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Currently, the main methods for detecting plant diseases are sampling and manual visual inspection. However, these methods are time-consuming, laborious and prone to misinterpretation. Rapid advances in Deep Learning (DL) techniques offer new possibilities. This study focused on analysing the confounding factors among three types of wheat rust (stripe rust, leaf rust and stem rust) and aimed to achieve higher classification accuracy. The following approaches were used: (1) Images were collected from several crops and diseases: Wheat Rusts Dataset (WRD), Wheat Common Disease Dataset (WDD), and Common Poaceae Disease Dataset (PDD); (2) Seven common convolutional neural network (CNN) models were made and their performance compared. DenseNet121 was selected as the base model, and its classification results further analysed. The results of the above analyses were then considered using phenotypic morphology and model structure analysis, as well as potential confounder discussions; (3) Adjustments and optimisations were made based on the identified confounding factors. The final improved model, designated Imp-DenseNet, achieved the following accuracies with different datasets: Top-1 accuracy = 98.32% (WRD), Top-3 accuracy = 97.30% (WDD) and Top-5 accuracy = 96.60% (PDD) (Top-x Accuracy refers to the accuracy of the top-ranked category that matches or containing the actual results). The study revealed the potential factors contributing to the confusion among the three wheat rusts and successfully achieved higher accuracy. It can provide a new perspective for future research on other diseases of wheat or other crops. [Display omitted] • Confusion factors of wheat rust were analysed from different perspectives • Combine knowledges of plant protection, plant phenotype morphology analysis, DL and CV • Remodel DenseNet using attention mechanism, SPP, and factorisation thoughts • Verify validity of methods with Grad-CAM, Yolo v8 and Transformer [ABSTRACT FROM AUTHOR]

Fusarium damage in wheat reduces the quality and safety of associated food and feed products. In this study, a specific Fusarium head blight (FHB) classification index (FCI) for detection of this disease in wheat is proposed. Hyperspectral microscopy images of wheat spikelets are used as the data source. An algorithm combining the instability index (ISI) and spectral angle mapper (SAM) classifier (ISI-SAM) is used to extract four sensitive single wavelengths. Then, partial least-squares regression of the disease area ratio with simple spectral vegetation indices (SVIs) for each image is used to determine the most relevant spectral index (i.e., difference spectral index, DSI (668, 417)). On this basis, in order to develop a hyperspectral index for FHB detection, an exhaustive search for the best weighted combination of a single wavelength and DSI (668, 417) is conducted, with all possible combinations being tested. The final FCI is F C I = 0.25 × [ 2 (R 668 − R 417) − R 539 ] , with an overall classification accuracy of 89.80%. The FCI is tested for its ability to detect and classify the healthy and diseased areas of wheat spikelets through comparison with six commonly used SVIs, and its disease identification accuracy is almost 30% higher than that of the best-performing SVI. The FCI is also successfully applied to classification of hyperspectral image data from wheat spikes. The FCI developed in this study constitutes a stable and feasible method of early FHB detection through spectral and low-altitude remote sensing, and will improve FHB detection, identification, and monitoring performance in precision agriculture applications. Image 1 • Hyperspectral microscopic images used to develop Fusarium classification index FCI. • A joint algorithm, ISI-SAM, is proposed to select the optimum sensitive wavebands. • A diseased area ratio of each HMI image is provided to assess damage level. • FCI based on combination of optimal single waveband and difference spectral index. • The proposed FCI is effective for recognition of FHB infected wheat ears. [ABSTRACT FROM AUTHOR]

This study utilizes hyperspectral imaging technology to capture spectral images of three grades of green tea. Reflectance spectra of the three quality grades of tea, namely Grade A, Grade B, and Grade C, were collected between 370 nm and 1040 nm. Three methods, namely Savitzky-Golay (SG) smoothing, standard normal variable (SNV), and first derivative (FD), were employed to preprocess the raw spectra. Dimensionality reduction and visual display were accomplished using t-distributed stochastic neighbor embedding, and the preprocessed spectra with the most optimal visualization effect were selected to extract the key wavelengths using principal components analysis (PCA). The extracted key bands could aid in achieving the detection of tea quality grades, as tea polyphenols are more sensitive between 650 and 800 nm, where 664 nm and 765 nm represent the key wavelengths of catechins. K-nearest neighbor (KNN) and support vector machine (SVM) discriminative models were deployed to model the key wavelengths. The model built by FD-PCA-SVM exhibited the best discriminant effect, with an accuracy of 93.8 % in the training set and 98.2 % in the test set. FD-PCA-SVM discriminant model was used to identify and visualize the tea quality grades with good results. Hyperspectral imaging technology is well-suited to identifying the quality of green tea. [Display omitted] • Hyperspectral imaging achieved rapid non-destructive testing of tea quality. • The PCA can diminish the superfluity of spectral data, but ameliorate the accuracy. • The key wavelength of the biochemical compounds present in tea was investigated. • FD-PCA-SVM was used to identify and visualize the tea quality grades with good results. [ABSTRACT FROM AUTHOR]

Apple is one of the most important cash crops in China. However, negative economic, environmental and social impacts are associated with its production. This study aims to apply a holistic systems perspective to understand existing problems associated with apple production in China and use this information to improve its sustainability. A structured survey was administered to farmers (n = 245) in Shandong and Shanxi provinces, combined with semi-structured interviews with apple supply chain stakeholders (n = 25). Themes, dimensions and relationships were identified based on an inductive thematic analysis of interview data, and then triangulated against the survey data. Interpretive Structural Modelling and Cross-Impact Matrix Multiplication Applied to Classification methods were applied to investigate interrelationships and effects of the elicited elements within the system. The results indicated that various environmental, economic and social problems are associated with apple production in China, including environmental and health risks associated with synthetic input applications, yield instability, deterioration of apple quality, farmers' uncertainty about accessing routes to market, and the ageing farming workforce. The interaction of socio-economic and supply chain issues has contributed to the system "lock-in" to unsustainable practices within the apple production system. Existing agricultural policies were ineffective as they did not include policy leverage to mitigate the multiple factors driving lock-in to unsustainable practices within the system. The research has provided evidence to enable policymakers to develop effective and targeted strategies to facilitate sustainable production within the apple production system. In particular, the future policy mix should consider the entirety of the food system including perspectives and requirements of different stakeholders. The three-stage approach applied has demonstrated its feasibility of investigating sustainability issues facing a particular industry within a specific cultural and policy context. [Display omitted] • 21 barriers to Chinese sustainable apple production were identified. • Vicious circles trap different problems in the apple production system. • There exists socio-technical lock-in around existing tools and practices. • Ineffective agricultural policies strongly hinder upgrading apple production. • A three-stage approach was established for researching agricultural sustainability. [ABSTRACT FROM AUTHOR]

Apple is one of the most important cash crops in China. However, negative economic, environmental and social impacts are associated with its production. This study aims to apply a holistic systems perspective to understand existing problems associated with apple production in China and use this information to improve its sustainability. A structured survey was administered to farmers (n = 245) in Shandong and Shanxi provinces, combined with semi-structured interviews with apple supply chain stakeholders (n = 25). Themes, dimensions and relationships were identified based on an inductive thematic analysis of interview data, and then triangulated against the survey data. Interpretive Structural Modelling and Cross-Impact Matrix Multiplication Applied to Classification methods were applied to investigate interrelationships and effects of the elicited elements within the system. The results indicated that various environmental, economic and social problems are associated with apple production in China, including environmental and health risks associated with synthetic input applications, yield instability, deterioration of apple quality, farmers' uncertainty about accessing routes to market, and the ageing farming workforce. The interaction of socio-economic and supply chain issues has contributed to the system "lock-in" to unsustainable practices within the apple production system. Existing agricultural policies were ineffective as they did not include policy leverage to mitigate the multiple factors driving lock-in to unsustainable practices within the system. The research has provided evidence to enable policymakers to develop effective and targeted strategies to facilitate sustainable production within the apple production system. In particular, the future policy mix should consider the entirety of the food system including perspectives and requirements of different stakeholders. The three-stage approach applied has demonstrated its feasibility of investigating sustainability issues facing a particular industry within a specific cultural and policy context. [Display omitted] • 21 barriers to Chinese sustainable apple production were identified. • Vicious circles trap different problems in the apple production system. • There exists socio-technical lock-in around existing tools and practices. • Ineffective agricultural policies strongly hinder upgrading apple production. • A three-stage approach was established for researching agricultural sustainability. [ABSTRACT FROM AUTHOR]