Organic taro planting method

Abstract

The invention relates to the technical field of planting, and discloses an organic taro planting method which comprises the following steps: evaluating soil health, and measuring the pH value, nitrogen, phosphorus and potassium content and organic matter content of soil; installing an intelligent sensor system in a field, and collecting soil, climate and crop growth data; an intelligent monitoring system and an image recognition technology are used for monitoring diseases and insect pests in real time and early warning in advance; taking ecological control and biological control measures according to the monitoring result of the plant diseases and insect pests; the fertilization amount and the irrigation amount are controlled according to the intelligent monitoring data; a data analysis platform is utilized, management measures are optimized through an intelligent algorithm, and a planting strategy is adjusted. The method has the advantages of accurately identifying, preventing and controlling diseases and insect pests, reducing manual intervention, and effectively keeping soil health and crop yield at the same time.

Description

Technical Field

[0001] The invention relates to the technical field of planting, in particular to a planting method for organic taro. Background Art

[0002] Organic taro refers to taro that is grown without the use of chemical fertilizers, pesticides or genetically modified technology. Its cultivation method usually follows the principles of natural agriculture, emphasizing soil health and ecological balance. The characteristics of organic taro include: a more natural appearance, without the use of artificial yield-enhancing agents or preservatives; it usually tastes more delicious and has a delicate texture. Because there is no intervention from chemical ingredients, organic taro is richer in nutrients, contains more dietary fiber, vitamins and minerals, and is more likely to retain its original natural flavor and taste.

[0003] The cultivation method of organic taro is based on ecological agriculture, emphasizing soil health, sustainable development and no chemical pollution. Organic taro does not use chemical pesticides, so it is more susceptible to pests and diseases. Although organic farms can use some natural prevention and control methods, such as biological control and manual pest control, the effect is often not as direct and efficient as chemical pesticides. Therefore, it is necessary to design a cultivation method to improve the production efficiency of organic taro. Summary of the Invention

[0004] (1) Technical problems solved In response to the shortcomings of the existing technology, the present invention provides an organic taro planting method, which has the advantages of accurately identifying and preventing and controlling pests and diseases, reducing human intervention, and effectively maintaining soil health and crop yield, thereby solving the problems in the above-mentioned background technology.

[0005] (2) Technical solution To achieve the above-mentioned purpose of accurately identifying and controlling pests and diseases, reducing human intervention, and effectively maintaining soil health and crop yield, the present invention provides the following technical solution: a method for growing organic taro, comprising the following steps: S1: Conduct a soil health assessment and measure soil pH, nitrogen, phosphorus, potassium, and organic matter content.

[0006] Preferably, S1 further includes maintaining the soil pH value between 5.5-6.5, adjusting the ratio of nitrogen, phosphorus and potassium according to the soil test results, the ratio of nitrogen, phosphorus and potassium is 3:1:2, the soil organic matter content is not less than 3%, 4-6 tons of compost is applied per hectare, and the amount of lime applied is adjusted according to the soil pH value. If the pH value is too low, 500-800 kg of lime is applied per hectare.

[0007] S2: Install smart sensor systems in the fields to collect soil, climate and crop growth data.

[0008] Preferably, S2 further includes monitoring soil moisture, which is required to be within ±3%, real-time monitoring of air temperature, precipitation, wind speed, and light, and collecting soil moisture, temperature, humidity, and light once every hour.

[0009] S3: Utilize intelligent monitoring systems and image recognition technology to monitor pests and diseases in real time and provide early warnings.

[0010] Preferably, S3 further includes regular scanning of taro leaves and roots, and weekly inspections for pests and diseases. When the visible pest density exceeds 30 per square meter or the area of leaf lesions exceeds 10%, the system automatically issues an early warning and predicts the probability of pest and disease occurrence based on climate data and historical data. If the risk is greater than 60%, the system automatically notifies farm managers.

[0011] S4: Take ecological and biological control measures based on the monitoring results of pests and diseases.

[0012] Preferably, the method 4 further includes introducing predatory insects per hectare, the number of which is adjusted according to the type of pest, introducing 3,000 ladybugs or 1,000 parasitic wasps per hectare, spraying natural plant extracts for common pests and diseases, spraying 10-15L per hectare, and covering each hectare of planting area with an insect-proof net with a grid width of 1.5-2 meters and a net height of 2 meters.

[0013] S5: Control the amount of fertilizer and irrigation based on intelligent monitoring data.

[0014] Preferably, the S5 further includes controlling the fertilizer application amount at 150-200 kg / hectare according to the soil test results, the ratio of nitrogen, phosphorus and potassium is 3:1:2, and the soil moisture is maintained at 60-75% according to the soil moisture monitoring data. A drip irrigation system is used, and the hourly irrigation amount is adjusted according to real-time data. The irrigation amount is 5-10 mm each time. In the dry season, irrigation is carried out every 3-4 days; in the rainy season, irrigation is adjusted according to soil moisture.

[0015] S6: Utilize the data analysis platform to optimize management measures and adjust planting strategies through intelligent algorithms.

[0016] Preferably, the S6 further includes utilizing the real-time collected soil, climate, crop growth and pest and disease data to perform real-time analysis and dynamic adjustment of planting strategies through intelligent algorithms, and adjusting fertilization, irrigation and pest and disease control measures according to the crop growth stage, climate change and soil nutrient requirements.

[0017] (3) Beneficial effects Compared with the prior art, the present invention provides a method for growing organic taro, which has the following beneficial effects: This invention integrates soil, climate, crop growth, and pest and disease data, and uses intelligent sensors and image recognition technology to monitor environmental changes in real time, achieving precise fertilization, irrigation, and pest and disease control, thereby optimizing agricultural management. It reduces pesticide use through ecological and biological control measures, protects the ecological environment, and improves soil health and crop yields. The application of data analysis platforms and intelligent algorithms makes management decisions more scientific and accurate, thereby improving agricultural production efficiency, reducing resource waste, and promoting sustainable agricultural development. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0020] The present invention provides a technical solution: a method for growing organic taro, comprising the following steps: S1: Conduct a soil health assessment and measure soil pH, nitrogen, phosphorus, potassium, and organic matter content.

[0021] The soil pH value is monitored in real time through an intelligent soil pH monitoring system, and the amount of lime applied is dynamically adjusted in combination with seasonal changes in the soil. pH sensors and a soil data acquisition system are installed, and real-time soil pH value data is uploaded to the central control platform through the sensor for analysis and adjustment. This enables real-time monitoring and precise regulation, keeping the soil pH value within the optimal range (5.5-6.5), maximizing the promotion of taro root nutrient absorption, enhancing taro growth potential, and increasing yield.

[0022] Dynamically adjust the application amount and method of nitrogen, phosphorus and potassium fertilizers according to soil test results and crop growth stage, use precision fertilization technology, such as variable-speed fertilization technology and sensor-controlled fertilization, and automatically adjust the fertilizer application amount through soil sensors and plant demand sensing technology to ensure that the nitrogen, phosphorus and potassium application amount at each stage is accurate and balanced, improve the nutrient absorption efficiency of crops, avoid fertilizer waste or excessive application, thereby improving crop growth quality and yield, and protecting the soil environment.

[0023] Combine the use of organic fertilizers, adopt the recycling of agricultural waste and the supplementation of green fertilizers to increase the content of soil organic matter, compost farmland waste such as straw, root residues and organic matter such as animal manure, use composters and organic matter processing equipment to efficiently complete the composting process, use biomass fertilizers treated with beneficial microorganisms to improve the decomposition and release efficiency of organic fertilizers, and further increase the content of soil organic matter by applying compost (4-6 tons / hectare) and organic fertilizers, enhance the soil's water retention capacity, aeration and fertility, promote the root development of taro, improve soil microbial diversity, and improve the crop's stress resistance and long-term production capacity.

[0024] S2: Install smart sensor systems in the fields to collect soil, climate and crop growth data.

[0025] On the basis of soil moisture sensor, combined with soil temperature sensor for monitoring, in order to understand the relationship between soil moisture and temperature changes, to help better understand the moisture status of the soil. In addition to temperature, precipitation, wind speed and light, it can also integrate the monitoring of meteorological parameters such as air humidity and air pressure to fully grasp the changes in the environment, especially the factors affecting soil moisture and crop growth. Humidity sensors are deployed at different soil depths, such as 0-10cm, 10-20cm, 20-30cm, etc. to understand the humidity changes in different soil layers and help adjust irrigation strategies; using Internet of Things technology, multiple sensors are connected to the smart agricultural system through wireless communication to achieve real-time data upload and remote monitoring, and adopt high-precision soil moisture Sensors and meteorological sensors ensure the accuracy and real-time nature of monitoring data, especially maintaining the accuracy of soil moisture within ±3%. Data from different sources are comprehensively processed through data fusion algorithms to further improve data accuracy and stability. Data is collected once an hour and updated in real time to ensure real-time monitoring of environmental factors and soil moisture during crop growth. Multi-sensor fusion can improve the accuracy of soil moisture and climate data, ensuring soil moisture monitoring within ±3%. Through the Internet of Things and high-precision sensors, real-time data collection and monitoring can be achieved, reducing delays and errors. Through comprehensive monitoring of soil moisture and climate factors, irrigation strategies can be adjusted more accurately, reducing water waste and improving irrigation efficiency.

[0026] The meteorological data analysis platform combines historical data, meteorological models, and machine learning algorithms to predict short-term weather changes and provide early warnings of weather changes that may affect soil moisture. By integrating crop growth stages with environmental data, data modeling analyzes the correlation between soil moisture and meteorological factors such as temperature, sunlight, and precipitation, providing a basis for refined management. Machine learning algorithms, based on historical meteorological data and crop growth models, provide weather warnings and humidity forecasts, enabling early agricultural management preparations. Data storage and real-time analysis via a cloud platform provide farmers with weather and soil moisture trend forecasts and generate precise irrigation and crop management recommendations. A dynamic climate and crop growth model is established, using meteorological data to analyze crop adaptability to different climatic conditions and provide appropriate crop management plans. The meteorological data forecasting system can identify the potential impact of severe weather on soil moisture in advance and make timely adjustments. Based on weather forecasts and soil moisture analysis, farmers can adjust agricultural management measures such as irrigation, fertilization, and sowing to ensure an optimal growing environment for their crops. Through intelligent early warnings and precise adjustments, crop losses caused by climate fluctuations can be reduced, improving the stability and resilience of agricultural production.

[0027] S3: Utilize intelligent monitoring systems and image recognition technology to monitor pests and diseases in real time and provide early warnings.

[0028] Taro leaves and roots are scanned weekly using cameras, capturing high-definition images and performing real-time analysis to monitor for signs of pests and diseases, lesions, and wilting. Image recognition technology is used to automatically identify pest and disease symptoms, such as lesions, pest marks, and leaf discoloration, reducing manual intervention. Ground scanning technology is used to monitor the roots for infestation by underground pests, ensuring comprehensive monitoring. Drones and ground robots equipped with cameras, infrared sensors, and ultrasonic sensors conduct regular patrols and real-time scanning to capture image data of leaves and roots. Deep learning-based convolutional neural networks are used to automatically analyze this data to identify pest density, lesion area, and health status. Cameras combined with multispectral imaging technology provide precise identification of lesions and pests, particularly for early-stage disease diagnosis. Deep learning image recognition technology can significantly improve the efficiency and accuracy of pest and disease detection, enabling rapid diagnosis of lesions and pest density. Drones and ground scanning technologies enable comprehensive monitoring of leaves and roots, enabling timely identification of potential pest and disease risks. Automated scanning and recognition technologies reduce the workload and errors of manual inspections, improving work efficiency.

[0029] Combined with real-time climate data, soil moisture, historical meteorological data and historical records of pests and diseases, a prediction model is established to predict the probability of pests and diseases. When the detected pest density exceeds 30 / ㎡ or the leaf spot area exceeds 10%, the system will automatically issue an early warning and combine climate data and historical data to predict the future risk of pests and diseases. If the predicted risk exceeds 60%, the system will automatically notify farm managers and initiate preventive measures. Real-time climate data is collected using weather stations and soil sensors, and combined with historical pest and disease data, a pest and disease prediction model is established through data analysis tools. Through multivariate data The analytical model makes real-time predictions on the probability of occurrence of pests and diseases, and pushes real-time monitoring data, prediction results, and early warning information to the farm managers' devices through the smart agricultural management platform and mobile APP. Combined with climate data and historical pest and disease occurrence patterns, it can identify the risk of pest and disease occurrence in advance and formulate prevention and control measures based on the prediction results. Once it is identified that the pest density exceeds the threshold or the leaf lesion area reaches the warning line, the system will immediately issue an early warning to ensure that farm managers can respond in time. Through accurate predictions and timely warnings, preventive measures can be taken in advance to reduce the losses and prevention costs caused by pest and disease outbreaks.

[0030] S4: Take ecological and biological control measures based on the monitoring results of pests and diseases.

[0031] Through intelligent sensors or video surveillance, the number and effectiveness of predatory insects are monitored in real time, and their control effects are evaluated to ensure their activity and effectiveness during the control process; according to the type and number of pests, drones, automatic delivery robots, etc. are used to accurately deliver predatory insects to designated areas; drones and robots can automatically adjust the amount and location of insects based on the real-time monitored pest density, install predatory insect monitoring sensors to monitor the activity of insects, and analyze the type, number and control effect of insects through AI image recognition algorithms; through the introduction of a combination of multiple predatory insects, the control capabilities of different pests are enhanced to achieve more extensive and effective pest management, use drones and automated equipment to accurately introduce predatory insects to ensure their even distribution and effective control of pests, monitor the activity and effectiveness of predatory insects in real time, optimize control strategies, and improve control efficiency.

[0032] By combining Internet of Things technology, an intelligent spray system can be established, which can automatically adjust the spray dosage and frequency according to sensor data; sensors can monitor soil moisture, climate change, pest density, etc., and provide real-time feedback to the spray system to automatically adjust the application amount. Combined with image recognition technology and machine learning, the control effect of plant extracts can be evaluated in real time; when spraying, regional spraying is carried out according to the number and distribution of target pests to avoid unnecessary waste of resources, and drones are used to spray plant extracts accurately to designated areas, especially when covering large areas of farmland, drones can complete the operation more efficiently; natural plant extracts are used as an alternative to chemical pesticides to reduce environmental pollution, protect biodiversity, ensure crop safety and environmental sustainability, and the intelligent spraying system can achieve precise control of the spraying amount and area of plant extracts to avoid waste and unnecessary pollution. Natural plant extracts can effectively prevent and control a variety of common diseases and pests, and combined with other ecological control methods, provide a multi-level control network.

[0033] Through intelligent mechanical devices, the laying, adjustment and retraction of insect-proof nets are automatically completed. The height and tension of the nets are flexibly adjusted according to crop growth and weather changes to avoid adverse effects on crop growth. Environmental changes are monitored in real time, and the coverage strategy of the insect-proof nets is adjusted through the linkage of the Internet of Things and the automatic control system to ensure the best protection effect. The grid width and height are reasonably adjusted so that the insect-proof nets can block the entry of pests to the greatest extent possible without affecting the normal growth of taro. The automatic net-stretching system and Internet of Things technology make the management of insect-proof nets more flexible and intelligent, reduce manual intervention, and improve work efficiency. The automated net coverage and adjustment reduce the workload of manual net laying and adjustment, and improve management efficiency and work accuracy.

[0034] S5: Control the amount of fertilizer and irrigation based on intelligent monitoring data.

[0035] Soil analysis equipment is used to monitor the content of multiple elements in the soil in real time, providing more accurate fertilization guidance. Precision fertilization equipment can automatically adjust the amount of fertilizer applied per hectare based on the soil analysis results, monitor and adjust the amount of fertilizer applied in real time, and integrate soil test data, meteorological data and crop needs through the smart agriculture platform to generate personalized fertilization plans. These plans are then regulated in real time through the automated fertilization system. Precision fertilization reduces fertilizer waste, lowers the environmental impact of over-fertilization, and improves fertilizer efficiency. Adjusting fertilization strategies based on real-time monitoring of soil nutrients helps maintain good soil structure and health, accurately controls the amount of fertilizer applied, and prevents nutrient loss and groundwater contamination caused by excessive fertilizer use.

[0036] Equipped with soil moisture sensors and weather stations, they collect data in real time and use the control system to precisely adjust the irrigation amount to ensure that soil moisture is between 60-75%. Combined with meteorological data and historical precipitation records, they predict precipitation in the next few days, optimize irrigation plans, and avoid over-irrigation during the rainy season. Combined with an intelligent rainwater collection system, rainwater is stored in reservoirs for effective reuse to provide water for the dry season or high-demand period. Precise irrigation scheduling can significantly reduce water waste, maintain crop water supply during the dry season, and avoid over-irrigation during the rainy season. By reasonably adjusting the irrigation amount, soil erosion and water pollution that may be caused by the irrigation process can be avoided. The optimized water management system can ensure that crops receive the best water supply, promote their healthy growth and increase yields.

[0037] S6: Utilize the data analysis platform to optimize management measures and adjust planting strategies through intelligent algorithms.

[0038] Integrate various data such as soil moisture, meteorological data, pest and disease monitoring, and crop growth information into the big data platform. Use data mining and analysis technology to analyze crop growth status and agricultural management status in real time. Use machine learning algorithms to perform data pattern recognition, analyze the impact of different environments and management measures on crop growth, optimize irrigation, fertilization, and pest and disease control management measures, combine with crop growth models, and use agricultural simulation technology to simulate the impact of different management measures on crop growth, help adjust planting strategies, and ensure that crops grow in the optimal environment. Based on the analysis results of big data and intelligent algorithms, the decision support system can automatically generate specific agricultural management recommendations and adjust fertilization, irrigation, sowing and other measures in real time to ensure the sustainability of agricultural production. Intelligent algorithms automatically adjust fertilization, irrigation, and pest and disease control measures based on real-time data analysis to ensure that crops can obtain the most suitable environmental conditions at different growth stages and reduce resource waste. With the help of intelligent algorithms, fertilization, irrigation, and pest and disease control measures will be adjusted according to real-time data. The goal is to maximize crop growth benefits while minimizing resource waste. The comprehensive benefit function is calculated:

[0039] Where, is the growth benefit of crops, I is the irrigation amount, F is the fertilizer amount, P is the pest and disease control measures, S is the soil moisture, T is the temperature, and W is the crop growth stage.

[0040] Intelligent algorithms automatically adjust irrigation, fertilization, and pest and disease control based on real-time data. The adjustment strategy for each management measure can be quantified based on different environmental and crop needs. The optimization formula for each factor is:

[0041] Where, , , is the weight coefficient, which indicates the influence of each factor on the irrigation amount; S is the current soil moisture, T is the current temperature, and W is the growth stage of the crop.

[0042] The amount of fertilizer applied is dynamically adjusted according to soil nutrient status, crop growth stage, and weather conditions. The calculation formula for the amount of fertilizer applied, F, is:

[0043] Where N, P, and K are the current nitrogen, phosphorus, and potassium contents in the soil, respectively. , , is the crop's demand for nitrogen, phosphorus, and potassium, W is the crop's growth stage, , , , It is the weight coefficient, which reflects the adjustment factor of fertilizer application amount.

[0044] Pest and disease control measures can be dynamically adjusted based on climatic conditions, crop health status, and pest and disease prediction models. The optimization calculation formula for pest and disease control is:

[0045] Where, is the probability of pests and diseases occurring predicted based on climate data and historical data, T is the temperature, W is the growth stage of the crop, , , It is the weight coefficient, reflecting the priority and influencing factors of pest and disease control.

[0046] To optimize fertilization, irrigation, and pest and disease control measures, the intelligent algorithm can construct a comprehensive objective function by combining the benefits and costs of each management measure through a weighted summation method. The optimization goal is to maximize crop growth benefits and minimize resource waste. The formula for calculating the comprehensive objective function is:

[0047] Where, , is the weight coefficient, represents the weight of crop growth benefits and resource use; is the crop growth benefit, I, F, and P are the inputs of irrigation, fertilization, and pest and disease control, respectively.

[0048] Based on crop growth models and climate condition forecasts, the system optimizes planting density, variety selection, fertilization, and irrigation plans to promote healthy crop growth, thereby increasing yield and quality. The system can respond in real time to weather changes, market demand changes, and potential pest and disease risks, and reduce potential losses through intelligent adjustments and early warning measures to ensure stable yields.

[0049] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0050] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for growing organic taro, characterized in that: The following steps are involved: S1: Conduct soil health assessments, measuring soil pH, nitrogen, phosphorus, potassium, and organic matter content; S2: Install smart sensor systems in the fields to collect soil, climate, and crop growth data; S3: Utilize intelligent monitoring systems and image recognition technology to monitor pests and diseases in real time and provide early warnings; S4: Take ecological and biological control measures based on the monitoring results of pests and diseases; S5: Control the amount of fertilizer and irrigation based on intelligent monitoring data; S6: Utilize the data analysis platform to optimize management measures and adjust planting strategies through intelligent algorithms.

2. A method for planting organic taro according to claim 1, characterized in that, Said S1 further includes maintaining the soil pH value between 5.5-6.5, adjusting the ratio of nitrogen, phosphorus and potassium according to the soil test results, the ratio of nitrogen, phosphorus and potassium is 3:1:2, the soil organic matter content is not less than 3%, applying 4-6 tons of compost per hectare, and adjusting the amount of lime applied according to the soil pH value. If the pH value is too low, 500-800 kg of lime is applied per hectare.

3. A method for planting organic taro according to claim 1, characterized in that, The S2 further includes monitoring soil moisture, which is required to be within ±3%, real-time monitoring of temperature, precipitation, wind speed, and light, and collecting soil moisture, temperature, humidity, and light once every hour.

4. A method for planting organic taro according to claim 1, characterized in that, The S3 further includes regular scanning of taro leaves and roots, and weekly inspections for pests and diseases. When the visible pest density exceeds 30 per square meter or the area of leaf lesions exceeds 10%, the system automatically issues an early warning and predicts the probability of pest and disease occurrence based on climate data and historical data. If the risk is greater than 60%, the system automatically notifies farm managers.

5. A method for planting organic taro according to claim 1, characterized in that, The S4 further includes introducing predatory insects per hectare, with the number adjusted according to the pest species, introducing 3,000 ladybugs or 1,000 parasitic wasps per hectare, spraying common pests and diseases with natural plant extracts, spraying 10-15L per hectare, and using insect-proof nets to cover each hectare of planting area, with a grid width of 1.5-2 meters and a net height of 2 meters.

6. A method for planting organic taro according to claim 1, characterized in that, The S5 further includes controlling the fertilizer application amount at 150-200 kg / hectare based on the soil test results, the ratio of nitrogen, phosphorus and potassium is 3:1:2, and the soil moisture is maintained at 60-75% based on the soil moisture monitoring data. A drip irrigation system is used, and the hourly irrigation amount is adjusted according to real-time data. The irrigation amount each time is 5-10 mm. In the dry season, irrigation is carried out every 3-4 days; in the rainy season, irrigation is adjusted according to soil moisture.

7. A method for planting organic taro according to claim 1, characterized in that, The S6 further includes using real-time collected soil, climate, crop growth and pest and disease data to conduct real-time analysis and dynamic adjustment of planting strategies through intelligent algorithms, and adjust fertilization, irrigation and pest and disease control measures according to the crop growth stage, climate change and soil nutrient requirements.

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