Predicting rice diseases across diverse agro-meteorological conditions using an artificial intelligence approach

Patil, Rutuja Rajendra; Kumar, Sumit

doi:10.7717/peerj-cs.687

Cited by 18 publications

(7 citation statements)

References 34 publications

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“…Sigmoid, hyperbolic tangent (tanh), and rectified linear activation function (ReLU) are the most popular activation functions. Of these, the hyperbolic tangent is preferred when the input and output are constrained to values between −1 and 1 ( Patil & Kumar, 2021 ). The formula of the hyperbolic tangent activation function is given in Eq.…”

Section: Methodsmentioning

confidence: 99%

Detection of protein, starch, oil, and moisture content of corn kernels using one-dimensional convolutional autoencoder and near-infrared spectroscopy

Cataltas

Tutuncu

2023

PeerJ Computer Science

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Background Analysis of the nutritional values and chemical composition of grain products plays an essential role in determining the quality of the products. Near-infrared spectroscopy has attracted the attention of researchers in recent years due to its advantages in the analysis process. However, preprocessing and regression models in near-infrared spectroscopy are usually determined by trial and error. Combining newly popular deep learning algorithms with near-infrared spectroscopy has brought a new perspective to this area. Methods This article presents a new method that combines a one-dimensional convolutional autoencoder with near-infrared spectroscopy to analyze the protein, moisture, oil, and starch content of corn kernels. First, a one-dimensional convolutional autoencoder model was created for three different spectra in the corn dataset. Thirty-two latent variables were obtained for each spectrum, which is a low-dimensional spectrum representation. Multiple linear regression models were built for each target using the latent variables of obtained autoencoder models. Results R2, RMSE, and RMSPE were used to show the performance of the proposed model. The created one-dimensional convolutional autoencoder model achieved a high reconstruction rate with a mean RMSPE value of 1.90% and 2.27% for calibration and prediction sets, respectively. This way, a spectrum with 700 features was converted to only 32 features. The created MLR models which use these features as input were compared to partial least squares regression and principal component regression combined with various preprocessing methods. Experimental results indicate that the proposed method has superior performance, especially in MP5 and MP6 datasets.

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Section: Methodsmentioning

confidence: 99%

Detection of protein, starch, oil, and moisture content of corn kernels using one-dimensional convolutional autoencoder and near-infrared spectroscopy

Cataltas

Tutuncu

2023

PeerJ Computer Science

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“…So, by using this method, training of the model is not required from scratch. Therefore, artificial neural networks are famous in agriculture as they produce more accurate results in plant disease detection [64]. In [65], Bari et al employed Faster RCNN to detect rice diseases in rice crops.…”

Section: Artificial Intelligence In Plant Disease Detectionmentioning

confidence: 99%

Role of Artificial Intelligence in Agriculture: An Analysis and Advancements With Focus on Plant Diseases

Rani,

Sahoo,

Bellamkonda

et al. 2023

IEEE Access

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The increased demand for food is accelerating plant diseases globally. Hence, a manual process of detection of plant diseases is almost impossible. Artificial Intelligence (AI) can offer several solutions to many problems of farmers. AI is facile to mitigate farmer's agriculture challenges. With the unpredictable changing climate, plants are often affected by several diseases where AI can play an important role. AI techniques such as Machine learning and deep Learning have been employed in literature to detect, predict, and design recommendation systems for plant diseases. Significant work has been done in this area in the last two decades, which can change farmer's lives in the coming years. This paper presents a systematic multi-fold survey and analysis of such work focusing on recent AI techniques developed to combat plant diseases. This article discusses various challenges faced by farmers and their AI solutions. It analyzes several applications of AI in agriculture, current trends, and advancements in plant disease detection. It will serve researchers as a valuable document for further research to solve farmer's issues.

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“…It is perceived that the fine-tuning approach is superior to a CNN model that is trained from scratch. The ANN approach became popular in the agricultural domain and thus was applied to the crop disease detection domain [30]. On a similar basis, Faster RCNN was used to control the rice diseases that overcame the limitation of overfitting.…”

Section: A State Of the Art Approaches In The Identification Of Crop ...mentioning

confidence: 99%

Rice Transformer: A Novel Integrated Management System for Controlling Rice Diseases

Patil

Kumar

2022

IEEE Access

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Rice disease classification is vital during the cultivation of rice crops. However, rice diseases were initially detected by visual examination from agricultural experts. Later the detection process progressed to automation, which involved images. The images captured lead to a lack of supporting information. The traditional approaches are less accurate when used with real time images. To address this limitation, a novel Rice Transformer is proposed in the paper that merges inputs from agricultural sensors and image data captured from the fields simultaneously. The proposed system consists of two branches: the sensor and image branches. Specifically, the attention approach is employed to extract the features from both modalities. Later, the extracted features are sent to the cross-attention module as input in a crisscross fashion, enhancing the ability to identify the features specific to rice diseases. The extracted features are further pooled, merged, and later passed through the Softmax classifier to classify the rice disease precisely. The dataset collected is a customized dataset with 4200 samples collected on a real-time basis from rice farms. The experiments conducted on the dataset represent that the proposed approach outperforms all the other fusion and attention models considered for comparison in this paper. The ablation analysis and performance metrics are measured to determine the effectiveness of the proposed system. The results achieved are quite promising as the proposed Rice transformer model achieves an accuracy of 97.38% for controlling rice disease.

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Predicting rice diseases across diverse agro-meteorological conditions using an artificial intelligence approach

Cited by 18 publications

References 34 publications

Detection of protein, starch, oil, and moisture content of corn kernels using one-dimensional convolutional autoencoder and near-infrared spectroscopy

Detection of protein, starch, oil, and moisture content of corn kernels using one-dimensional convolutional autoencoder and near-infrared spectroscopy

Role of Artificial Intelligence in Agriculture: An Analysis and Advancements With Focus on Plant Diseases

Rice Transformer: A Novel Integrated Management System for Controlling Rice Diseases

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