A new artificial intelligence (AI) tool available for free in a smartphone app can predict near-term crop productivity for farmers in Africa. The app may help farmers in Africa protect their staple crops – such as maize, cassava and beans – in the face of climate warming, according to Penn State researchers, reports Science Daily. The new AI tool will work with the existing AI assistant developed by Penn State, called PlantVillage Nuru, that is being used across Africa to diagnose crop diseases. App twice as good as human experts The researchers have tested the performance of their machine-learning models with locally sourced smartphones in the typical high light and temperature settings of an African farm. In these tests, the app was shown to be twice as good as human experts at making accurate diagnoses, and it increased the ability of farmers to discover problems on their own farms. Now PlantVillage Nuru can draw in data from the United Nations’ WaPOR (Water Productivity through Open access of Remotely sensed derived data) portal, a database that integrates 10 years’ worth of satellite-derived data from NASA and computes relevant metrics for crop productivity given the available water. Weather forecast data PlantVillage Nuru also incorporates weather forecast data, a soil dataset for Africa, and the United Nations Crop Calendar, which is a series of algorithms on adaptive measures that can be taken under certain conditions. The PlantVillage AI tool incorporates tens of thousands of data points across Africa with hundreds more being collected every day. All of these data are freely available to the global community, who can work collectively to improve the AI. Drought tolerance of crops Specifically, the AI assistant has the opportunity to integrate diverse data streams to provide information about drought tolerance of crops and which crops are suitable in which areas, for example. In addition, the app offers advice that could help farmers learn about crop varieties that are climate-resilient, affordable irrigation methods, and flood mitigation and soil conservation strategies, among other best practices. Although the tool is smartphone based, it can be accessed through a webpage to inform diverse stakeholders. In Kenya, the PlantVillage AI tool informs messages that are then sent out to SMS phones across the country.
Online grocery retailer Ocado Group has completed its deal to partner and invest in Infinite Acres. Infinite Acres is a partnership of three companies working together to scale indoor food production through vertical farming done in environmentally controlled, pesticide-free facilities. The joint venture was first announced in June and since has been engaged in several international projects. Priva Holding and 80 Acres Farms Along with UK-based Ocado, the Infinite Acres partnership includes Netherlands-based Priva Holding BV, a provider of technology solutions, services and automation systems to horticultural and other industries; and U.S.-based 80 Acres Farms, specialised in technology-assisted indoor growing and a multi-farm operator marketing a wide variety of freshly-harvested vegetables and fruits.
根据Million Insights的一份报告，全球精细农业市场预计将在未来几年迎来重大变革。Million Insights认为这可能要归功于精耕细作具有使用基于IT的下一代耕作方法提高作物产量的潜力。报告指出，由于农业中物联网的过度使用，精细农业市场也受到了刺激。农场的生产者和管理者都在利用物联网设备的功能，如自动转向系统、GNSS和GPS系统，以及用于文件制图、温度监测、土壤取样、灌溉管理和各种其他应用的传感器。研究人员表示:“上述设备确实为实时洞察提高农业生产效率的方法开辟了道路。”研究者称，越来越多地采用无人机技术来监测作物、识别种植缺陷、检测和控制病虫害是推动精细农业市场发展的另一个因素。
[学术文献] Measurement Method Based on Multispectral Three-Dimensional Imaging for the Chlorophyll Contents of Greenhouse Tomato Plants 进入全文
Nondestructive plant growth measurement is essential for researching plant growth and health. Anondestructive measurement system to retrieve plant information includes the measurement of morphological and physiological information, but most systems use two independent measurement systems for the two types of characteristics. In this study, a highly integrated, multispectral, three-dimensional (3D) nondestructive measurement system for greenhouse tomato plants was designed. The system used a Kinect sensor, an SOC710 hyperspectral imager, an electric rotary table, and other components. A heterogeneous sensing image registration technique based on the Fourier transform was proposed, which was used to register the SOC710 multispectral reflectance in the Kinect depth image coordinate system. Furthermore, a 3D multiview RGB-D image-reconstruction method based on the pose estimation and self-calibration of the Kinect sensor was developed to reconstruct a multispectral 3D point cloud model of the tomato plant. An experiment was conducted to measure plant canopy chlorophyll and the relative chlorophyll content was measured by the soil and plant analyzer development (SPAD) measurement model based on a 3D multispectral point cloud model and a single-view point cloud model and its performance was compared and analyzed. The results revealed that the measurement model established by using the characteristic variables from the multiview point cloud model was superior to the one established using the variables from the single-view point cloud model. Therefore, the multispectral 3D reconstruction approach is able to reconstruct the plant multispectral 3D point cloud model, which optimizes the traditional two-dimensional image-based SPAD measurement method and can obtain a precise and efficient high-throughput measurement of plant chlorophyll.
Flood has an important effect on plant growth by affecting their physiologic and biochemical properties. Soybean is one of the main cultivated crops in the world and the United States is one of the largest soybean producers. However, soybean plant is sensitive to flood stress that may cause slow growth, low yield, small crop production and result in significant economic loss. Therefore, it is critical to develop soybean cultivars that are tolerant to flood. One of the current bottlenecks in developing new crop cultivars is slow and inaccurate plant phenotyping that limits the genetic gain. This study aimed to develop a low-cost 3D imaging system to quantify the variation in the growth and biomass of soybean due to flood at its early growth stages. Two cultivars of soybeans, i.e. flood tolerant and flood sensitive, were planted in plant pots in a controlled greenhouse. A low-cost 3D imaging system was developed to take measurements of plant architecture including plant height, plant canopy width, petiole length, and petiole angle. It was found that the measurement error of the 3D imaging system was 5.8% in length and 5.0% in angle, which was sufficiently accurate and useful in plant phenotyping. Collected data were used to monitor the development of soybean after flood treatment. Dry biomass of soybean plant was measured at the end of the vegetative stage (two months after emergence). Results show that four groups had a significant difference in plant height, plant canopy width, petiole length, and petiole angle. Flood stress at early stages of soybean accelerated the growth of the flood-resistant plants in height and the petiole angle, however, restrained the development in plant canopy width and the petiole length of flood-sensitive plants. The dry biomass of flood-sensitive plants was near two to three times lower than that of resistant plants at the end of the vegetative stage. The results indicate that the developed low-cost 3D imaging system has the potential for accurate measurements in plant architecture and dry biomass that may be used to improve the accuracy of plant phenotyping.