Development Of Intelligent Agriculture Research Articles

Water Fertilizer Integration Equipment Research Status and Development Trend

Integrated water and fertilizer technology can accurately adjust the mix of water and fertilizer on demand, and then through the irrigation system, water and fertilizer are delivered to the roots of the crop in quantity, on time, and with precision. As an important part of the integrated system of water and fertilizer, the fertilizer has a critical influence on the quality of the fertilizing system. Nowadays, the development of intelligent agriculture has become a general trend of agricultural development. By exploring the development process of water and fertilizer mixing, this paper sorted out the research status of various kinds of fertilizers in China, compared different mixing methods of water and fertilizer, and analyzed and summarized the excellent working performance of different fertilizers. Secondly, the current research emphases were discussed, including precision fertilization technology, intelligent control, comprehensive utilization of resources and circular agriculture. Then, the development trend of integrated water and fertilizer equipment is put forward, including the improvement of precision fertilization, the development of multifunctional integrated equipment, and the application of data management and decision support system. Through continuous technological innovation and application, the integrated equipment of water and fertilizer is expected to play a greater role in the agricultural field.

An accurate detection and segmentation model of obscured green fruits

Accurate detection and segmentation of obscured green fruits is a key factor in the efficiency of the harvesting robot. In the real and complex orchard environment, there are many influencing factors such as illumination changes, occlusion of branches and leaves, weather, etc., which bring difficulties to the work of picking robots. Therefore, higher requirements are put forward for accurate detection and segmentation algorithms of machine vision. An accurate detection and segmentation method for obscured green fruit based on a fully convolutional one-stage (FCOS) object detection model is proposed. First, the feature pyramid network (FPN) in FCOS is replaced by residual feature pyramid network (RFPN), which improves the detection accuracy of green fruits with different sizes. Next, as the boundaries of fuzzy fruits are often not accurately identified, a two-layer convolutional block attention network is connected to a modified FCOS to recover the edges of incomplete green fruits. The experimental results show that the detection and segmentation accuracy of green fruit reaches 81.2% and 85.3% on Apple dataset. On the Apple-ape dataset, the detection accuracy and segmentation accuracy reached 77.2% and 79.7%, respectively. The Params of the model is 39.7 M, and the floating point operations (FLOPs) are 169.8G, which has great advantages over other ten advanced models in terms of accuracy and complexity. The model also improves the working efficiency of orchard harvesting robots and lays the foundation for the further development of intelligent agriculture.

Intelligent Agriculture Technology Based on Internet of Things

Although the application of agricultural product traceability technology is a key point to realize Modern Agricultural IoT, it has still encountered various food safety problems. For example, immature environmental monitoring technology of agricultural products, weak product traceability and imperfect product monitoring equipment. For this reason, this paper studies and compares several emerging technologies of the things Internet, then it analyzes the functional diversity and practicability of the Modern Agricultural IoT. It builds the experimental environment based on the agricultural product traceability technology, so as to realize the monitoring of crop growth environment and traceability. The result plays a positive role in popularizing the traceability technology of agricultural products and has a certain impact on the development of intelligent agriculture. It has designed and developed a set of crop growth monitoring terminal equipment through experimental debugging. Then it could obtain temperature and humidity, illumination, CO2 concentration, soil data, and other crop growth environment parameters in real time. The relevant data has provided strong support for the traceability model to realize the multi-point monitoring, intelligent control, and automatic operation of crop growing environment. Then it can be sorted and analyzed through the application layer. This system can promote the intelligent processing, improve the utilization efficiency of agricultural resources, promote the development of modern agriculture, save agricultural production costs, and increase the production and marketing. It is an innovative application of the Internet of things technology applied to agricultural production.