AN EFFICIENT CROP IMAGE CLASSIFICATION PROCESS USING ENSEMBLE CONVOLUTION FEATURES-INTEGRATED MULTI-SCALE AND DILATED ADAPTIVE DEEP NETWORK FOR HIGH YIELD PRODUCTION

Abstract

The agricultural sector is the most important asset in the country, which boosts development by reducing unemployment, food shortages, poverty, and unstable economic conditions. The recognition and classification of agricultural imagery are the fundamental requisites of contemporary farming methodologies. Thus, it helps to accurately identify the enumeration of plant growth, plant diseases and so on, leading to an increase in crop yield production. There are numerous methods for classifying crop images; however, the conventional methods struggle with issues like noise, distortion, and image quality. It is not capable of fully exploiting the rich potential of image characteristics due to the weather conditions and shooting angles. It does not have the ability to significantly extract the relevant information in the training process and enhance the negative classified outcomes. Multiple learning-based methods have been created to gain more accurate and trustworthy information from image classification. In order to overcome these challenges, a novel crop image classification model is implemented in this research work for classifying the crop images to improve yield production. The required high-quality crop images are collected from the standard datasets. These images are further fed into the feature extraction phase, the Visual Geometry Group 16 (VGG16), graph convolutional neural network (GCNN), and residual network (ResNet) models are utilized to extract the relevant primary information in the collected crop images for generating ensemble convolution features. Subsequently, the resultant features are fed into the classification process, where the multi-scale and dilated adaptive recurrent neural network (MDARNN) mechanism is utilized to effectively classify the crops. In this process, a random function improved dark forest algorithm (RFIDFA) is employed for tuning the RNN parameters, thus improving the classification process. Finally, the validation of the designed approach is performed using several performance measures and compared with the previous works to ensure the designed model’s superior performance. The designed method achieves the best outcome of 95.19% precision, 95.14% NPV, and 95.15% accuracy measures. The developed crop image classification method can continuously monitor the crop growth information to improve yield production. Also, it helps to optimally detect the disease and pest-affected crops in an earlier stage to reduce crop losses, allowing for proper treatment and preventing widespread infection. It is possible to determine the water needs of various crops to ensure efficient crop production and precise farming practices.

Plant phenotyping relevance

植物画像から生育状態や病害状態を分類する画像解析手法の開発・検証が中心であり、植物の状態を直接推定するため採用。

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