The Internet of Things (IoT) has tremendous success in health care, smart city, industrial production and so on. Protected agriculture is one of the fields which has broad application prospects of ...IoT. Protected agriculture is a mode of highly efficient development of modern agriculture that uses artificial techniques to change climatic factors such as temperature, to create environmental conditions suitable for the growth of animals and plants. This review aims to gain insight into the state-of-the-art of IoT applications in protected agriculture and to identify the system structure and key technologies. Therefore, we completed a systematic literature review of IoT research and deployments in protected agriculture over the past 10 years and evaluated the contributions made by different academicians and organizations. Selected references were clustered into three application domains corresponding to plant management, animal farming and food/agricultural product supply traceability. Furthermore, we discussed the challenges along with future research prospects, to help new researchers of this domain understand the current research progress of IoT in protected agriculture and to propose more novel and innovative ideas in the future.
With the improvement of people's living standard, the fast-increase demand for agricultural products with high quality is endless. Therefore, developing accurate and rapid detection methods for ...agricultural products quality analyses is of great importance and strongly required. Hyperspectral imaging (HSI) technology, as a rapid and non-destructive method, has earned wide concern. In this review, the applications of HSI for agricultural products quality analyses have been proposed to prove the feasibility of this technology for disease detection, grading, classification, detection of chemical attributes and other aspects. The application status of HSI in quality analyses for each category of agricultural products was summarized in several different aspects, including the quality properties, the used pattern, the modeling methods and the detection accuracy. Finally, based on the above summary, the advantages and disadvantages of HSI have been discussed and some viewpoints on current research situation and research directions of future have also been given.
•Nondestructive test of peanut fat content was realized by Hyperspectral method.•PCA was used to detect outlier samples.•Comparing several modeling methods, Baseline-SPA-MLR obtained a very high ...accuracy.
Hyperspectral imaging technology combined with chemometrics were applied to detect fat content in peanut kernels. Four varieties of peanuts were scanned to acquire hyperspectral images by using a “push-broom” system. Then, the spectral data was extracted from hyperspectral images. Principal component analysis (PCA) was used to detect outliers. After outliers removed, five different pre-processing methods were used to preprocess spectral data. Successive projections algorithm (SPA) and regression coefficient (RC) were adopted to select effective wavelengths. Finally, based on the full wavelengths and the effective wavelengths, the models of partial least squares regression (PLSR), support vector machine regression (SVR) and multiple linear regression (MLR) were established respectively. Comparing these models, Baseline-SPA-MLR was the most excellent with determination coefficient (R2p) of 0.9736, root mean square errors (RMSEp) of 0.4635% and residual prediction deviation (RPD) of 6.1273 in the prediction set. All results in this study indicated that the combination of chemometrics and hyperspectral imaging technology provided an efficient and non-destructive method for detecting the fat content in peanut kernels.
In this work, a simple synthesis of low-toxicity transition metal material of WO3-x dots was used as a co-reactant with Au@SiO2 as a core-shell material and a signal amplification factor to ...collaboratively promote Ru(bpy)32+ electrochemiluminescence (ECL) for the construction of a highly sensitive aptasensor for the detection of diazinon (DZN) in vegetables. Electrodes modified with multi-walled carbon nanotubes-chitosan composite membranes (MWCNTs-CS) were used to load and immobilize more Ru(bpy)32+.can load more Ru(bpy)32+. WO3-x dots synthesized by a simple method showed excellent ECL efficiency as a novel co-reactant for Ru(bpy)32+. Under optimized conditions, this aptasensor for DZN has a wide detection range (10 pg mL−1 - 1 μg mL−1.) and a low detection limit (0.0197 ng L−1). The aptasensor has shown good results in the analysis of real samples in the experiment. This work provides a new approach to the construction of a novel electrochemiluminescence sensor for the detection of pesticides.
•A simple method for synthesizing WO3-x dots.•WO3-x dots was used as a novel Ru(bpy)32+ co-reactants for detecting DZN.•Using WO3-x dots and Au@SiO2 collaborative promotion of ECL detection for DZN.•This work provides a new idea for the new ECL detection system of pesticides.
In this study, an innovative dual-aspect attention spatial-spectral transformer (DAASST) was introduced to advance postharvest quality control by the detection of Aspergillus flavus contamination and ...the accurate identification of contamination times in peanut kernels. The critical importance of maintaining postharvest quality and safety in nuts was recognized, with hyperspectral imaging technology being leveraged due to its great potential in non-destructive testing and quality assessment of nuts. At the heart of DAASST's innovation, an enhanced transformer architecture that incorporated an attention fusion mechanism was employed for the effective integration of the extracted features. This sophisticated integration not only improved the model’s performance but also was significantly surpassed by the capabilities of traditional machine learning methods in the context of postharvest biology and technology. Exceptional accuracy was demonstrated in testing, with 99.40% achieved in detecting Aspergillus flavus contamination and a remarkable 100% in distinguishing between different contamination times. Significant contributions to the field of postharvest biology and technology were made by merging cutting-edge feature extraction techniques, attention mechanisms, and transformer architecture to refine hyperspectral image analysis for postharvest quality control. The proven effectiveness of the DAASST in accurately detecting Aspergillus flavus and determining contamination times in peanut kernels highlighted its potential as a valuable tool for ensuring the safety and quality of postharvest nuts.
•Integration of a 3D-CNN and a 2D-CNN optimally captured spatial-spectral features.•Laplacian-weighted feature tokenizer and GCN feature extractor improved features.•A novel dual-aspect attention spatial-spectral transformer (DAASST) was proposed.•DAASST detected Aspergillus flavus and identified contamination times in peanut kernels.
In this work, a simple synthesis of low-toxicity transition metal material of WO
dots was used as a co-reactant with Au@SiO
as a core-shell material and a signal amplification factor to ...collaboratively promote Ru(bpy)
electrochemiluminescence (ECL) for the construction of a highly sensitive aptasensor for the detection of diazinon (DZN) in vegetables. Electrodes modified with multi-walled carbon nanotubes-chitosan composite membranes (MWCNTs-CS) were used to load and immobilize more Ru(bpy)
.can load more Ru(bpy)
. WO
dots synthesized by a simple method showed excellent ECL efficiency as a novel co-reactant for Ru(bpy)
. Under optimized conditions, this aptasensor for DZN has a wide detection range (10 pg mL
- 1 μg mL
.) and a low detection limit (0.0197 ng L
). The aptasensor has shown good results in the analysis of real samples in the experiment. This work provides a new approach to the construction of a novel electrochemiluminescence sensor for the detection of pesticides.
Hyperspectral imaging technology at 416–1000 nm was investigated to detect moisture content in peanut kernels. Four varieties of peanuts were scanned using a “push‐broom” system to acquire ...hyperspectral images. In this study, three models including partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVR) were established to detect moisture content in peanut kernels based on full wavelengths. The performance of SVR was the best with determination coefficient (R2) of .9432, root mean square errors (RMSE) of 0.7054%, and residual prediction deviation (RPD) of 3.9694 for prediction set. In order to simplify modeling process and improve calculation speed of the models, successive projections algorithm (SPA) and regression coefficient were applied for optimal wavelengths selection. Then, PCR, PLSR, and SVR models were established based on these selected wavelengths, respectively. As a result, SPA–SVR generated a satisfied effect with R2 of .9363, RMSE of 0.7021%, and RPD of 3.988 for prediction set. All results in this study indicated that the combination of chemometrics and hyperspectral imaging technology could achieve rapid and nondestructive detection of moisture content in peanut kernels.
Practical applications
The quality of peanut has a direct relationship with the moisture content in peanut. If moisture content in peanut is over standard, the peanut storage time will be shorter and also easy to be bad. It is harmful to eat this kind of peanut. Traditional methods for detection of peanut moisture content are tedious, time‐consuming, and even greatly influenced by subjective factors. As an emerging technique, hyperspectral imaging technology can detect moisture content in peanut kernels rapidly, accurately, and nondestructive. In this study, we generated a best prediction model, successive projections algorithm–support vector machine regression, which was established based on several characteristic wavelengths. All these results provided a theoretical basis for the detection of portable moisture content in peanut kernels.
The extraction and classification of different kinds of defects can achieve a more sophisticated classification of apples. In this study, a region of interest (ROI) extraction algorithm based on ...background separation, brightness correction, and the global threshold was designed for the extraction of apple decay and bruise under uneven light conditions. After brightness correction, we calculate the average gray value of the apple region and multiply the value by 0.8 as the threshold. In this way, the threshold of different apple images can be determined automatically without a manual setting. After extracting ROI, we realized the classification of the two kinds of defects by extracting texture features. Based on the selected texture features, the support vector machine discriminant models were established. The accuracy of the model based on angular second moment was 94.8% and the model based on entropy was 94.7%. The total time from ROI extraction to classification was approximately 1.2 s. The results showed that this extraction and classification process had application prospects in practical detection classification.
Practical Applications
Ungraded fruits were not easy to store, transport, or sell at a price based on quality. The classification was directly related to fruit packaging, transportation, storage and sales of the results, and benefits, so it was very important to carry out fruit classification. Machine vision technology can achieve objective and nondestructive rapid detection. Many studies had achieved extraction of defective parts. However, they directly classified apples with defects into one category without further classification of types of the defects. In many classification standards, such as GBT10651‐2008, they need to classify apples more finely according to the defect types. This study achieved extraction and classification of different kinds of apple defects by machine vision technology and achieved a satisfactory accuracy and detection speed, which provided theoretical support for the realization of strict and fine classification of apple in industrial applications in the future.
Texture feature was useful for discriminating bruise and decay of apples.
More detailed grading of apple quality.
Decay and bruise of apples were distinguished with more than 94% accuracy.
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