Image‐Based Detection of Plant Diseases: From Classical Machine Learning to Deep Learning Journey

Khan, Rehan Ullah; Khan, Khalil; Albattah, Waleed; Qamar, Ali Mustafa

doi:10.1155/2021/5541859

Cited by 59 publications

(24 citation statements)

References 57 publications

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“…Reference [15] discusses various classical machine learning and deep learning techniques used in detection of plant diseases. They also elaborate on how while there are several mobile and online applications for this task, only few of them are publicly available and accessible online.…”

Section: B Plant Disease Detectionmentioning

confidence: 99%

Farmer's Assistant: A Machine Learning Based Application for Agricultural Solutions

Gupta¹,

Chopade²,

Jain³

et al. 2022

Preprint

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Farmers face several challenges when growing crops like uncertain irrigation, poor soil quality, etc. Especially in India, a major fraction of farmers do not have the knowledge to select appropriate crops and fertilizers. Moreover, crop failure due to disease causes a significant loss to the farmers, as well as the consumers. While there have been recent developments in the automated detection of these diseases using Machine Learning techniques, the utilization of Deep Learning has not been fully explored. Additionally, such models are not easy to use because of the high-quality data used in their training, lack of computational power, and poor generalizability of the models. To this end, we create an open-source easy-to-use web application to address some of these issues which may help improve crop production. In particular, we support crop recommendation, fertilizer recommendation, plant disease prediction, and an interactive news-feed. In addition, we also use interpretability techniques in an attempt to explain the prediction made by our disease detection model.

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Section: B Plant Disease Detectionmentioning

confidence: 99%

Farmer's Assistant: A Machine Learning Based Application for Agricultural Solutions

Gupta¹,

Chopade²,

Jain³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The authors of [31] provided a survey on ongoing research related to computer vision, IoT, and data fusion for crop disease detection using ML techniques. The authors of [32] proposed a review of image-based plant disease detection, focusing on ML and deep learning. The authors of [33] provided a survey on the recent findings on the genes that control SCN resistance in soybeans.…”

Section: Related Workmentioning

confidence: 99%

Soybean cyst nematode detection and management: a review

et al. 2022

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Soybeans play a key role in global food security. U.S. soybean yields, which comprise $$32\%$$ 32 % of the total soybeans planted in the world, continue to experience unprecedented grain loss due to the soybean cyst nematode (SCN) plant pathogen. SCN remains one of the primary disruptive pests despite the existence of advanced management techniques such as crop rotation and SCN-resistant varieties. SCN detection is a key step in managing this disease; however, early detection is challenging because soybeans do not show any above ground symptoms unless they are significantly damaged. Direct soil sampling remains the most common method for SCN detection, however, this method has several problems. For example, the threshold damage methods—adopted by most of the laboratories to make recommendations—is not reliable as it does not consider soil pH, N, P, and K values and relies solely on the egg count instead of assessment of the root infection. To overcome the challenges of manual soil sampling methods, deep learning and hyperspectral imaging are important current topics in precision agriculture for plant disease detection and have been proposed as cost-effective and efficient detection methods that can work at scale. We have reviewed more than 150 research papers focusing on soybean cyst nematodes with an emphasis on deep learning techniques for detection and management. First: we describe soybean vegetation and reproduction stages, SCN life cycles, and factors influencing this disease. Second: we highlight the impact of SCN on soybean yield loss and the challenges associated with its detection. Third: we describe direct sampling methods in which the soil samples are procured and analyzed to evaluate SCN egg counts. Fourth: we highlight the advantages and limitations of these direct methods, then review computer vision- and remote sensing-based detection methods: data collection using ground, aerial, and satellite approaches followed by a review of machine learning methods for image analysis-based soybean cyst nematode detection. We highlight the evaluation approaches and the advantages of overall detection workflow in high-performance and big data environments. Lastly, we discuss various management approaches, such as crop rotation, fertilization, SCN resistant varieties such as PI 88788, and SCN’s increasing resistance to these strategies. We review machine learning approaches for soybean crop yield forecasting as well as the influence of pesticides, herbicides, and fertilizers on SCN infestation reduction. We provide recommendations for soybean research using deep learning and hyperspectral imaging to accommodate the lack of the ground truth data and training and testing methodologies, such as data augmentation and transfer learning, to achieve a high level of detection accuracy while keeping costs as low as possible.

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“…Traditional expert diagnosis of rice leaf disease is expensive and vulnerable to subjective error [12]. Artificial Intelligence [13], machine learning, computer vision, the Internet Of Things [14], and deep learning [15] are commonly utilized in crop disease diagnosis due to the rapid advancement of computer technology [16].…”

Section: Introductionmentioning

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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Image‐Based Detection of Plant Diseases: From Classical Machine Learning to Deep Learning Journey

Cited by 59 publications

References 57 publications

Farmer's Assistant: A Machine Learning Based Application for Agricultural Solutions

Farmer's Assistant: A Machine Learning Based Application for Agricultural Solutions

Soybean cyst nematode detection and management: a review

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

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