Deep Learning Techniques for Identification of Different Malvaceae Plant Leaf Diseases

Abstract

INTRODUCTION: The precise and timely detection of plant diseases plays a crucial role in ensuring efficient crop management and disease control. Nevertheless, conventional methods of disease identification, which heavily rely on manual visual inspection, are often time-consuming and susceptible to human error. The knowledge acquired from this research paper enhances the overall comprehension of the discipline and offers valuable direction for future progressions in the application of deep learning for the identification of plant diseases.[1][2] AIM: to investigate the utilization of deep learning techniques in identifying various Malvaceae plant diseases. METHODS: AlexNet, VGG, Inception, REsNet and other CNN architectures are analyzed on Malvaceae plant diseases specially on Cotton, Ocra and Hibiscus, different data collection methods ,Data augmentation and Normalization techniques. RESULTS: Inception V4 have Training Accuracy 98.58%, VGG-16 have Training Accuracy 84.27%, ResNet-50 have Training Accuracy 98.72%, DenseNet have Training Accuracy 98.87%, Inception V4 have Training Loss 0.01%, VGG-16 have Training Loss 0.52%, ResNet-50 have Training Loss 6.12%, DenseNet have Training Loss 0.016%, Inception V4 have Test Accuracy 97.59%, VGG-16 have Test accuracy 82.75%, ResNet-50 have Test Accuracy 98.73%, DenseNet have Test Accuracy 99.81%, Inception V4 have Test Loss 0.0586%, VGG-16 have Test Loss 0.64%, ResNet-50 have Test Loss 0.027%, DenseNet have Test Loss 0.0154% . CONCLUSION: conclusion summarizes the key findings and highlights the potential of deep learning as a valuable tool for accurate and efficient identification of Malvaceae plant diseases.

Plant phenotyping relevance

マルバ科植物の病徴を画像から深層学習で識別し、複数CNNの精度を比較評価しており、植物病害状態の表現型取得・推定手法が中心である。

Code and data availability

This is a review paper on deep learning for Malvaceae leaf disease identification. The supplied blocks contain no public phenotype/trait datasets, plant image datasets, author analysis code, trained model checkpoints, or supplements with availability statements. All cited datasets (e.g., Plant Village, Kaggle cotton) p

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