Plant stress is one of major issues that cause significant economic loss for growers. The labor-intensive conventional methods for identifying the stressed plants constrain their applications. To address this issue, rapid methods are in urgent needs. Developments of advanced sensing and machine learning techniques trigger revolutions for precision agriculture based on deep learning and big data. In this paper, we reviewed the latest deep learning approaches pertinent to the image analysis of crop stress diagnosis. We compiled the current sensor tools and deep learning principles involved in plant stress phenotyping. In addition, we reviewed a variety of deep learning applications/functions with plant stress imaging, including classification, object detection, and segmentation, of which are closely intertwined. Furthermore, we summarized and discussed the current challenges and future development avenues in plant phenotyping.

Maturity is a key attribute to evaluate the quality and acceptability of fruit products. In this study, the impact method was used for nondestructive measurement of kiwifruit maturity. The fruit was vertically dropped onto an impact plate, and an accelerometer was used to measure the response signal. Then, fruit firmness, soluble solid content (SSC), titratable acidity (TA), and sensory scores were measured to determine the kiwifruit maturity. In addition, different modeling methods were proposed for data analysis. The results showed that the optimized prediction results were obtained by the principal component analysis–back‐propagation neural network (PCA‐BPNN) method for both quantitative and qualitative analysis. The optimized correlation coefficient between prediction and actual values (r_p) and root mean square error of prediction (RESEP) for firmness, SSC, TA, and sensory score were 0.881 (2.359N), 0.641 (1.511 Brix), 0.568 (0.023%), and 0.935 (0.693), respectively. The optimized discriminant accuracy for immature, mature, and overmature kiwifruits was 94.2% and 92.1% for calibration and validation, respectively. Such results indicated the feasibility of the proposed impact method for kiwifruit maturity evaluation.

Monitoring of the quality change of cherry tomatoes during storage is very important for the quality control of cherry tomatoes. In this study, the soluble solids content (SSC), reducing sugars (RSs), titratable acids (TAs), ascorbic acid (AA) and lycopene of cherry tomatoes during storage at 0, 4, 10 or 25 °C were measured, and the kinetic models were established. The results showed that the zero-order reaction combined with the Arrhenius kinetic model could be used for the prediction of changes in SS, RS and AA content. The first-order reaction combined with the Arrhenius kinetic model could be used for the prediction of changes in the TA and lycopene content. The volatile compounds of cherry tomatoes were simultaneously determined by the gas chromatography–mass spectrometry (GC–MS) and electronic nose (E-nose). A total of 104 volatile compounds were identified by GC–MS. Orthogonal partial least squares discriminant analysis (OPLS-DA) showed that there were 13 different metabolites among cherry tomatoes with different freshness. The accuracies of Fisher’s models based on E-nose for discriminating freshness of cherry tomatoes stored at 0, 4, 10 and 25 °C were 96%, 100%, 92% and 90%, respectively. This study provides a theoretical basis for the quality control of cherry tomatoes during storage.