Establishing an accurate, fast, and operable method for diagnosing crop nutrition is very important for crop nutrient management. In this study, static scanning technology was used to collect images of a rice sample's fully expanded top three leaves and corresponding sheathes. From these images, 32 spectral and shape characteristic parameters were extracted using an RGB mean value function and using the Regionprops function in MATLAB. Hierarchical identification was used to identify NPK deficiencies. First, the normal samples and non-normal (NPK deficiencies) samples were identified. Then, N deficiency and PK deficiencies were identified. Finally, P deficiency and K deficiency were identified. In the identification of every hierarchy, SVFS was used to select the optimal characteristic set for different deficiencies in a targeted manner, and Fisher discriminant analysis was used to build the diagnosis model. In the first hierarchy, the selected characteristics were the leaf sheath R, leaf sheath G, leaf sheath B, leaf sheath length, leaf tip R, leaf tip G, leaf area and leaf G. In the second hierarchy, the selected characteristics were the leaf sheath G, leaf sheath B, white region of the leaf sheath, leaf B, and leaf G. In the third hierarchy the selected characteristics were the leaf G, leaf sheath length, leaf area/leaf length, leaf tip G, difference between the 2^nd and 3^rd leaf lengths, leaf sheath G, and leaf lightness. The results showed that the overall identification accuracies of NPK deficiencies were 86.15, 87.69, 90.00 and 89.23% for the four growth stages. Data from multiple years were used for validation, and the identification accuracies were 83.08, 83.08, 89.23 and 90.77%.

Non-destructive nutrition diagnosis provides effective technological support for agricultural sustainability. According to the plant nutrition mechanism, leaf characteristics displays different changing trends under nitrogen (N), phosphorus (P), and potassium (K) nutrition stress. In this study, the dynamic capture of rice leaf by scanning was used to research the changing regulation of leaf characteristics under nutrition stress. The leaf characteristics were extracted by mean value and regionprops functions in MATLAB, and the leaf dynamics were quantified by calculating the relative growth rate. Stepwise discriminant analysis and leave one out cross validation were applied to identify NPK deficiencies. The results indicated that leaves with N deficiency presented the lowest extension rate and the fastest wilt rate, followed by P and K deficiencies. During the identification, both morphological and color indices of the first incomplete leaf were effective indices for identification, but for the third fully expanded leaf, they were mainly color indices. Moreover, the first incomplete leaf had comparative advantage in early diagnosis (training accuracy 73.7%, validation accuracy 71.4% at the 26th day after transplantation), and the third fully expanded leaf generated higher accuracy at later stage. Overall, dynamic analysis expanded the application of leaf characteristics in identification, which contributes to improving the diagnostic effect.

This paper, examining the Pilot Free Trade Zone Lin-Gang Special Area in China (Shanghai), identifies the relationship between urban expansion and habitat change and analyzes the influence mechanism of habitat quality (HQ) on spatiotemporal distribution. The results show the following: (1) From 2002 to 2019, the HQ in the study area decreased significantly, and the spatial differences gradually expanded over time. The HQ was low in the southwest and high in the northeast, and low-level habitats gradually moved to the southwest. This spatiotemporal evolutionary law was consistent with the local government’s 2003–2020 plans, which are composed of the joint development of a logistics park in the north, the Lin-Gang industrial zone in the west, and Shanghai port in the south. (2) Due to the interspersed distribution of high and low habitats caused by urban development and expansion, Moran’s index in spatial autocorrelation decreased over time, which means the spatial agglomeration of HQ decreased and that homogeneity increased. (3) The spatial distribution of HQ was quantified by landscape analysis. The results showed that the fragmentation degree in high-level habitat areas increased with time, while the middle-level habitat areas first increased and then decreased, and the low-level habitat areas displayed the opposite change in trend to that of the middle habitat.

Machine vision technology enables the continuous and nondestructive monitoring of leaf responses to different nutrient supplies and thereby contributes to the improvement of diagnostic effects. In this study, we analysed the temporal dynamics of rice leaf morphology and colour under different nitrogen (N), phosphorus (P), and potassium (K) treatments by continuous imaging and further evaluated the effectiveness of dynamic characteristics for identification. The top four leaves (the 1st incomplete leaf and the top three fully expanded leaves) were scanned every three days, and all images were processed in MATLAB to extract the morphological and colour characteristics for dynamic analysis. Subsequently, the mean impact value was applied to evaluate the effectiveness of dynamic indices for identification. According to the results, higher nutrient supply resulted in a faster leaf extension rate and a lower developing rate of chlorosis, and the influence of N deficiency on leaf growth was the greatest, followed by P deficiency and then K deficiency. Furthermore, the optimal indices for identification were mainly calculated from morphological characteristics of the 1st incomplete leaf and colour characteristics of the 3rd fully expanded leaf. Overall, dynamic analysis contributes not only to the exploration of the plant growth mechanism but also to the improvement of diagnostics.

Special symptoms could be observed on rice leaves when exposed to potassium deficiency, and these symptoms usually display differently under different potassium levels, which offer a foundation for rapid nutrition diagnosis. In this research study, two years of hydroponic experiments on rice (providing 5 levels of potassium nutrition from extremely short to normal) were carried out and the leaf images were acquired by optical scanning at four growth periods. To diagnose the potassium nutrition content, the special symptoms including the yellowish brown leaf margin and the necrotic spots were segmented and quantized by the object-oriented method from leaf images, and the 6 further spectral characteristics of leaf were extracted by the image color analyzing function of MATLAB software. Based on the relationship between potassium content and leaf characteristics, the G value (average value of G channel in the RGB color model) calculated from the entire leaf and leaf tip, the area of yellowish leaf margin, and the number of necrotic spots were applied in the establishment of the identification model of potassium stress by using the support vector machine (SVM). The results indicated that the overall identification accuracies of rice potassium nutrition contents were 90%, 94%, 94%, and 96% at four different growth periods (productive tillering stage, invalid tillering stage, jointing stage, and booting stage), respectively. The data obtained from another year were used to validate the model, and the identification accuracies were 94%, 78%, 80%, and 84%, respectively. Generally speaking, the extraction of the specific symptoms by using object-oriented segmentation is an extension of machine vision technology in diagnosing potassium deficiency, and its application in diagnosing plant nutrition is valuable for the quantization of effective characteristics and improvement of identification accuracy.