Automatic Fruit Disease Classification using Machine Learning Strategies for Agriculture Farming

Abstract

Fruit protection is critical in the agricultural industry in the context of the global economy. The general public has recently learned that various diseases are wreaking havoc on fruit supplies. Agriculture economies all over the world are failing as a result of this. Computerized automatic methods for assessing the quality of fresh and rotting apples can relieve the burden of manually researching various apple fruit varieties. This study presents a novel method for comparing apple varieties based on fruit quality. The technique employs principal component analysis (PCA) early on to collect relevant characteristics. Additionally, the statistical, textual, and geometrical features are also extracted. The model is first tested by classifying apples from the Fruits-360 dataset as fresh or spoiled. Furthermore, we classified four different types of fresh and rotting fruits during the training and testing phases of the classification procedure (apple, avocado, banana, and orange). In addition, the k-NN algorithm, the linear support vector machine (LSVM), the kernel support vector machine (KSVM), and decision trees (DT) are classifiers to categorize the fruits according to quality. After evaluating the models’ performance, they are retrained using only the first two principal components. The results of using SVM and DT models for quality evaluation have been discovered to be more encouraging and comparable to those obtained using state-of-the-art methods.

Plant phenotyping relevance

果実画像から鮮度・腐敗状態を抽出・分類する機械学習手法が研究の中心であり、植物器官の状態を直接評価しているため採用。

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