On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey

Orchi, Houda; Sadik, Mohamed; Khaldoun, Mohammed

doi:10.3390/agriculture12010009

Cited by 85 publications

(39 citation statements)

References 106 publications

(131 reference statements)

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“…For example, the way of conducting a disease survey can be improved. Besides the questionnaire, the disease incidence can be indicated using imagebased or spectral-based approaches, to mitigate possible subjective error [39]. In addition, some observation data from wireless sensors networks (WSNs) and satellite remote sensing can also be included in the forecasting models to indicate environmental conditions and the growing status of host plants [40,41].…”

Section: Discussionmentioning

confidence: 99%

Forecasting Alternaria Leaf Spot in Apple with Spatial-Temporal Meteorological and Mobile Internet-Based Disease Survey Data

Huang

Zhang

et al. 2022

Agronomy

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Early warning of plant diseases and pests is critical to ensuring food safety and production for economic crops. Data sources such as the occurrence, frequency, and infection locations are crucial in forecasting plant diseases and pests. However, at present, acquiring such data relies on fixed-point observations or field experiments run by agricultural institutions. Thus, insufficient data and low rates of regional representative are among the major problems affecting the performance of forecasting models. In recent years, the development of mobile internet technology and conveniently accessible multi-source agricultural information bring new ideas to plant diseases’ and pests’ forecasting. This study proposed a forecasting model of Alternaria Leaf Spot (ALS) disease in apple that is based on mobile internet disease survey data and high resolution spatial-temporal meteorological data. Firstly, a mobile internet-based questionnaire was designed to collect disease survey data efficiently. A specific data clean procedure was proposed to mitigate the noise in the data. Next, a sensitivity analysis was performed on the temperature and humidity data, to identify disease-sensitive meteorological factors as model inputs. Finally, the disease forecasting model of the apple ALS was established using four machine learning algorithms: Logistic regression(LR); Fisher linear discriminant analysis(FLDA); Support vector machine(SVM); and K-Nearest Neighbors (KNN). The KNN algorithm is recommended in this study, which produced an overall accuracy of 88%, and Kappa of 0.53. This paper shows that through mobile internet disease survey and a proper data clean approach, it is possible to collect necessary data for disease forecasting in a short time. With the aid of high resolution spatial-temporal meteorological data and machine learning approaches, it is able to achieve disease forecast at a regional scale, which will facilitate efficient disease prevention practices.

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Section: Discussionmentioning

confidence: 99%

Forecasting Alternaria Leaf Spot in Apple with Spatial-Temporal Meteorological and Mobile Internet-Based Disease Survey Data

Huang

Zhang

et al. 2022

Agronomy

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“…Electron micrographs and electron micrograph analysis techniques perform crop tracking by means of analysis in the temporal dimension. In [28], the authors measured the progress of the crystalized region using a selective wavelength filter and an infrared camera. The results in [29] resembled obtained by means of the traditional manual analysis test.…”

Section: Electron Micrograph Processing For the Detection Of Calcium ...mentioning

confidence: 99%

Computerized Detection of Calcium Oxalate Crystal Progression

Mahmoud

2022

Crystals

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Calcium oxalate crystals in plants can cause health issues such as kidney stones if ingested in large amounts. Calcium oxalate crystallizations affect approximately 4% of plants. Some of these crystallizations are more common, and human and animal ingestion can be avoided if the degree of severity is detected at an early stage. Therefore, in this paper, we present a computerized method for detecting calcium oxalate crystallizations at an early stage, when chances for avoiding it are higher. In our research, electron micrograph processing techniques are used to extract features and measure the degree of crystallization progression in cases of crystalized plants and normal plants. A new fast search algorithm—ODS: One Direction Search—is proposed to detect calcium oxalate crystal progression. The calcium oxalate crystal progression is detected on the basis of electron micrographs of calcium oxalate crystals by means of a temporal test. We employed deep learning for feature extraction. The deep learning technique uses transfer learning, which allows the proposed detection model to be trained on only a small amount of data regarding calcium oxalate crystals for the determination of the presence of calcium oxalate crystals and the severity of the cases. The experimental results, using electron micrographs of 6900 clusters, demonstrated a success rate of 97.5% when detecting cases of calcium oxalate crystals. The simulation results of the new temporal algorithm show an enhancement of the speed by 70% compared to well-known temporal algorithms, and increased accuracy when computing PRSN against other algorithms.

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“…The CNN models use sliding window extraction to automatically extract image features and then use fully connected layers for classification to implement an end-to-end disease detection model. Recently, CNN models were used to detect and identify crop diseases instead of traditional machine learning methods [12]. Liu et al proposed a novel recognition approach based on an improved CNN model for the diagnosis of grape leaf diseases [13].…”

Section: Introductionmentioning

confidence: 99%

GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases

et al. 2022

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Most convolutional neural network (CNN) models have various difficulties in identifying crop diseases owing to morphological and physiological changes in crop tissues, and cells. Furthermore, a single crop disease can show different symptoms. Usually, the differences in symptoms between early crop disease and late crop disease stages include the area of disease and color of disease. This also poses additional difficulties for CNN models. Here, we propose a lightweight CNN model called GrapeNet for the identification of different symptom stages for specific grape diseases. The main components of GrapeNet are residual blocks, residual feature fusion blocks (RFFBs), and convolution block attention modules. The residual blocks are used to deepen the network depth and extract rich features. To alleviate the CNN performance degradation associated with a large number of hidden layers, we designed an RFFB module based on the residual block. It fuses the average pooled feature map before the residual block input and the high-dimensional feature maps after the residual block output by a concatenation operation, thereby achieving feature fusion at different depths. In addition, the convolutional block attention module (CBAM) is introduced after each RFFB module to extract valid disease information. The obtained results show that the identification accuracy was determined as 82.99%, 84.01%, 82.74%, 84.77%, 80.96%, 82.74%, 80.96%, 83.76%, and 86.29% for GoogLeNet, Vgg16, ResNet34, DenseNet121, MobileNetV2, MobileNetV3_large, ShuffleNetV2_×1.0, EfficientNetV2_s, and GrapeNet. The GrapeNet model achieved the best classification performance when compared with other classical models. The total number of parameters of the GrapeNet model only included 2.15 million. Compared with DenseNet121, which has the highest accuracy among classical network models, the number of parameters of GrapeNet was reduced by 4.81 million, thereby reducing the training time of GrapeNet by about two times compared with that of DenseNet121. Moreover, the visualization results of Grad-cam indicate that the introduction of CBAM can emphasize disease information and suppress irrelevant information. The overall results suggest that the GrapeNet model is useful for the automatic identification of grape leaf diseases.

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On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey

Cited by 85 publications

References 106 publications

Forecasting Alternaria Leaf Spot in Apple with Spatial-Temporal Meteorological and Mobile Internet-Based Disease Survey Data

Forecasting Alternaria Leaf Spot in Apple with Spatial-Temporal Meteorological and Mobile Internet-Based Disease Survey Data

Computerized Detection of Calcium Oxalate Crystal Progression

GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases

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