Artificial Intelligence Enabled Apple Leaf Disease Classification for Precision Agriculture

Abstract

Precision agriculture enables the recent technological advancements in farming sector to observe, measure, and analyze the requirements of individual fields and crops. The recent developments of computer vision and artificial intelligence (AI) techniques find a way for effective detection of plants, diseases, weeds, pests, etc. On the other hand, the detection of plant diseases, particularly apple leaf diseases using AI techniques can improve productivity and reduce crop loss. Besides, earlier and precise apple leaf disease detection can minimize the spread of the disease. Earlier works make use of traditional image processing techniques which cannot assure high detection rate on apple leaf diseases. With this motivation, this paper introduces a novel AI enabled apple leaf disease classification (AIE-ALDC) technique for precision agriculture. The proposed AIE-ALDC technique involves orientation based data augmentation and Gaussian filtering based noise removal processes. In addition, the AIE-ALDC technique includes a Capsule Network (CapsNet) based feature extractor to generate a helpful set of feature vectors. Moreover, water wave optimization (WWO) technique is employed as a hyperparameter optimizer of the CapsNet model. Finally, bidirectional long short term memory (BiLSTM) model is used as a classifier to determine the appropriate class labels of the apple leaf images. The design of AIE-ALDC technique incorporating the WWO based CapsNet model with BiLSTM classifier shows the novelty of the work. A wide range of experiments was performed to showcase the supremacy of the AIE-ALDC technique. The experimental results demonstrate the promising performance of the AIE-ALDC technique over the recent state of art methods.

An Industrial-Grade Solution for Crop Disease Image Detection Tasks

“…Even the accuracy of the YOLO V5 model used by Mathew and Mahesh ( 2022 ) was 4.8% lower than our study. By comparing the model accuracy of the different number of disease categories, the model accuracy of Zhang et al ( 2020 ), Gao et al ( 2021 ), Sharma et al ( 2021 ), Zhao et al ( 2021 ), and Al-Wesabi et al ( 2022 ) is higher than our results, which is due to the smaller number of disease categories (up to 10 categories). Our study required the identification of up to 59 plant disease categories, which exceeded at least 85% of the disease categories in other studies and reduced the accuracy by up to 4.5% relative to other studies.…”

Artificial intelligence-based decision support systems in smart agriculture: Bibliometric analysis for operational insights and future directions

“…The higher central aspect of C1 indicates that the articles published within the scope of agriculture, article, and classification are concerned more with crop classification. Some of the recent publications within C1 focus on digital soil mapping (Lagacherie et al, 2022), an intelligence-based approach for agricultural soil prediction (Nguyen et al, 2022), AI-based apple leaf disease classification (Al-Wesabi et al, 2022), and simulating various water-deficit regimes for irrigation scheduling optimization (Martínez-Valderrama et al, 2020). Recent publications indicate that C1 is more inclined toward procedures and classification of multiple aspects of agriculture segments.…”

An Improved Deep Residual Convolutional Neural Network for Plant Leaf Disease Detection

“…The model produced a better performance than support vector machine (SVM), k-nearest neighbour (K-NN), random forest (RF), and logistic regression (LR) techniques. On the other hand, the authors in [ 13 ] used a capsule network with a bidirectional long short-term memory model for the classification of apple leaf diseases. The classification performance of their model was better than that of the standard machine learning techniques.…”