Crop Growth Monitoring and Leaf Area Index Estimation Using Wireless Sensor Network and CNN

Abstract

Agriculture was the critical improvement in the ascent of human civilization. Leaf area is viewed as a significant marker in the investigation and evaluation of the development and assessment of the production potential of a crop. Leaf area index is an important factor for calculating the amount of energy used in the photosynthesis process, evapotranspiration, crop growth monitoring, nutrition requirement, and change in crop water requirement. Leaf area index is directly related to yield prediction. It also helps to estimate the percentage of crop growth affected by pests and diseases. Leaf area index is calculated using destructive sampling method are removing several plants from the field and placing the plant in leaf area index calculating machine. This type of sampling method is an expensive and time taking process. The camera sensor captures the crop image and transmits it to the base station to estimate the leaf area index through identifying potentially constitute the number of pixels in the leaf. These green pixels are used to calculate the leaf area in the image. The important features of this cost-efficient method are its suitability for precise, user-friendly, and non-destructive crop sample methods. This method can estimate the leaf area index for a crop without any expensive tools. The convolutional neural network is used for calculating leaf area index. Wireless sensor networks place an important role in data management and future data analysis. Crop growth monitoring is a reliable process because wireless sensor networks provide frequent data collecting without human interaction.