Potato is the world’s fourth-largest food crop, following rice, wheat, and maize. Unlike other crops, it is a typical root crop with a special growth cycle pattern and underground tubers, which makes it harder to track the progress of potatoes and to provide automated crop management. The classification of growth stages has great significance for right time management in the potato field. This paper aims to study how to classify the growth stage of potato crops accurately on the basis of spectroscopy technology. To develop a classification model that monitors the growth stage of potato crops, the field experiments were conducted at the tillering stage (S1), tuber formation stage (S2), tuber bulking stage (S3), and tuber maturation stage (S4), respectively. After spectral data pre-processing, the dynamic changes in chlorophyll content and spectral response during growth were analyzed. A classification model was then established using the support vector machine (SVM) algorithm based on spectral bands and the wavelet coefficients obtained from the continuous wavelet transform (CWT) of reflectance spectra. The spectral variables, which include sensitive spectral bands and feature wavelet coefficients, were optimized using three selection algorithms to improve the classification performance of the model. The selection algorithms include correlation analysis (CA), the successive projection algorithm (SPA), and the random frog (RF) algorithm. The model results were used to compare the performance of various methods. The CWT-SPA-SVM model exhibited excellent performance. The classification accuracies on the training set (A_train) and the test set (A_test) were respectively 100% and 97.37%, demonstrating the good classification capability of the model. The difference between the A_train and accuracy of cross-validation (A_cv) was 1%, which showed that the model has good stability. Therefore, the CWT-SPA-SVM model can be used to classify the growth stages of potato crops accurately. This study provides an important support method for the classification of growth stages in the potato field.

The dynamic interactions between soil, weather and crop management have considerable influences on crop yield within a region, and should be considered in optimizing nitrogen (N) management. The objectives of this study were to determine the influence of soil type, weather conditions and planting density on economic optimal N rate (EONR), and to evaluate the potential benefits of site-specific N management strategies for maize production. The experiments were conducted in two soil types (black and aeolian sandy soils) from 2015 to 2017, involving different N rates (0 to 300 kg ha⁻¹) with three planting densities (55,000, 70,000, and 85,000 plant ha⁻¹) in Northeast China. The results showed that the average EONR was higher in black soil (265 kg ha⁻¹) than in aeolian sandy soil (186 kg ha⁻¹). Conversely, EONR showed higher variability in aeolian sandy soil (coefficient of variation (CV) = 30%) than in black soil (CV = 10%) across different weather conditions and planting densities. Compared with farmer N rate (FNR), applying soil-specific EONR (SS-EONR), soil- and year-specific EONR (SYS-EONR) and soil-, year-, and planting density-specific EONR (SYDS-EONR) would significantly reduce N rate by 25%, 30% and 38%, increase net return (NR) by 155 $ ha⁻¹, 176 $ ha⁻¹, and 163 $ ha⁻¹, and improve N use efficiency (NUE) by 37–42%, 52%, and 67–71% across site-years, respectively. Compared with regional optimal N rate (RONR), applying SS-EONR, SYS-EONR and SYDS-EONR would significantly reduce N application rate by 6%, 12%, and 22%, while increasing NUE by 7–8%, 16–19% and 28–34% without significantly affecting yield or NR, respectively. It is concluded that soil-specific N management has the potential to improve maize NUE compared with both farmer practice and regional optimal N management in Northeast China, especially when each year’s weather condition and planting density information is also considered. More studies are needed to develop practical in-season soil (site)-specific N management strategies using crop sensing and modeling technologies to better account for soil, weather and planting density variation under diverse on-farm conditions.

Nitrogen (N) is one of the most essential nutrients that can significantly affect crop grain yield and quality. The implementation of proximal and remote sensing technologies in precision agriculture has provided new opportunities for non-destructive and real-time diagnosis of crop N status and precision N management. Notably, leaf fluorescence sensors have shown high potential in the accurate estimation of plant N status. However, most studies using leaf fluorescence sensors have mainly focused on the estimation of leaf N concentration (LNC) rather than plant N concentration (PNC). The objectives of this study were to (1) determine the relationship of maize (Zea mays L.) LNC and PNC, (2) evaluate the main factors influencing the variations of leaf fluorescence sensor parameters, and (3) establish a general model to estimate PNC directly across growth stages. A leaf fluorescence sensor, Dualex 4, was used to test maize leaves with three different positions across four growth stages in two fields with different soil types, planting densities, and N application rates in Northeast China in 2016 and 2017. The results indicated that the total leaf N concentration (TLNC) and PNC had a strong correlation (R² = 0.91 to 0.98) with the single leaf N concentration (SLNC). The TLNC and PNC were affected by maize growth stage and N application rate but not the soil type. When used in combination with the days after sowing (DAS) parameter, modified Dualex 4 indices showed strong relationships with TLNC and PNC across growth stages. Both modified chlorophyll concentration (mChl) and modified N balance index (mNBI) were reliable predictors of PNC. Good results could be achieved by using information obtained only from the newly fully expanded leaves before the tasseling stage (VT) and the leaves above panicle at the VT stage to estimate PNC. It is concluded that when used together with DAS, the leaf fluorescence sensor (Dualex 4) can be used to reliably estimate maize PNC across growth stages.

Remote sensing approaches have several advantages over traditional methods in determining information on physical and chemical parameters, including timely data acquisition, low costs, and wide coverage. Thus, remote sensing is widely used in crop growth monitoring. Unlike vertical observations, multi-angular remote sensing technology can obtain the vertical distribution information of the central and lower leaves of a crop. Furthermore, applications of remote sensing on the vertical distribution of maize canopy components is complicated, and related research is limited. In the current paper, we employed multi-angular spectral data, measured by a self-designed multi-angular observation instrument at view zenith angles (VZAs) of 0°, 10°, 20°, 30°, 40°, 50°, and 60°, to explore the monitoring strategy and monitoring precision of the vertical distribution of chlorophyll content in the maize canopy. This was then used to determine the optimal monitoring method for the chlorophyll content (soil and plant analyzer development (SPAD) value) of each layer. The correlation between SPAD value and chlorophyll sensitivity indices at different growth stages was used as the basis for screening indices and VZAs. The correlation between the selected EPI (eucalyptus pigment index) and REIP (red edge inflection point) indices and chlorophyll content indicated view zenith angles (VZAs) of 0°, 30°, and 40° as optimal for the early growth stage monitoring of chlorophyll content in the 1st, 2nd, and 3rd layers, respectively. These values were associated with RMSEs of 4.14, 1.71, and 1.11 for EPI, respectively; and 4.61, 2.31, and 1.00 for REIP, respectively. In addition, a VZA of 50° was selected to monitor the chlorophyll content of the 1st, 2nd, 3rd, and 4th layers at the late growth stage, with RMSE values of 2.97, 3.50, 2.80, and 4.80 for EPI, respectively; and 3.16, 5.02, 4.55, and 7.85 for REIP, respectively. The results demonstrated the ability of canopy multi-angular spectral reflectance to accurately estimate the maize canopy chlorophyll content vertical distribution, with the VZAs of different vertical layers varying between the early and late growth stages.

Conducting suitability assessment for sugarcane cultivation is of great significance for optimizing the sugarcane cultivation structure and industrial layout. In this paper, based on the requirements of sugarcane growth and development on climate, terrain, and other environmental conditions, as well as the influence of natural disasters, a total of 11 specific indicators in terms of climate factor, terrain factor, and disaster factor were selected to construct a sugarcane cultivation suitability assessment system based on the analytic hierarchy process (AHP). Then, using Guangxi Province, China, as an example, a suitability assessment for sugarcane cultivation was conducted using multi-source data on climate, terrain, and hazards over the past 30 years. The results showed that among 11 indicators, including annual average temperature, elevation had the largest contribution rate, followed by precipitation during the period of ≥20 °C, slope, and the autumn drought frequency. From the spatial distribution, 37% of the provincial regions were suitable for sugarcane cultivation, mainly distributed in Chongzuo City, Nanning City, Qinzhou City, and Beihai City. In total, 44% of the provincial regions were moderately suitable for sugarcane cultivation, mainly distributed in Hezhou City, Laibin City, and Liuzhou City. Additionally, only 19% of the provincial regions were unsuitable for sugarcane cultivation, mainly distributed in Baise City, Hechi City, and Guilin City, with the terrain factor being the main influencing factor of sugarcane suitability assessment. In order to make reasonable use of land resources and increase sugarcane yield, it is suggested that sugarcane cultivation areas should be adjusted to the central and southern regions such as Chongzuo City, Nanning City, Beihai City, and Qinzhou City, and other industries should be developed in the northern regions which are not suitable for sugarcane cultivation.