Mango (Mangifera Indica L. Family Anacardiaceae) is a climatic fruit with a short shelf life. A significant percentage of fruit is wasted each year due to the time-consuming manual grading and classification process. There is a need to replace the traditional methods by adopting automation technologies in the agriculture sector. This paper presents a deep learning-based approach for automated classification and grading of eight cultivars of harvested mangoes based on quality features such as color, size, shape, and texture. Five types of data augmentation methods were used: images rotation, translation, zooming, shearing, and horizontal flip. We compared three architectures of 3-layer Convolutional Neural Network (CNN): VGG16, ResNet152, and Inception v3 on augmented data. The proposed approach achieved up to 99.2% classification accuracy and 96.7% grading accuracy respectively using the Inception v3 architecture of CNN.

Fruit flies and fall-armyworm are one of the major insect pest that adversely affect fruit and crops, whereas fall-armyworm is also a highly destructive pest in maize crop but also damage other economically important field crops and vegetables. Adults of both pests can fly, making it hard to monitor them in the field. This study focuses on fine-tuning the YoloV8x model for automated monitoring and identifying insect pests, like fruit flies and fall-armyworms, in open fields and closed environments using IoT-based Smart Traps. The conventional techniques for monitoring of these insect pests involve pheromone attractants and sticky traps that require regular farm visits. We developed an IoT-based device, called Smart Trap, that attracts insect pests with pheromones and captures real-time images using cameras and IoT sensors. Its main objective is automated pest monitoring in fields or indoor grain storage houses. Images captured by smart traps are transmitted to the server, where Yolo-pest, a fine-tuned YoloV8x model with customized hyperparameters performs in real time for object detection. The performance of the smart trap was evaluated in a mango orchard (Fruit Flies) and maize field (fall Armyworm) in an arid climate, achieving a 94% average detection accuracy. The validation process on grayscale and coloured images further confirmed the model’s consistent accuracy in identifying insect pests in maze crop and mango orchards. The mobile application also enhances the practical utility as having a user-friendly interface for real time identification of insect pest. Farmers can easily acces the information and data remotely that empowering them for efficient pest maangment.

This data article describes the image dataset collection and annotation of the two most common fruitfly species Bactrocera Zonata and Bactrocera Dorsalis. The dataset is released as a collection of more than 2000 images captured through two sources: images of specially reared fruitfly species in laboratory captured by (48-megapixels) smartphone camera, and images of fruitflies captured by (8-megapixels) Raspberry Pi camera through insect traps installed in fruit orchards. Each image sample is associated with a ground truth label that mentions the fruit fly species. The dataset has been classified and annotated using the object detection method into two fruitfly species with an average 85% accuracy. The results of classification and annotation have been validated by expert entomologists by manually examining test samples in a laboratory setting. This dataset is best suited for developing smart monitoring systems to provide advisory services to farmers through mobile applications that provides real-time information about fruitfly species for effective control and management.

Jujube (Ziziphus mauritiana Lam.) is a nutritionally rich tropical fruit which is native to South Asia. In Pakistan, there are 50 varieties of jujube, and approximately 24,000 tons of jujube is harvested annually from a growing area of nearly 5000 hectares. The current study was conducted to investigate whether insect pollination affects fruit yield, and whether it influences the physical and biochemical properties of the fruit. We selected fifty inflorescences from each of the three jujube cultivars (Kheri, Desi, Ayuba) and covered them with muslin cloth to promote self-pollination. The same number was tagged to observe the impact of open pollination. Fruits from both treatments were counted on each panicle, harvested, and then subjected to lab investigation for measuring physical (fruit length, fruit width, fruit weight, and pulp weight) and biochemical (total soluble solids, titratable acidity, vitamin C, and pH) parameters. A diverse range of floral visitors, including bees, flies, wasps, moths, and butterflies, were observed on jujube, with bees being the most abundant functional group. Open pollination increased fruit set (fruits per inflorescence) by 2.3–2.5 times compared to self-pollination. Similar trends were observed in physical parameters: fruit length (1.15 to 1.30 times increase), fruit width (1.15 to 1.21 times), total weight (1.60 to 1.74 times), and pulp weight (1.55 to 1.62 times). Changes in physiological parameters, including pH, vitamin C, titratable acidity, and total soluble solids, were also observed in pollination treatments of all three varieties. Open-pollinated fruit also exhibited greater length and width, but lower firmness. These findings show the vital role of insect-mediated pollination in enhancing both the yield and quality of jujube fruit, with consistent benefits observed across multiple varieties.

Globally, the mango industry is one of the largest industrial sectors – also considered the backbone industry of the country’s agriculture sector. This sector mobilizes key industrial activities of many economies, such as agriculture, production, handling, storage, logistics, certification, and packing of a variety of mangoes (e.g., with over 1,500 varieties, of which at least 30 are grown commercially in Pakistan). From local, regional, and national, to international levels, many businesses in this sector are involved in extensive mango supply networks fulfilling the demands of many consumers and markets (e.g., B2B, B2C, etc.) locally worldwide. Currently, 43% of total farmers in Pakistan are smallholder farmers, as they possess less than 2.6 hectares of land. Intermediaries dominate the entire supply chain in Pakistan, due to which small to average farmers face several challenges. This can be avoided by developing a user-friendly system to link farmers directly to the market or consumers. The proposed system identifies the gaps in the process model of Pakistan’s mango supply chain system. It creates a platform to link farmers directly to the market or consumers through a seamless online system using blockchain technology for performing reliable transactions using smart contracts. The proposed system is implemented through Ethereum, a permissioned blockchain network using Smart Contracts. It is a distributed ledger accessible to participants of the supply chain with permission to ensure the trust and authenticity of the information. By using this system, the farmers will attain increased profit through direct access to retailers, consumers, and the international market.