Improved determination of the optimum maturity of maize based on Alexnet

The increase in the number of humans and animals, particularly livestock in Sub-Sahara Africa without a correspondent increase in land resources has led to shortages, and consequently metamorphose into unhealthy clashes between farmers and herders. The unpredictable changes in climatic conditions in recent times and human activities has also contributed to deforestation and desertification. The maize plant is being considered to mitigate for the shortage by the application of Computational Intelligence technique and image processing in the determination of the optimum maturity of the maize. There are different varieties of maize that are quite suitable for different climatic conditions in Sub-Saharan Africa. In this paper, the optimum maturity of SAMMAZ 17 variety of maize seedling is selected due to its high resilient to drought, striga condition and its good composition of nutrients. The maturity is determined by the application of Alexnet on 3000 samples of maize comb captured at different maturity stages cultivated in the same farm land. The network gave an accuracy of 72.44%. The result obtained showed a 4.44% improvement over an earlier result obtained by the use of Resnet-50. The finding is a window of opportunity for improvement in the determination of the optimum maturity of maize

Application of YOLOv8L Deep Learning in Robotic Harvesting of Persimmon (Diospyros kaki)

Deep learning has been a branch of science that has been used by many researchers and has gained popularity in recent years. Deep learning techniques perform better than traditional methods by providing high accuracy in analyzing and processing agricultural data. Therefore, the use of deep learning techniques in agriculture is increasing. The persimmon used in this study is a fruit tree belonging to the Ebenaceae family and is cultivated in various regions of Turkey, including the Trabzon region. The persimmon harvest is typically done during the fall season when the fruits reach optimal maturity. It is recommended to harvest the persimmon when they are hard but slightly soft to the touch. In this study, using the deep learning method, the classification was made by considering the color feature of the fruit. The aim here is to develop a method to be used in robotic harvesting systems. YOLOv8L was chosen as the method. The metric values of the model were analyzed and it was observed that the 'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5' and 'metrics/mAP_0.5:0.95' values of the model increased as the number of epochs increased. In the last epoch, precision was measured at about 71%, recall was measured at 79%, mAP_0.5 was measured at about 84% and mAP_0.5:0.95 was measured at about 76%. These values indicated that the model was able to detect and classify objects with high accuracy in the validation set. Measured value Size: 640x640, Batch: 16, Epoch: 102, Algorithm: YOLOv8L. It was concluded that YOLOv8L was the best detection model to be used in robotic persimmon harvesting to separate the persimmon from branch

Determinación de la madurez de mazorcas de Cacao, haciendo uso de redes neuronales convolucionales en un sistema embebido

A correct cocoa harvest involves determining a pod maturity. However, this farm activity is usually handmade, using criteria such as Size and Color of the pod; those characteristics differ according to the cocoa variety, making it difficult to standardize. For this reason, this work proposes an automated method to simplify the number of variables to develop a portable, low-cost, and custom-made tool, which makes use of a convolutional neural network to indicate whether a cocoa pod is found it at the right time to harvest. The main results of this work are: 1) the construction of three labeled data sets (1992 images each), and 2) we developed an embedded system with a 34.83% mAP (mean Average Precision) accuracy. Finally, variance analysis demonstrates that image size (i.e., 4033x4033 p, 1009x1009 p, and 505x505 p) does not affect accuracy.Una correcta cosecha Cacao implica determinar si la mazorca se encuentra en un adecuado estado de madurez. No obstante, este proceso suele darse de manera artesanal y basarse en atributos como el tamaño y color de la mazorca, características que difieren según la variedad cultivada, lo cual dificulta su estandarización. Con el fin de simplificar la cantidad de variables y presentar un método automatizado, el presente trabajo propone desarrollar una herramienta portable, de bajo costo, y hecha a medida, la cual hace uso de una red neuronal convolucional para indicar si una mazorca de cacao se encuentra en el momento oportuno para ser cosechada. Entre los principales resultados del presente trabajo se encuentran: 1) la construcción de tres conjuntos de datos etiquetados (1992 imágenes cada uno), y 2) un sistema embebido con una precisión de 34.83% mAP (mean Average Precision). Finalmente, se demuestra estadísticamente que el tamaño de las imágenes (4033x4033 p, 1009x1009 p y 505x505 p) no incide sobre la eficacia del entrenamiento