A Novel Model to Detect and Classify Fresh and Damaged Fruits to Reduce Food Waste Using a Deep Learning Technique

Abstract

Due to a lack of efficient measures for dealing with food waste at many levels, including food supply chains, homes, and restaurants, the world’s food supply is shrinking at an alarming pace. In both homes and restaurants, overcooking and other factors are to be blamed for the majority of food that is wasted. Families are the primary source of food waste, and we sought to reduce this by identifying fresh and damaged food. In agriculture, the detection of rotting fruits becomes crucial. Despite the fact that people routinely classify healthy and rotten fruits, fruit growers find it ineffective. In contrast to humans, robots do not grow tired from doing the same thing again and again. Because of this, finding faults in fruits is a declared objective of the agricultural business in order to save labour, waste, manufacturing costs, and time spent on the process. An infected apple may infect a healthy one if the defects are not discovered. Food waste is more likely to occur as a consequence of this, which causes several problems. Input images are used to identify healthy and deteriorated fruits. Various fruits were employed in this study, including apples, bananas, and oranges. For classifying photographs into fresh and decaying fruits, softmax is used, while CNN obtains fruit image properties. A dataset from Kaggle was used to evaluate the suggested model’s performance, and it achieved a 97.14 percent accuracy rate. The suggested CNN model outperforms the current methods in terms of performance.

Automatic Fruits Freshness Classification Using CNN and Transfer Learning

“…Our method achieves high accuracy and outperforms several recently published works [5,19,20,[23][24][25] due to intelligent selection of the AlexNet architecture. Our study indicates that the training time of AlexNet architecture is five times faster as compared to others deeper architecture speed.…”

“…The authors concluded that the SVM the achieved highest 99% classification accuracy than the compared methods. In [19], fruit classification was achieved by using CNN and Softmax, which yielded 97.14% accurate classification.…”

“…Dataset 2: This data set contains total of 13,346 images of fresh and rotten fruits [19]. The foremost categories of this dataset are total of 6 classes, out of which are 3 classes for each fresh and rotten for apple, orange, and banana.…”

“…In this section, we present the comparison of our developed method with several other methods on same datasets. We compare our work with three recently reported fruit freshness classification methods [5,19,20,[23][24][25]. For fair comparison, we use the same training strategy as reported by above-described methods.…”

Non-destructive identification of Pseudostellaria heterophylla from different geographical origins by Vis/NIR and SWIR hyperspectral imaging techniques

“…The fully connected (Fc) layers were composed of 512, 128, 64, 32, and 4 neurons, respectively. To convert the DCNN output into probabilities for each category, a softmax function was introduced to the activation function of Fc5 ( Kumar et al., 2022 ). The categorical cross-entropy loss function was employed to measure the distance between the probability distribution of the DCNN output values and the true values.…”