揭秘Python在智能化农业中的突破性进展

import cv2
import numpy as np

def detect_pest(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # 进行病虫害检测算法
    # ...

def spray_pest(image, location):
    # 进行精确喷药算法
    # ...

def main():
    # 读取无人机传回的图像
    image = cv2.imread("image.jpg")
    
    # 检测病虫害
    pest_location = detect_pest(image)
    
    # 喷洒药物
    spray_pest(image, pest_location)

if __name__ == "__main__":
    main()

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

def predict_yield(data):
    # 提取特征和目标变量
    X = data[["temperature", "humidity", "rainfall"]]
    y = data["yield"]
    
    # 数据拆分为训练集和测试集
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
    
    # 线性回归模型训练与预测
    model = LinearRegression()
    model.fit(X_train, y_train)
    predictions = model.predict(X_test)
    
    return predictions

def main():
    # 读取农田数据
    data = pd.read_csv("data.csv")
    
    # 预测产量
    predictions = predict_yield(data)
    
    print(predictions)

if __name__ == "__main__":
    main()

import time
import RPi.GPIO as GPIO

def irrigate_soil():
    GPIO.setmode(GPIO.BCM)
    GPIO.setup(18, GPIO.OUT)
    
    while True:
        # 获取土壤湿度
        soil_humidity = get_soil_humidity()
        
        if soil_humidity < 50:
            # 开启灌溉
            GPIO.output(18, GPIO.HIGH)
            time.sleep(5)
            # 关闭灌溉
            GPIO.output(18, GPIO.LOW)
        
        time.sleep(3600)

def get_soil_humidity():
    # 获取土壤湿度数据
    # ...
    return soil_humidity

def main():
    irrigate_soil()

if __name__ == "__main__":
    main()