This study presents an investigation into the utilization of a Multi-Input architecture for the classification of fruits (apples and mangoes) into healthy and defective states, employing both RGB and silhouette images. The primary aim is to enhance the accuracy of CNN models. The methodology encompasses image acquisition, preprocessing of datasets, training, and evaluation of two CNN models: MobileNetV2 and VGG16. Results reveal that the inclusion of silhouette images alongside the Multi-Input architecture yields models with superior performance compared to using only RGB images for fruit classification, whether healthy or defective. Specifically, optimal results were achieved using the MobileNetV2 model, achieving 100\% accuracy. This finding suggests the efficacy of this combined methodology in improving the precise classification of healthy or defective fruits, which could have significant implications for applications related to external quality inspection of fruits.

Maxime Firth's business is complicated to manage, even in good times. His company, Onduline, turns recycled fibres into roofing material, after dousing them with bitumen to make them waterproof, and sells products in 100 countries. Its eight production plants span from Nizhny Novgorod in Russia and Penang in Malaysia, to Juiz de Fora in Brazil. Further complicating his supply chain, Mr Firth's business is strongly seasonal. People install roofs in the summer, so products are made from January to March, to sell from April to September.

The SMART Forecasting team at Walmart Labs is tasked with providing demand forecasts for over 70 million store-item combinations every week! For example, just how much of every type of ginger needs to go to every Walmart store in the U.S., every week for the next 52 weeks, with the goal of improving in stocks and reducing food waste. Our algorithm strategy was to build a suite of machine learning models and deploy them at scale to generate bespoke solutions for (oh so many!) store-item-week combinations. Random Forests would be part of this suite. We went through the traditional model development workflow of data discovery, identifying demand drivers, feature engineering, training, cross validation and testing.

Suppose you go shopping for mangoes one day. The vendor has laid out a cart full of mangoes. You can handpick the mangoes, the vendor will weigh them, and you pay according to a fixed Rs per Kg rate (typical story in India). Obviously, you want to pick the sweetest, most ripe mangoes for yourself (since you are paying by weight and not by quality). How do you choose the mangoes?

Abstract-- An accurate and reliable image based fruit detection system is critical for supporting higher level agriculture tasks such as yield mapping and robotic harvesting. This paper presents the use of a state-of-the-art object detection framework, Faster R-CNN, in the context of fruit detection in orchards, including mangoes, almonds and apples. Ablation studies are presented to better understand the practical deployment of the detection network, including how much training data is required to capture variability in the dataset. Data augmentation techniques are shown to yield significant performance gains, resulting in a greater than twofold reduction in the number of training images required. In contrast, transferring knowledge between orchards contributed to negligible performance gain over initialising the Deep Convolutional Neural Network directly from ImageNet features. Finally, to operate over orchard data containing between 100-1000 fruit per image, a tiling approach is introduced for the Faster R-CNN framework. The study has resulted in the best yet detection performance for these orchards relative to previous works, with an F1-score of 0.9 achieved for apples and mangoes. I. INTRODUCTION Vision based fruit detection is a critical component for infield automation in agriculture. With accurate knowledge of individual fruit locations in the field, it is possible to perform yield estimation and mapping, which is important for growers as it facilitates efficient utilisation of resources and improves returns per unit area and time. Precise localisation of the fruit is also a necessary component of an automated robotic harvesting system, which can help mitigate one of the most labour intensive tasks in an orchard [1].