Custard Apple Leaf Parameter Analysis, Leaf Diseases, and Nutritional Deficiencies Detection Using Machine Learning

Gargade, Appasaheb; Khandekar, Shridhar

doi:10.1007/978-981-15-8391-9_5

Cited by 11 publications

(6 citation statements)

References 24 publications

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“…Table 1 (continuation) [26] Deep Siamese CNN Grape 90% [27] Delta Tributary Network many varieties 96% [39] PCA and Machine Learning Pumpkin 97.30% [20] KNN, SVM Custard 99.50% [30] VGG16, Faster Region based CNN Tea leaf 95.74%…”

Section: Resultsmentioning

confidence: 99%

“…In order to classify the diseases a custom created model classifier along with a pre trained DenseNet-161 classifier is implemented. Gargade A. et al, (2021) introduced an automated computer based model to detect the nutritional deficiencies and some of the leaf diseases in custard apple plant. The algorithms applied for the classification are SVM and K Nearest neighbor.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Techniques in Plant Disease Detection and Classification – A State of the Art

John

Arul

2021

INMATEH

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As we belong to a developing country, the agricultural importance is a known criterion. Majority of the Indians depend on agriculture for their basic living. It also serves as the backbone of the Indian economy. Therefore this sector should be considered important and taken care of. Diseases affecting the plants and pest are the two major threats of agriculture production. Naked eye observation followed by the addition of chemical fertilizers is the traditional method adopted by most of the farmers to avoid plant diseases. But the main limitation to this method is that it works only in the case of small scale farming. In order to tackle this issue many automatic plant disease detection systems have been developed from the early 70s. This paper is intended to survey some of the existing works in plant disease recognition that include various procedures, materials and approaches. They use different machine learning algorithms, image processing techniques and deep learning methods for disease detection. This paper also compares and suggests novel methods to recognize and classify the various kinds of infections affecting agricultural plants.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning Techniques in Plant Disease Detection and Classification – A State of the Art

John

Arul

2021

INMATEH

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“…Gargade et al [9] devised a parameter-measuring system using the k-NN algorithm to diagnose apple leaf faults. Prediction of apple leaves was accomplished with the help of a Multilayered Perceptron (MLP) pattern classifier that used 11 features extracted from images of apple leaves [10].…”

Section: Related Workmentioning

confidence: 99%

Sustainable Apple Disease Management Using an Intelligent Fine-Tuned Transfer Learning-Based Model

Sulaiman,

Anand,

Gupta

et al. 2023

Sustainability

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Apple foliar diseases are a group of diseases that affect the leaves of apple trees. These diseases can significantly impact apple tree health and fruit yield. Ordinary apple foliar diseases include frog_eye_leaf_spots, powdery mildew, rust, apple scabs, etc. Early detection of these diseases is important for effective apple crop management to increase the yield of apples. Therefore, this research proposes a fine-tuned EfficientNetB3 model for the quick and precise assessment of these apple foliar diseases. A dataset containing 23,187 RGB images of eleven different apple foliar diseases is used for experimentation. The proposed model is compared with four transfer learning models, i.e., InceptionResNetV2, ResNet50, AlexNet, and VGG16. All models are fine-tuned by adding different layers like the global average pooling layer, flatten layer, dropout layer, and dense layer. The performance of these five models is compared in terms of the precision, recall, accuracy, and F1-score. The EfficientNetB3 outperformed the other models in terms of all performance parameters. The best model is further optimized with the help of three optimizers, i.e., Adam, SGD, and Adagrad. The proposed model achieved the precision, recall, and F1-score values of 86%, 88%, and 86%, respectively, at 32 batch sizes and 10 epochs. This research formulated a model for an apple foliar disease diagnosis within sustainable agriculture.

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“…Shi et al (2017) proposed an apple disease recognition method based on two-dimensional subspace learning dimensionality reduction, with recognition accuracy above 90% on the apple leaf disease dataset. Gargade and Khandekar (2021) used K-NN and SVM algorithms to classify apple leaf defects with 99.5% accuracy. Jan and Ahmad (2020) used 11 apple leaf image features and a multilayer perceptron (MLP) pattern classifier to detect apple Alternaria leaf blotch with 99.1% accuracy.…”

Section: Introductionmentioning

confidence: 99%

YOLOV5-CBAM-C3TR: an optimized model based on transformer module and attention mechanism for apple leaf disease detection

Lv,

2024

Front. Plant Sci.

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Apple trees face various challenges during cultivation. Apple leaves, as the key part of the apple tree for photosynthesis, occupy most of the area of the tree. Diseases of the leaves can hinder the healthy growth of trees and cause huge economic losses to fruit growers. The prerequisite for precise control of apple leaf diseases is the timely and accurate detection of different diseases on apple leaves. Traditional methods relying on manual detection have problems such as limited accuracy and slow speed. In this study, both the attention mechanism and the module containing the transformer encoder were innovatively introduced into YOLOV5, resulting in YOLOV5-CBAM-C3TR for apple leaf disease detection. The datasets used in this experiment were uniformly RGB images. To better evaluate the effectiveness of YOLOV5-CBAM-C3TR, the model was compared with different target detection models such as SSD, YOLOV3, YOLOV4, and YOLOV5. The results showed that YOLOV5-CBAM-C3TR achieved mAP@0.5, precision, and recall of 73.4%, 70.9%, and 69.5% for three apple leaf diseases including Alternaria blotch, Grey spot, and Rust. Compared with the original model YOLOV5, the mAP 0.5increased by 8.25% with a small change in the number of parameters. In addition, YOLOV5-CBAM-C3TR can achieve an average accuracy of 92.4% in detecting 208 randomly selected apple leaf disease samples. Notably, YOLOV5-CBAM-C3TR achieved 93.1% and 89.6% accuracy in detecting two very similar diseases including Alternaria Blotch and Grey Spot, respectively. The YOLOV5-CBAM-C3TR model proposed in this paper has been applied to the detection of apple leaf diseases for the first time, and also showed strong recognition ability in identifying similar diseases, which is expected to promote the further development of disease detection technology.

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Custard Apple Leaf Parameter Analysis, Leaf Diseases, and Nutritional Deficiencies Detection Using Machine Learning

Cited by 11 publications

References 24 publications

Machine Learning Techniques in Plant Disease Detection and Classification – A State of the Art

Machine Learning Techniques in Plant Disease Detection and Classification – A State of the Art

Sustainable Apple Disease Management Using an Intelligent Fine-Tuned Transfer Learning-Based Model

YOLOV5-CBAM-C3TR: an optimized model based on transformer module and attention mechanism for apple leaf disease detection

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