Apples are one of the most widely planted fruits in the world, with an extremely high annual production. Several issues should be addressed to avoid the damaging of samples during the quality grading process of apples (e.g., the long detection period and the inability to detect the internal quality of apples). In this study, an electronic nose (e-nose) detection system for apple quality grading based on the K-nearest neighbor support vector machine (KNN-SVM) was designed, and the nasal cavity structure of the e-nose was optimized by computational fluid dynamics (CFD) simulation. A KNN-SVM classifier was also proposed to overcome the shortcomings of the traditional SVMs. The performance of the developed device was experimentally verified in the following steps. The apples were divided into three groups according to their external and internal quality. The e-nose data were pre-processed before features extraction, and then Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used to reduce the dimension of the datasets. The recognition accuracy of the PCA–KNN-SVM classifier was 96.45%, and the LDA–KNN-SVM classifier achieved 97.78%. Compared with other commonly used classifiers, (traditional KNN, SVM, Decision Tree, and Random Forest), KNN-SVM is more efficient in terms of training time and accuracy of classification. Generally, the apple grading system can be used to evaluate the quality of apples during storage.

Sensors have been used in various agricultural production scenarios due to significant advances in the Agricultural Internet of Things (Ag-IoT), leading to smart agriculture. Intelligent control or monitoring systems rely heavily on trustworthy sensor systems. Nonetheless, sensor failures are likely due to various factors, including key equipment malfunction or human error. A faulty sensor can produce corrupted measurements, resulting in incorrect decisions. Early detection of potential faults is crucial, and fault diagnosis techniques have been proposed. The purpose of sensor fault diagnosis is to detect faulty data in the sensor and recover or isolate the faulty sensors so that the sensor can finally provide correct data to the user. Current fault diagnosis technologies are based mainly on statistical models, artificial intelligence, deep learning, etc. The further development of fault diagnosis technology is also conducive to reducing the loss caused by sensor failures.

The air environment (e.g., high concentration of carbon dioxide) in a pig house will affect the health conditions and growth performance of the pigs, and the quality of pork as well. In order to reduce the cumulative concentration of carbon dioxide in the pig house, the prediction model was established by the deep learning method to predict the changes of the carbon dioxide cumulative concentration in a pig house. This model will also be used for the real-time monitoring and adjustment of the concentration of carbon dioxide of the pig house. The experiment was designed to collect environmental parameters (e.g., temperature, humidity, wind speed, and carbon dioxide concentration) data in the pig house for several months. The ensemble empirical mode decomposition–gated recurrent unit (EEMD–GRU) prediction model was established in the prediction of carbon dioxide concentration in the pig house. The results show that compared with the other models, the prediction accuracy of the EEMD–GRU model is the highest, and the root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and r-squared (R2) of carbon dioxide concentration in autumn and winter are 123.2 ppm, 88.3 ppm, 3.2%, and 0.99, respectively. The RMSE, MAE, MAPE, and R2 for carbon dioxide concentration are 129.1 ppm, 93.2 ppm, 5.9%, and 0.76 in spring and summer. The prediction model proposed in this paper can effectively predict the concentration of carbon dioxide in the pig house and provide effective help for the precise control of the pig house environment.

The heat stress response of broilers will adversely affect the large-scale and welfare of the breeding of broilers. In order to detect the heat stress state of broilers in time, make reasonable adjustments, and reduce losses, this paper proposed an improved Cascade R-CNN (Region-based Convolutional Neural Networks) model based on visual technology to identify the behavior of yellow-feathered broilers. The improvement of the model solved the problem of the behavior recognition not being accurate enough when broilers were gathered. The influence of different iterations on the model recognition effect was compared, and the optimal model was selected. The final average accuracy reached 88.4%. The behavioral image data with temperature and humidity data were combined, and the heat stress evaluation model was optimized using the PLSR (partial least squares regression) method. The behavior recognition results and optimization equations were verified, and the test accuracy reached 85.8%. This proves the feasibility of the heat stress evaluation optimization equation, which can be used for reasonably regulating the broiler chamber.

Broiler behavior is closely related to the breeding environment. Therefore, studying broiler behavior helps breeding farm workers to better carry out welfare breeding. In the breeding environment of yellow feather broilers, temperature, humidity, and ammonia concentration are the main factors that affect the behavior of the broilers. This study used a multichromatic aberration model to process the color images of yellow feather broilers to extract the activity feature of the broilers at different periods, utilized the Cb component of YCbCr color model and the b component of Lab color model to remove background litter in images, and employed the Q component of YIQ color model to remove the feeders and the drinkers from the image. The segmented images were constructed into an accumulator to generate a heat map of yellow feather broilers’ activity. Then, the correlation between the activity and the temperature and humidity index (THI) and the correlation between the activity and ammonia concentration were explored. The experiment found that the activity of the broilers was significantly positively correlated with ammonia concentration ( P < 0.05 ), indicating that the activity of yellow feather broilers increased with ammonia concentration ascending. Besides, the THI in the broiler chamber had a significant positive correlation with the ammonia data ( P < 0.01 ), indicating that when the THI in the broiler chamber increases, the ammonia concentration rises. The research provides a direction for exploring the impact of THI and ammonia concentration on the performance of yellow feather broilers. At the same time, it provides a theoretical basis for the early warning and judgment of broiler breeding by farm workers in the future.