The only availability statement says data are available from the author on request, with no public URL, and no qualifying paper-specific,

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