Crop Disease Severity Research Articles

Unlocking the potential of simulated hyperspectral imaging in agro environmental analysis: a comprehensive study of algorithmic approaches

This study focuses on identifying and evaluating the severity of powdery mildew disease in tomato plants. The uniqueness of this work lies in combining the imaging and advanced deep learning methods to develop a technique that transforms Red Green Blue (RGB) images into Simulated Hyperspectral Images (SHSI) to perform spectral and spatial analysis for precise detection and assessment of powdery mildew severity, thereby enhancing disease management. Furthermore, this research evaluates three advanced pre-trained VGG16 models, ResNet50 and EfficientNet-B7 algorithms for image preprocessing and feature extraction. Extracted features are passed to a neural network generator model to convert RGB image features into SHSIs, providing insights into the spectrum. This method enables the image analysis to perform assessments from SHSIs for health classification using Normalized Difference Vegetation Index (NDVI) values, which are meticulously compared with accurate hyperspectral data using metrics like mean absolute error (MAE) and root mean squared error (RMSE). This strategy enhances precision farming, environmental monitoring, and remote sensing accuracy. Results show that ResNet50’s architecture offers a robust framework for this study’s spectral and spatial analysis, making it a suitable choice over VGG16 and EfficientNet-B7 for transforming RGB images into SHSI. These simulated hyperspectral images offer a scalable and affordable approach for precise assessment of crop disease severity.

A crop disease severity index derived from transfer learning and feature fusion using enhanced OPTICS algorithm

The adoption of automated methods for the identification and assessment of tomato-related disorders is highly sought-after in the agriculture sector. Using this technology is crucial for reducing wasteful spending, increasing the efficiency of treatments, and ultimately growing more resilient crops by reducing losses in agricultural output and maximising the effectiveness of these processes. An automated method has been suggested for accurately identifying and classifying diseases using a single photograph. The described method for disease detection in tomato plants makes use of a computer vision-based technique. Image processing, ML, and deep learning are just a few of the methods that this strategy uses. The goal of this approach is to prevent tomato crops from being damaged by various illnesses by reducing the need of conventional procedures. Bacterial spot, early blight, late blight, leaf mould, spider mites, target spot, spotted spider mite, mosaic virus, and yellow leaf curl are all examples of these illnesses. The following ten diseases frequently strike tomato crops in India. By utilising picture segmentation in combination with the Enhanced OPTICS algorithm (EOPTICSA), the affected area of the tomato plant may be precisely detected and defined after image pre-processing procedures have been used. It may be necessary to look for certain visual signs in order to diagnose the previously mentioned illnesses. The primary goal of this study was to evaluate the efficacy of the EOPTICSA method for detecting diseases in plant leaves. To eliminate the geometric features associated with colour, texture, and leaf arrangement in the provided plant pictures, image segmentation and edge detection methods are employed. Using these methods allows us to achieve our goal. Various efficacy measures are used to assess and provide a technique recommendation. This research shows that when performance metrics are used to implement these strategies, the suggested strategy outperforms the current methods in terms of accuracy, precision, and F1-score. The process of detecting sickness involves several consecutive steps. Capturing images, segmenting them, detecting edges, and determining the infection’s severity are all steps in this process. To accomplish the goal of recognising and categorising different types of diseases that might impact tomato plants, the method of transfer learning is employed. As soon as the problem is identified, it is recommended to take proactive measures to help individuals and organisations involved in agriculture address the effects of these disorders using appropriate measures.

A new mobile diagnosis system for estimation of crop disease severity using deep transfer learning

Crop diseases pose as a major threat to global food security. Minimizing disease-induced damage during crop growth and optimizing crop yields are vital for agricultural sustainability. Therefore, advanced disease detection and prevention of such diseases are crucial and the detection must be prompt and efficient as it is essential for the implementation of appropriate control measures. In this work, a parallel deep learning framework based on deep feature fusion is developed to precisely identify the severity of crop diseases. The framework utilizes ResNet50 and Xception as separate branches for feature extraction. Convolutional layer weights are initialized through transfer learning techniques employing models pre-trained on the ImageNet dataset. A fine-tuning strategy is employed for the optimization of convolutional layers and the design of the top layer. This framework achieves an accuracy of 88.58% on the AI Challenger 2018 dataset, marking an enhancement of 2.8% and 13% over other influential deep learning models, and it also outperforms some recent works. Likewise, the framework exhibits a recognition accuracy of 99.53% on the PlantVillage dataset. Moreover, an Android-based application is developed to diagnose the severity of crop diseases in real-time. The diagnostic system swiftly procures results and provides control recommendations through the capture and upload of images to the platform. The advanced severity detection system reduces the expertise required from users, facilitating precise prevention and control measures whilst focusing on accessibility. This work aims to provide innovative approaches and solutions for disease detection in the agricultural field, utilizing artificial intelligence to enhance agricultural informatization and creating more sustainable farming methods.

PADDY CARE: DETECTION OF PADDY PLANT LEAF DISEASE USING YOLOV8

Paddy is the most significant crop utilized by more than 2.6 billion people. Paddy leaf infections are a common hazard to rice production, affecting many farmers all over the world. Paddy Plant diseases are a severe threat to the entire production. However, plant disease diagnosis through observation of the plant leaves is a very complex work. Even experienced farmers are often unable to successfully identify certain plant diseases, leading to wrong conclusions and treatment methods. Thus, the traditional method of identifying crop diseases by visual observation is no longer suitable for modern agriculture. In the widespread application of various machine learning techniques, recognition time consumption and accuracy remain the main challenges in moving agriculture toward industrialization. Therefore, it is essential for farmers to effectively deal with them and check them with the help of timely prevention. Classifying the severity of crop diseases are the requirement for formulating disease prevention and control strategies. Early detection and treatment of rice leaf infection are critical for promoting healthy rice plant growth and ensuring adequate supply for the fast-growing population. Therefore, automatic and accurate diagnosis of plant diseases plays an essential role in ensuring high yield and quality. This research work attempts to create a simple and best model for Paddy leaf disease detection using deep learning model based on the Convolutional Neural Network (CNN) and Yolo v8. Yolo v8 is used to detect the disease part and Convolutional Neural Network used to categorize and analyzing the leaf condition that would be diseased and non-diseased. Using the Preventive Knowledge Solution, the farmers can get the preventive measures to protect the crop from the disease. And also this research work recommends the pesticides and fertilizers to defeat the identified paddy leaf disease. This model is able to differentiate and successfully detect the rice leaf diseases. The developed model utilizes epochs: 100. The experimental results show that the deep learning model created with 100 epochs has shown the best performance with precision, recall, and mAP value of 1.00, 0.94, and 0.62, respectively. At the same time, the advantages in terms of accuracy and computational cost can meet the needs of agricultural industrialization.

Winter Durum Wheat Disease Severity Detection with Field Spectroscopy in Phenotyping Experiment at Leaf and Canopy Level

Accurate disease severity assessment is critical for plant breeders, as it directly impacts crop yield. While hyperspectral remote sensing has shown promise for disease severity assessment in breeding experiments, most studies have focused on either leaf or canopy levels, neglecting the valuable insights gained from a combined approach. Moreover, many studies have centered on experiments involving a single disease and a few genotypes. However, this approach needs to accurately represent the challenges encountered in field conditions, where multiple diseases could occur simultaneously. To address these gaps, our current study analyses a combination of diseases, yellow rust, brown rust, and yellow leaf spots, collectively evaluated as the percentage of the diseased leaf area relative to the total leaf area (DA) at both leaf and canopy levels, using hyperspectral data from an ASD field spectrometer. We quantitatively estimate overall disease severity across fifty-two winter durum wheat genotypes categorized into early (medium milk) and late (late milk) groups based on the phenophase. Chlorophyll content (CC) within each group is studied concerning infection response, and a correlation analysis is conducted for each group with nine vegetation indices (VI) known for their sensitivity to rust and leaf spot infection in wheat. Subsequent parametric (linear and polynomial) and nonparametric (partial least squares and kernel ridge) regression analyses were performed using all available spectral bands. We found a significant reduction in Leaf CC (>30%) in the late group and Canopy CC (<10%) for both groups. YROI and LRDSI_1 are the VIs that exhibited notable and strong negative correlations with Leaf CC in the late group, with a Pearson coefficient of −0.73 and −0.72, respectively. Interestingly, spectral signatures between the early and late disease groups at both leaf and canopy levels exhibit opposite trends. The regression analysis showed we could retrieve leaf CC only for the late group, with R2 of 0.63 and 0.42 for the cross-validation and test datasets, respectively. Canopy CC retrieval required separate models for each group: the late group achieved R2 of 0.61 and 0.37 (cross-validation and test), while the early group achieved R2 of 0.48 and 0.50. Similar trends were observed for canopy DA, with separate models for early and late groups achieving comparable R2 values of 0.53 and 0.51 (cross-validation) and 0.35 and 0.36 (test), respectively. All of our models had medium accuracy and tended to overfit. In this study, we analyzed the spectral response mechanism associated with durum wheat diseases, offering a novel crop disease severity assessment approach. Additionally, our findings serve as a foundation for detecting resistant wheat varieties, which is the most economical and environmentally friendly management strategy for wheat leaf diseases on a large scale in the future.

Rational modification of melatonin for broad-spectrum antifungal agents discovery.

Natural products, known for their environmental safety, are regarded as a significant basis for the modification and advancement of fungicides. Melatonin, as a low-cost natural indole, exhibits diverse biological functions, including antifungal activity. However, its potential as an antifungal agent has not been fully explored. In this study, a series of melatonin derivatives targeting the mitogen-activated protein kinase (Mps1) protein of fungal pathogens were synthesized based on properties of melatonin, among which the trifluoromethyl-substituted derivative Mt-23 exhibited antifungal activity against seven plant pathogenic fungi, and effectively reduced the severity of crop diseases, including rice blast, Fusarium head blight of wheat and gray mold of tomato. In particular, its EC50 (5.4 µM) against the rice blast fungus Magnaporthe oryzae is only one-fourth that of isoprothiolane (22 µM), a commercial fungicide. Comparative analyzes revealed that Mt-23 simultaneously targets the conserved protein kinase Mps1 and lipid protein Cap20. Surface plasmon resonance assays showed that Mt-23 directly binds to Mps1 and Cap20. In this study, we provide a strategy for developing antifungal agents by modifying melatonin, and the resultant melatonin derivative Mt-23 is a commercially valuable, eco-friendly and broad-spectrum antifungal agent to combat crop disease.

Assessing fusarium oxysporum disease severity in cotton using unmanned aerial system images and a hybrid domain adaptation deep learning time series model

Currently, deep learning has achieved remarkable success in estimating plant disease from unmanned aerial system (UAS) images. However, two critical challenges remain unexplored: spatiotemporal variations in disease symptoms and the domain shift between source and target datasets. To overcome these challenges, this paper proposes an approach that incorporates temporal aspects of disease progression using time series analysis. Spatiotemporal information is integrated by combining convolutional neural networks and bidirectional long-short term memory (CNN-BiLSTM) to classify the disease into five severity levels. Various feature extraction methods, including both handcrafted and CNN-based feature extractors, are evaluated. Furthermore, to tackle the problem of domain shift, a feature-level domain adaptation method is proposed. This method aims to learn transferable feature representations that remain consistent despite variations between source and target datasets. This approach enhances the spatiotemporal transferability of the CNN-BiLSTM model, enabling the effective utilisation of historical datasets. The study demonstrates that the CNN-BiLSTM model outperforms traditional time-independent machine-learning methods that rely on handcrafted features. Specifically, the Resnet101-BiLSTM model achieves the highest overall classification accuracy of 89.7% among all tested models evaluated on a one-year dataset. Moreover, it shows superior generalisation with 72.7% accuracy for cross-spatiotemporal disease severity classification using domain adaptation, as demonstrated through two-year experiments. By reducing the domain shift of source and target datasets and harnessing time series high-resolution images obtained throughout the crop growing season, this hybrid approach has substantial potential to advance the assessment of crop disease severity in field conditions.

Tomato Leaf Disease Identification by Restructured Deep Residual Dense Network

As COVID-19 spread worldwide, many major grain-producing countries have adopted measures to restrict their grain exports; food security has aroused great concern from various parties. How to improve grain production has become one of the most important issues facing all countries. However, crop diseases are a difficult problem for many farmers so it is important to master the severity of crop diseases timely and accurately to help staff take further intervention measures to minimize plants being further infected. In this paper, a restructured residual dense network was proposed for tomato leaf disease identification; this hybrid deep learning model combines the advantages of deep residual networks and dense networks, which can reduce the number of training process parameters to improve calculation accuracy as well as enhance the flow of information and gradients. The original RDN model was first used in image super resolution, so we need to restructure the network architecture for classification tasks through adjusted input image features and hyper parameters. Experimental results show that this model can achieve a top-1 average identification accuracy of 95% on the Tomato test dataset in AI Challenger 2018 datasets, which verifies its satisfactory performance. The restructured residual dense network model can obtain significant improvements over most of the state-of-the-art models in crop leaf identification, as well as requiring less computation to achieve high performance

The Evaluation of the Grade of Leaf Disease in Apple Trees Based on PCA-Logistic Regression Analysis

Extensive research suggested that the core of how to use pesticides scientifically is the careful and accurate determination of the severity of crop diseases. The existing grading standards of plant leaf diseases have been excessively singular. Thus, the diseases roughly fall into general and severe grades. To address the above problems, this study considered the effect of the distribution of disease spots, and two evaluation indicators (termed the imbalance degree and main vein distance) were newly added to optimize the grading criteria of apple leaf diseases. Combined with other factors, the grade evaluation indicator was determined through PCA principal component analysis. A gradual multivariate logistic regression algorithm was proposed to evaluate apple leaf disease grade and an optimized apple leaf disease grade evaluation model was built through PCA-logistic regression analysis. In addition, three common apple leaf diseases with a total of 4500 pictures (i.e., black rot, scab, and rust) were selected from several open-source datasets as the subjects of this paper. The object detection algorithm was then used to verify the effectiveness of the new model. As indicated by the results, it can be seen from the loss curve that the loss rate reaches a stable range of around 70 at the epoch. Compared with Faster R-CNN, the average accuracy of Mask R-CNN for the type and grade recognition of apple leaf disease was optimized by 4.91%, and the average recall rate was increased by 5.19%. The average accuracy of the optimized apple leaf disease grade evaluation model was 90.12%, marking an overall increase of 20.48%. Thus, the effectiveness of the new model was confirmed.

An Improved Approach to Monitoring Wheat Stripe Rust with Sun-Induced Chlorophyll Fluorescence

Sun-induced chlorophyll fluorescence (SIF) has shown potential in quantifying plant responses to environmental changes by which abiotic drivers are dominated. However, SIF is a mixed signal influenced by factors such as leaf physiology, canopy structure, and sun-sensor geometry. Whether the physiological information contained in SIF can better quantify crop disease stresses dominated by biological drivers, and clearly explain the physiological variability of stressed crops, has not yet been sufficiently explored. On this basis, we took winter wheat naturally infected with stripe rust as the research object and conducted a study on the responses of physiological signals and reflectivity spectrum signals to crop disease stress dominated by biological drivers, based on in situ canopy-scale and leaf-scale data. Physiological signals include SIF, SIFyield (normalized by absorbed photosynthetically active radiation), fluorescence yield (ΦF) retrieved by NIRvP (non-physiological components of canopy SIF) and relative fluorescence yield (ΦF-r) retrieved by near-infrared radiance of vegetation (NIRvR). Reflectance spectrum signals include normalized difference vegetation index (NDVI) and near-infrared reflectance of vegetation (NIRv). At the canopy scale, six signals reached extremely significant correlations (P < 0.001) with disease severity levels (SL) under comprehensive experimental conditions (SL without dividing the experimental samples) and light disease conditions (SL < 20%). The strongest correlation between NDVI and SL (R = 0.69) was observed under the comprehensive experimental conditions, followed by NIRv (R = 0.56), ΦF-r (R = 0.53) and SIF (R = 0.51), and the response of ΦF (R = 0.45) and SIFyield (R = 0.34) to SL was weak. Under lightly diseased conditions, ΦF-r (R = 0.62) showed the strongest response to disease, followed by SIFyield (R = 0.60), SIF (R = 0.56) and NIRv (R = 0.54). The weakest correlation was observed between ΦF and SL (R = 0.51), which also showed a result approximating NDVI (R = 0.52). In the case of a high level of crop disease severity, NDVI showed advantages in disease monitoring. In the early stage of crop diseases, which we pay more attention to, compared with SIF and reflectivity spectrum signals, ΦF-r estimated by the newly proposed ‘NIRvR approach’ (which uses SIF together with NIRvR (i.e., SIF/ NIRvR) as a substitute for ΦF) showed superior ability to monitor crop physiological stress, and was more sensitive to plant physiological variation. At the leaf scale, the response of SIF to SL was stronger than that of NDVI. These results validate the potential of ΦF-r estimated by the NIRvR approach to monitoring disease stress dominated by biological drivers, thus providing a new research avenue for quantifying crop responses to disease stress.

Assessing the severity of cotton Verticillium wilt disease from in situ canopy images and spectra using convolutional neural networks

Verticillium wilt (VW) is a common soilborne disease of cotton. It occurs mainly in the seedling and boll-opening stages and severely impairs the yield and quality of the fiber. Rapid and accurate identification and evaluation of VW severity (VWS) forms the basis of field cotton VW control, which has great significance to cotton production. Cotton VWS values are conventionally measured using in-field observations and laboratory test diagnoses, which require abundant time and professional expertise. Remote and proximal sensing using imagery and spectrometry have great potential for this purpose. In this study, we performed in situ investigations at three experimental sites in 2019 and 2021 and collected VWS values, in situ images, and spectra of 361 cotton canopies. To estimate cotton VWS values at the canopy scale, we developed two deep learning approaches that use in situ images and spectra, respectively. For the imagery-based method, given the high complexity of the in situ environment, we first transformed the task of healthy and diseased leaf recognition to the task of cotton field scene classification and then built a cotton field scenes (CFS) dataset with over 1000 images for each scene-unit type. We performed pretrained convolutional neural networks (CNNs) training and validation using the CFS dataset and then used the networks after training to classify scene units for each canopy. The results showed that the DarkNet-19 model achieved satisfactory performance in CFS classification and VWS values estimation (R2 = 0.91, root-mean-square error (RMSE) = 6.35%). For the spectroscopy-based method, we first designed a one-dimensional regression network (1D CNN) with four convolutional layers. After dimensionality reduction by sensitive-band selection and principal component analysis, we fitted the 1D CNN with varying numbers of principal components (PCs). The 1D CNN model with the top 20 PCs performed best (R2 = 0.93, RMSE = 5.77%). These deep learning-driven approaches offer the potential of assessing crop disease severity from spatial and spectral perspectives.

Improved greenhouse self-propelled precision spraying machine—Multiple height and level (MHL) control

In the research, an improved greenhouse self-propelled precision sprayer was designed. The traditional greenhouse spraying machine mainly relies on manual or single height and level operation, but in most cases, this kind of operation method cannot fully meet the requirements of spray. To address this issue, in earlier work an optimization framework was developed, where in step one a fast and accurate identification method for crop disease severity, and in step two design and control a Multiple Height and Level (MHL) rack and image recognition, remote control technology and visible light spectrum information and other configurations to achieve the expected spray needs. Here the aims are: 1) to develop step two; 2) to illustrate the potential precision spraying value and cost savings of both steps by comparing the optimization results with real operating data from traditional greenhouse self-propelled sprayers, as a benchmark. The analysis results show that the improved greenhouse self-propelled precision sprayer has higher uniformity and better stability than the traditional greenhouse self-propelled sprayer.

Crop Disease Recognition Based on Modified Light-Weight CNN With Attention Mechanism

The agricultural production is greatly affected by various plant diseases. Classifying the severity of crop diseases is the requirement for formulating disease prevention and control strategies. However, the differences between different severity of the same crop disease are very tiny. It increases the difficulty of correct crop disease recognition. For example, at the early stage of the disease, the lesions on the leaves are not obvious. And it is very difficult to extract the features of the lesions. However, these very small color and texture differences of the lesions are the key patterns to distinguish different kinds of diseases of the same species. In order to achieve better performance in the fine-grained classification of the crop diseases, a modified light-weight convolution neural network was proposed. Multi-scale convolution kernel and coordinate attention mechanism are introduced in SqueezeNext to extract the features of the lesions accurately. The performance of the proposed model was evaluated using the AI challenger 2018 plant disease recognition dataset, and the recognition accuracy can reach 91.94%, which is 3.02% point higher than the original SqueezeNext model. In order to verify the effectiveness of the proposed model, comparative experiments were carried out using ReseNet50, Xception and mobilenetv2. The experimental results showed that the accuracy of the proposed method was slightly better than Xception, while the model size is only 2.83 MB, which is only 3.45% of Xception. The proposed method balances the performance and efficiency very well. Thus, it is suitable for deployment on mobile terminals and other embedded resource-constrained devices, which help to promote the popularization of smart agriculture application.

A Method for Segmenting Disease Lesions of Maize Leaves in Real Time Using Attention YOLACT++

Northern leaf blight (NLB) is a serious disease in maize which leads to significant yield losses. Automatic and accurate methods of quantifying disease are crucial for disease identification and quantitative assessment of severity. Leaf images collected with natural backgrounds pose a great challenge to the segmentation of disease lesions. To address these problems, we propose an image segmentation method based on YOLACT++ with an attention module for segmenting disease lesions of maize leaves in natural conditions in order to improve the accuracy and real-time ability of lesion segmentation. The attention module is equipped on the output of the ResNet-101 backbone and the output of the FPN. The experimental results demonstrate that the proposed method improves segmentation accuracy compared with the state-of-the-art disease lesion-segmentation methods. The proposed method achieved 98.71% maize leaf lesion segmentation precision, a comprehensive evaluation index of 98.36%, and a mean Intersection over Union of 84.91%; the average processing time of a single image was about 31.5 ms. The results show that the proposed method allows for the automatic and accurate quantitative assessment of crop disease severity in natural conditions.

Effects of Environmental Factors on Crop Diseases Development

Environmental factors are triggering simultaneous effects on the disease incidence and severity of crop diseases in agriculture. These both aspects are affected by various environmental factors viz. temperature, soil moisture, humidity, light, soil properties (pH and nutrients) and atmospheric carbon dioxide. The effect of environmental constraints on host and pathogen has positive, negative or neutral effects on crop disease incidence. Sometimes sole factor is responsible for profused disease incidence. On the other hand, interaction of various environmental factors causes huge intensity of diseases. Crop disease is the result of three way interaction between susceptible host, virulent pathogen and favourable environment. Elevated CO2 concentration and increased temperature influence the plant disease interaction. This review article focuses the relationship between various environmental factors and crop diseases and their role in increasing or decreasing the disease severity.

Tomato Leaf Disease Identification by Restructured Deep Residual Dense Network

As COVID-19 spread worldwide, many major grain-producing countries have adopted measures to restrict their grain exports; food security has aroused great concern from various parties. How to improve grain production has become one of the most important issues facing all countries. However, crop diseases are a difficult problem for many farmers so it is important to master the severity of crop diseases timely and accurately to help staff take further intervention measures to minimize plants being further infected. In this paper, a restructured residual dense network was proposed for tomato leaf disease identification; this hybrid deep learning model combines the advantages of deep residual networks and dense networks, which can reduce the number of training process parameters to improve calculation accuracy as well as enhance the flow of information and gradients. The original RDN model was first used in image super resolution, so we need to restructure the network architecture for classification tasks through adjusted input image features and hyper parameters. Experimental results show that this model can achieve a top-1 average identification accuracy of 95% on the Tomato test dataset in AI Challenger 2018 datasets, which verifies its satisfactory performance. The restructured residual dense network model can obtain significant improvements over most of the state-of-the-art models in crop leaf identification, as well as requiring less computation to achieve high performance.

Climate change and disease in plant communities.

Climate change is triggering similar effects on the incidence and severity of disease for crops in agriculture and wild plants in natural communities. The complexity of natural ecosystems, however, generates a complex array of interactions between wild plants and pathogens in marked contrast to those generated in the structural and species simplicity of most agricultural crops. Understanding the different impacts of climate change on agricultural and natural ecosystems requires accounting for the specific interactions between an individual pathogen and its host(s) and their subsequent effects on the interplay between the host and other species in the community. Ultimately, progress will require looking past short-term fluctuations to multiyear trends to understand the nature and extent of plant and pathogen evolutionary adaptation and determine the fate of plants under future climate change.

Automatic Estimation of Crop Disease Severity Levels Based on Vegetation Index Normalization

The timely monitoring of crop disease development is very important for precision agriculture applications. Remote sensing-based vegetation indices (VIs) can be good indicators of crop disease severity, but current methods are mainly dependent on manual ground survey results. Based on VI normalization, an automated crop disease severity grading method without the use of ground surveys was proposed in this study. This technique was applied to two cotton fields infested with different levels of cotton root rot in south Texas in the United States, where airborne hyperspectral imagery was collected. Six typical VIs were calculated from the hyperspectral imagery and their histograms indicated that VI normalization could eliminate the influences of variable field conditions and the VI value range variations, allowing a potentially broader scope of application. According to the analysis of the obtained results from the spectral dimension, spatial dimension and descriptive statistics, the disease grading results were in general agreement with previous ground survey results, proving the validity of the disease severity grading method. Although satisfactory results could be achieved from different types of VI, there is still room for further improvement through the exploration of more VIs. With the advantages of independence of ground surveys and potential universal applicability, the newly proposed crop disease grading method will be of great significance for crop disease monitoring over large geographical areas.

Developing risk indicator system of non-compliance for organic crop farms based on China organic regulations

Abstract Farming management and certification are essential for organic agriculture development to make sure that farming practices are compliant with organic regulations. To improve the efficiency of organic certification and farm management, a risk-based indicator system of organic crop production was established according to literature review and Chinese organic regulations. Three dimensions, 11 themes, and 25 indicators were selected and the weights of which were determined through Analytic Hierarchy Process. The highest weight was assigned to the production dimension (0.59), followed by management (0.24) and environment (0.17). The three highest risk themes in the sequence were plant protection, detection and soil fertility management with a weight of 0.17, 0.15 and 0.12, respectively. At the indicator level, pesticide detection rate, nutrient satisfaction rate, the proportion of non-chemical treatment, the severity of crop diseases, pests and weeds, and the quality of soil environment ranked top five according to the weight of their risk. Chemicals application including pesticides and fertilizers was the main concern in organic production and certification. The results will provide producers, inspectors, and certifiers useful references to reduce the risk of non-compliance, and increase the integrity and credibility during organic production and certification.

Image processing based real-time variable-rate chemical spraying system for disease control in paddy crop

The agrochemical application with conventional sprayers results in wastage of applied chemicals, which not only increases the economic losses but also pollutes the environment. In order to overcome these drawbacks, an image processing based real-time variable-rate chemical spraying system was developed for the precise application of agrochemicals in diseased paddy crop based on crop disease severity information. The developed system comprised of web cameras for image acquisition, laptop for image processing, microcontroller for controlling the system functioning, and solenoid valve assisted spraying nozzles. The chromatic aberration (CA) based image segmentation method was used to detect the diseased region of paddy plants. The system further calculated the disease severity level of paddy plants, based on which the solenoid valves remained on for a specific time duration so that the required amount of agrochemical could be sprayed on the diseased paddy plants. Field performance of developed sprayer prototype was evaluated in the variable-rate application (VRA) and constant-rate application (CRA) modes. The field testing results showed a minimum 33.88% reduction in applied chemical while operating in the VRA mode as compared with the CRA mode. Hence, the developed system appears promising and could be used extensively to reduce the cost of pest management as well as to control environmental pollution due to such agrochemicals.