VERY few veterinary surgeons have thought fit to write a book on diseases of poultry. Mr. Ernest Gray has done justice to the subject and is to be congratulated on his effort. A book of this size, written by one with specialized knowledge, will add to the value of any library or private bookshelf. Diseases of Poultry Their Aetiology, Diagnosis, Treatment and Control; with a Section on the Normal Anatomy and Physiology of the Fowl. By Ernest Gray. (Lockwood's Agricultural and Horticultural Handbooks.) Pp. x + 198 + 16 plates. (London: Crosby Lockwood and Son, Ltd., 1940.) 9s. 6d. net.

Diseases of Poultry

Plant diseases affect the growth of their respective species, therefore their early identification is very important. Many Machine Learning (ML) models have been employed for the detection and classification of plant diseases but, after the advancements in a subset of ML, that is, Deep Learning (DL), this area of research appears to have great potential in terms of increased accuracy. Many developed/modified DL architectures are implemented along with several visualization techniques to detect and classify the symptoms of plant diseases. Moreover, several performance metrics are used for the evaluation of these architectures/techniques. This review provides a comprehensive explanation of DL models used to visualize various plant diseases. In addition, some research gaps are identified from which to obtain greater transparency for detecting diseases in plants, even before their symptoms appear clearly.

https://www.mdpi.com/2223-7747/8/11/468/pdf

Plant Disease Detection and Classification by Deep Learning.

Deep learning is a branch of artificial intelligence. In recent years, with the advantages of automatic learning and feature extraction, it has been widely concerned by academic and industrial circles. It has been widely used in image and video processing, voice processing, and natural language processing. At the same time, it has also become a research hotspot in the field of agricultural plant protection, such as plant disease recognition and pest range assessment, etc. The application of deep learning in plant disease recognition can avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature extraction more objective, and improve the research efficiency and technology transformation speed. This review provides the research progress of deep learning technology in the field of crop leaf disease identification in recent years. In this paper, we present the current trends and challenges for the detection of plant leaf disease using deep learning and advanced imaging techniques. We hope that this work will be a valuable resource for researchers who study the detection of plant diseases and insect pests. At the same time, we also discussed some of the current challenges and problems that need to be resolved.

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Plant Disease Detection and Classification by Deep Learning—A Review

Vibrational spectroscopy techniques provide a powerful approach to the study of environmental materials and processes. These multifunctional analytical tools can be used to probe molecular vibrations of solid, liquid, and gaseous samples for characterizing materials, elucidating reaction mechanisms, and examining kinetic processes. Although Fourier transform infrared (FTIR) spectroscopy is the most prominent type of vibrational spectroscopy used in the field of soil science, applications of Raman spectroscopy to study environmental samples continue to increase. The ability of FTIR and Raman spectroscopies to provide complementary information for organic and inorganic materials makes them ideal approaches for soil science research. In addition, the ability to conduct in situ, real time, vibrational spectroscopy experiments to probe biogeochemical processes at mineral interfaces offers unique and versatile methodologies for revealing a myriad of soil chemical phenomena. This review provides a comprehensive overview of vibrational spectroscopy techniques and highlights many of the applications of their use in soil chemistry research.

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Soil Chemical Insights Provided through Vibrational Spectroscopy

Fruit classification is quite difficult because of the various categories and similar shapes and features of fruit. In this work, we proposed two novel machine-learning based classification methods. The developed system consists of wavelet entropy (WE), principal component analysis (PCA), feedforward neural network (FNN) trained by fitness-scaled chaotic artificial bee colony (FSCABC) and biogeography-based optimization (BBO), respectively. The K-fold stratified cross validation (SCV) was utilized for statistical analysis. The classification performance for 1653 fruit images from 18 categories showed that the proposed “WE + PCA + FSCABC-FNN” and “WE + PCA + BBO-FNN” methods achieve the same accuracy of 89.5%, higher than state-of-the-art approaches: “(CH + MP + US) + PCA + GA-FNN ” of 84.8%, “(CH + MP + US) + PCA + PSO-FNN” of 87.9%, “(CH + MP + US) + PCA + ABC-FNN” of 85.4%, “(CH + MP + US) + PCA + kSVM” of 88.2%, and “(CH + MP + US) + PCA + FSCABC-FNN” of 89.1%. Besides, our methods used only 12 features, less than the number of features used by other methods. Therefore, the proposed methods are effective for fruit classification.

Fruit Classification by Wavelet-Entropy and Feedforward Neural Network Trained by Fitness-Scaled Chaotic ABC and Biogeography-Based Optimization

A review on plant high-throughput phenotyping traits using UAV-based sensors

Hyperspectral imaging for classification of healthy and gray mold diseased tomato leaves with different infection severities

In many regions of the world, wheat is vulnerable to severe yield and quality losses from the fungus disease of Fusarium head blight (FHB). The development of resistant cultivars is one means of ameliorating the devastating effects of this disease, but the breeding process requires the evaluation of hundreds of lines each year for reaction to the disease. These field evaluations are laborious, expensive, time-consuming, and are prone to rater error. A phenotyping cart that can quickly capture images of the spikes of wheat lines and their level of FHB infection would greatly benefit wheat breeding programs. In this study, mask region convolutional neural network (Mask-RCNN) allowed for reliable identification of the symptom location and the disease severity of wheat spikes. Within a wheat line planted in the field, color images of individual wheat spikes and their corresponding diseased areas were labeled and segmented into sub-images. Images with annotated spikes and sub-images of individual spikes with labeled diseased areas were used as ground truth data to train Mask-RCNN models for automatic image segmentation of wheat spikes and FHB diseased areas, respectively. The feature pyramid network (FPN) based on ResNet-101 network was used as the backbone of Mask-RCNN for constructing the feature pyramid and extracting features. After generating mask images of wheat spikes from full-size images, Mask-RCNN was performed to predict diseased areas on each individual spike. This protocol enabled the rapid recognition of wheat spikes and diseased areas with the detection rates of 77.76% and 98.81%, respectively. The prediction accuracy of 77.19% was achieved by calculating the ratio of the wheat FHB severity value of prediction over ground truth. This study demonstrates the feasibility of rapidly determining levels of FHB in wheat spikes, which will greatly facilitate the breeding of resistant cultivars.

https://www.mdpi.com/2072-4292/13/1/26/pdf

Automatic evaluation of wheat resistance to fusarium head blight using dual mask-rcnn deep learning frameworks in computer vision

Fusarium head blight (FHB) is a devastating disease of wheat worldwide. In addition to reducing the yield of the crop, the causal pathogens also produce mycotoxins that can contaminate the grain. The development of resistant wheat varieties is one of the best ways to reduce the impact of FHB. To develop such varieties, breeders must expose germplasm lines to the pathogen in the field and assess the disease reaction. Phenotyping breeding materials for resistance to FHB is time-consuming, labor-intensive, and expensive when using conventional protocols. To develop a reliable and cost-effective high throughput phenotyping system for assessing FHB in the field, we focused on developing a method for processing color images of wheat spikes to accurately detect diseased areas using deep learning and image processing techniques. Color images of wheat spikes at the milk stage were collected in a shadow condition and processed to construct datasets, which were used to retrain a deep convolutional neural network model using transfer learning. Testing results showed that the model detected spikes very accurately in the images since the coefficient of determination for the number of spikes tallied by manual count and the model was 0.80. The model was assessed, and the mean average precision for the testing dataset was 0.9201. On the basis of the results for spike detection, a new color feature was applied to obtain the gray image of each spike and a modified region-growing algorithm was implemented to segment and detect the diseased areas of each spike. Results showed that the region growing algorithm performed better than the K-means and Otsu’s method in segmenting diseased areas. We demonstrated that deep learning techniques enable accurate detection of FHB in wheat based on color image analysis, and the proposed method can effectively detect spikes and diseased areas, which improves the efficiency of the FHB assessment in the field.

Detection of Fusarium Head Blight in Wheat Using a Deep Neural Network and Color Imaging

Cold damage is one of the disasters that cause significant loss and irreversible damage in crop production. To avoid yield loss, high-throughput phenotyping can be used to select the crop varieties with cold stress resistance. Nowadays, non-destructive spectral image analysis has become an effective way and is widely used in high-throughput phenotyping, which reflects the structural, physiological, biochemical characteristics and traits of plant structure and composition, plant growth and development processes and outcomes. This study used convolutional neural network (CNN) model to extract spectral features in the visible-near-infrared range to estimate cold damage of corn seedlings. The hyperspectral images of cold treated corn seedlings from five varieties were used as research objects in this study. The spectral range of the images was 450-885nm. Gaussian low-pass filter and the Savitzky-Golay smoothing method combined with the first-order derivative was used to do pre-processing for spectral data. For each corn variety, 3600 pixel samples obtained from the selected region of interests in each variety of corn seedlings were used for the CNN modeling. After the CNN modeling, 400 pixel samples extracted from the hyperspectral images were used as the testing set for each variety. Finally, a 10-layer knot CNN model was determined by analyzing the classification accuracy and computational efficiency. CNN detected the cold damage level of different types of corn seedlings as W22 (41.8 %), BxM (35%), B73 (25.6%), PH207 (20%), Mo17 (14%), which had high correlation with the ranking given by chemical method. The coefficient of correlation between cold damage detection results of CNN and results from chemical method is 0.8219. Therefore, it proves that spectral analysis based on CNN modeling can provide reference for detecting cold damage in corn seedlings.

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Ce Yang

Papers

A review on plant high-throughput phenotyping traits using UAV-based sensors

Hyperspectral imaging for classification of healthy and gray mold diseased tomato leaves with different infection severities

Automatic evaluation of wheat resistance to fusarium head blight using dual mask-rcnn deep learning frameworks in computer vision

Detection of Fusarium Head Blight in Wheat Using a Deep Neural Network and Color Imaging

Diagnosis of Plant Cold Damage Based on Hyperspectral Imaging and Convolutional Neural Network