Spikelet number per panicle (SNPP) is one of the most important yield components used to estimate rice yields. The use of high-throughput quantitative image analysis methods for understanding the diversity of the panicle has increased rapidly. However, it is difficult to simultaneously extract panicle branch and spikelet/grain information from images at the same resolution due to the different scales of these traits. To use a lower resolution and meet the accuracy requirement, we proposed an interdisciplinary method that integrated image analysis and a 5-point calibration model to rapidly estimate SNPP. First, a linear relationship model between the total length of the primary branch (TLPB) and the SNPP was established based on the physiological characteristics of the panicle. Second, the TLPB and area (the primary branch region) traits were rapidly extracted by developing image analysis algorithm. Finally, a 5-point calibration method was adopted to improve the universality of the model. The number of panicle samples that the error of the SNPP estimates was less than 10% was greater than 90% by the proposed method. The estimation accuracy was consistent with the accuracy determined using manual measurements. The proposed method uses available concepts and techniques for automated estimations of rice yield information.

In the rice-wheat system, using straw for soil incorporation provides better soil health and improves agricultural production. The experiment was performed in Babaiqiao town, Jiangsu Province, China’s Luhe District, Nanjing City, in June 2024 using a Shichao TG-500 tractor equipped with a Qingxuan 1GKN-180 rotary cultivator. The impacts of the three tillage practices, deep rotary tiller with straw (DRTS), shallow rotary tiller with straw (SRTS), and no-tillage with straw return (NTSR), on the level of soil disturbance were observed in the single-factor and two-factor interaction experiments. Based on the profilometry analysis, it was observed that DRTS had the highest value of soil disturbance while SRTS had a moderate disturbance value and NTSR minimized disturbance. The effects of working depths, forward speed, and rotation speed on the straw return rate have been evaluated by further investigations. The results showed that enhancing straw return rates was significantly impacted by changing the tilling depths and the rotation speeds, especially when using deeper tillage and moderate to high rotary speeds. The investigation found that the forward speed, blade rotation speed, and tillage depth explained the overall rates of straw return, soil breaking, and soil flatness. In the research, the response surface design employed was the Box–Behnken Design (BBD). The optimal operating parameters were 14.23 cm of plowing depth, 297.6 rpm for the rotary blades, and 3.23 km/h for forward speed. Achieved were the following parameters: 94.766% soil breakage rate, 84.97% straw return rates, and 16.36 mm soil flatness. The findings demonstrate the potential to implement strategies through operational parameters to significantly enhance agricultural practices.

Organic–inorganic compound fertilizer application technology is a key technology for chemical fertilizer efficiency improvement, and stable grain yield increase. However, current agricultural machinery is unable to achieve uniform application of both organic and inorganic fertilisers. This study has compared two modeling methods and optimally selected the EDEM-Fluent coupled method. It aims to investigate the mechanism by which four factors—namely inorganic fertilizer drop location (Polar angle: −80° to 80°, polar radius: 60 mm to 180 mm), organic fertilizer flow rate (875–3500 g·s⁻¹), inorganic fertilizer proportion (10–50%), and fertilizer spreading disc rotational speed (300–700 r·min⁻¹)—influence inorganic fertilizer uniformity. A Box–Behnken test was designed with the pole angle and pole diameter of the drop location, organic fertiliser flow rate, spreading disc rotational speed, and coefficient of variation in the uniformity of the inorganic fertilisers as indexes. The Box–Behnken test divided the fertiliser drop location into left and right parts and established a mathematical model of fertiliser drop location, rotational speed, and organic fertiliser flow rate. Finally, the predictive performance of the model was verified in the field by testing four scenarios: low speed–low flow rate, low speed–high flow rate, high speed–low flow rate, and high speed–high flow rate. The root mean square error (RMSE) between the EDEM-Fluent coupled test and the bench test is 1.53, which is better than the RMSE (2.55) between the EDEM test and the bench test. Before optimization, the coefficients of variationof inorganic fertilizer (ICV) under four operating conditions were 28.93%, 32.43%, 38.17%, and 29.32% respectively. After optimization, the corresponding values were 19.34%, 23.78%, 21.45%, and 23.10% respectively. Compared with the pre-optimization results, the organic fertilizer coefficient of variation (OCV) remained stable, while the inorganic fertilizer coefficient of variation (ICV) decreased by an average of 10.29%. This study greatly improved the uniformity of inorganic fertiliser in the organic–inorganic spreader and provides a basis for subsequent intelligent spreaders.

Maximum head rice yield is related to the optimal harvest time or moisture content during rice maturity. The physical properties of rice grains, such as the breaking forces, changed with the increasing degree of rice maturity. Harvest moisture content, breaking forces for the dorsal and ventral...

In order to measure wheat yield and wheat spike phenotypes, the grain number of wheat spikes is counted manually at present, but acquiring the grain number of wheat spikes is laborious and time-consuming. Counting the grain number of wheat spikes with an image processing method is promising, yet the application of this method is flawed due to its low accuracy. In this work, images of wheat spikes were collected and processed with technical procedures, including image cropping, image graying, histogram equalization, image binarization, eroding operation, removing small objects, filling image holes, revolving vertical spikes, cutting off stems, and removing stems. Wheat stems in binary images were eliminated by the sum pixels method, and the morphological characteristic parameters of the image areas of wheat spikes and lengths of wheat spike axes were calculated. Mathematical models relating the image areas of wheat spikes and lengths of the wheat spike axes to the grain number were established, and the mathematical models were verified. The results showed that the characteristic parameters of the image areas of wheat spikes and the lengths of the wheat spike axes for the spike images were linear relative to the grain number, and the maximum determination coefficients R² were 0.9336 and 0.9012, respectively. The maximum determination coefficients R² for the practical and predicted grain numbers were 0.9552 and 0.9369, respectively, and the minimum average absolute error was 2.3, while the average relative error for the mathematical models was 5.65%. The mathematical models relating the image areas of wheat spikes and the lengths of the wheat spike axes to the grain number were practical and accurate, and the mathematical model comparing the image area of wheat spikes and the grain number was superior to that comparing the length of the wheat spike axis and the grain number. The grain number of wheat spikes could be acquired accurately and quickly by the image processing method extracting the characteristic parameters of wheat spikes.

Physicochemical properties of wheat grains with largest kernel thickness always was lowest than the other sections, examination of microstructure of wheat grains can help us understand this phenomena. Two varieties of wheat, soft white winter wheat Yangmai 11 and hard white winter wheat Zhengmai 9023, were fractionated into five sections by kernel thickness. Then the fractionated wheat grains in 2.7–3.0 mm section were separated into three fractions by kernel specific density sequentially. Microstructure of the fractured surface were evaluated at different scale level to two varieties wheat with different kernel thickness and specific density by using environmental scanning electron microscopy. Compactness and size of endosperm cell tended to decrease with decreasing wheat kernel thickness and specific density. Protein matrix tended to increase with decreasing wheat kernel thickness and specific density. Size of starch granules and proportion for different type starch granules also varied with different wheat kernel thickness and specific density. Those microstructure properties of the fractured surface, formation of endosperm cells, protein matrix and starch granules were close related to rheological properties and pasting properties of wheat grains.