Cucumber disease detection using artificial neural network

Pawar, Pooja; Turkar, Varsha; Patil, Pravin

doi:10.1109/inventive.2016.7830151

Cited by 50 publications

(21 citation statements)

References 7 publications

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“…ANNs allow us to develop models based on the intrinsic relations among the variables, without prior knowledge of their functional relationships [9]. Soft computing for ANN techniques has been widely used to develop models to predict different crop indicators, such as growth, yield, and other biophysical processes, and also because of the commercial importance of tomato [10][11][12][13][14][15][16][17][18][19][20][21][22][23] and other vegetables, such as lettuce [24][25][26][27][28][29][30], pepper [31][32][33][34], cucumber [35][36][37][38], wheat [39][40][41][42][43][44][45], rice [46][47][48], oat [49], maize [50,51], corn [52][53][54], corn and soybean [55], soybean…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Modeling of Greenhouse Tomato Yield and Aerial Dry Matter

et al. 2020

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Non-linear systems, such as biological systems, can be simulated by artificial neural network (ANN) techniques. This research aims to use ANN to simulate the accumulated aerial dry matter (leaf, stem, and fruit) and fresh fruit yield of a tomato crop. Two feed-forward backpropagation ANNs, with three hidden layers, were trained and validated by the Levenberg–Marquardt algorithm for weights and bias adjusted. The input layer consisted of the leaf area, plant height, fruit number, dry matter of leaves, stems and fruits, and the growth degree-days at 136 days after transplanting (DAT); these were obtained from a tomato crop, a hybrid, EL CID F1, with indeterminate growth habits, grown with a mixture of peat moss and perlite 1:1 (v/v) (substrate) and calcareous soil (soil). Based on the experimentation of the ANNs with one, two and three hidden layers, with MSE values less than 1.55, 0.94 and 0.49, respectively, the ANN with three hidden layers was chosen. The 7-10-7-5-2 and 7-10-8-5-2 topologies showed the best performance for the substrate (R = 0.97, MSE = 0.107, error = 12.06%) and soil (R = 0.94, MSE = 0.049, error = 13.65%), respectively. These topologies correctly simulated the aerial dry matter and the fresh fruit yield of the studied tomato crop.

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Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Modeling of Greenhouse Tomato Yield and Aerial Dry Matter

et al. 2020

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“…Plant diseases severely threaten the yield and quality of agricultural products. Rapid, accurate, and reliable disease detection and identification is vital to disease prevention and control for sustainable agriculture and food security [ 1 ]. Traditional methods rely on agronomists manually checking the plant disease symptoms or visible signs of a pathogen with the naked eye [ 2 , 3 ] or professional analysts performing physiological and biochemical analysis including molecular, serological, and deoxyribose nucleic acid [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…With the rapid development of computer vision and artificial intelligence, image processing techniques have shown great potential in automatic disease diagnosis, which can overcome some defects of the above methods and mitigate the problem of lack of expertise in the field of agriculture [ 7 ]. By now, numerous image processing-based diagnosis methods or systems have been developed by researchers and have achieved great success [ 1 , 8 , 9 , 10 , 11 , 12 , 13 ]. For instance, based on image processing techniques and artificial neural networks, Pawar et al [ 1 ] proposed a real-time cucumber disease detection system that consisted of five sequential procedures, including image acquisition, preprocessing, feature extraction, creating database and classification, providing classification accuracy of 80.45% on cucumber downy mildew, powdery mildew, and healthy plants.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Leaf Image-Based Cucumber Disease Recognition Using the Extended Collaborative Representation Model

Luo

Wang

et al. 2020

Sensors

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Collaborative representation (CR)-based classification has been successfully applied to plant disease recognition in cases with sufficient training samples of each disease. However, collecting enough training samples is usually time consuming and labor-intensive. Moreover, influenced by the non-ideal measurement environment, samples may be corrupted by variables introduced by bad illumination and occlusions of adjacent leaves. Consequently, an extended collaborative representation (ECR)-based classification model is presented in this paper. Then, it is applied to cucumber leaf disease recognition, which constructs a pure spectral library consisting of several representative samples for each disease and designs a universal variation spectral library that deals with linear variables superimposed on samples. Thus, each query sample is encoded as a linear combination of atoms from these two spectral libraries and disease identity is determined by the disease of minimal reconstruction residuals. Experiments are conducted on spectral curves extracted from normal leaves and the disease lesions of leaves infected with cucumber anthracnose and brown spot. The diagnostic accuracy is higher than 94.7% and the average online diagnosis time is short, about 1 to 1.3 ms. The results indicate that the ECR-based classification model is feasible in the fast and accurate diagnosis of cucumber leaf diseases.

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“…In such circumstances, methodologies for automated plant diagnosis characterized by accuracy, speed and low costs have been requested by the agricultural industry. Several studies have been carried out in response to such requests [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. In [4] used support vector machines (SVM) to classify rice plant diseases and attained 92.7% accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Practical Plant Diagnosis System for Field Leaf Images and Feature Visualization

Fujita¹,

Uga²,

Kagiwada³

et al. 2018

IJET

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An accurate, fast and low-cost automated plant diagnosis system has been called for. While several studies utilizing machine learning techniques have been conducted, significant issues remain in most cases where the dataset is not composed of field images and often includes a substantial number of inappropriate labels. In this paper, we propose a practical automated plant diagnosis system. We first build a highly reliable dataset by cultivating plants in a strictly controlled setting. We then develop a robust classifier capable of analyzing a wide variety of field images. We use a total of 9,000 original cucumber field leaf images to identify seven typical viral diseases, Downy mildew and healthy plants including initial symptoms. We also visualize the key regions of diagnostic evidence. Our system attains 93.6% average accuracy, and we confirm that our system captures important features for the diagnosis of Downy mildew.

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Cucumber disease detection using artificial neural network

Cited by 50 publications

References 7 publications

Artificial Neural Network Modeling of Greenhouse Tomato Yield and Aerial Dry Matter

Artificial Neural Network Modeling of Greenhouse Tomato Yield and Aerial Dry Matter

Hyperspectral Leaf Image-Based Cucumber Disease Recognition Using the Extended Collaborative Representation Model

A Practical Plant Diagnosis System for Field Leaf Images and Feature Visualization

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